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With the rise of data-driven recruitment, it is imperative for each recruitment tool, including candidate sourcing and screening tools, to integrate with Applicant Tracking Systems (ATS) for enabling centralized data management for end users. 

However, there are hundreds of ATS applications available in the market today. To integrate with each one of these applications with different ATS APIs is next to impossible. 

That is why more and more recruitment tools are looking for a better (and faster) way to scale their ATS integrations. Unified ATS APIs are one such cost-effective solution that can cut down your integration building and maintenance time by 80%. 

Before moving on to how companies can leverage unified ATS API to streamline candidate sourcing and screening, let’s look at the workflow and how ATS API helps. 

Candidate sourcing and screening workflow

Here’s a quick snapshot of the candidate sourcing and screening workflow: 

1) Job posting/ data entry from job boards

Posting job requirements/ details about open positions to create widespread outreach about the roles you are hiring for. 

2) Candidate sourcing from different platforms/ referrals

Collecting and fetching candidate profiles/ resumes from different platforms—job sites, social media, referrals—to create a pool of potential candidates for the open positions.

3) Resume parsing 

Taking out all relevant data—skills, relevant experience, expected salary, etc. —from a candidate’s resume and updating it based on the company’s requirement in a specific format.

4) Profile screening

Eliminating profiles which are not relevant for the role by mapping profiles to the job requirements.  

5) Background checks 

Conducting a preliminary check to ensure there are no immediate red flags. 

6) Assessment, testing, interviews

Setting up and administering assessments, setting up interviews to ensure role suitability and collating evaluation for final decision making. 

7) Selection 

Sharing feedback and evaluation, communicating decisions to the candidates and continuing the process in case the position doesn’t close. 

How ATS API helps streamline candidate sourcing and screening

Here are some of the top use cases of how ATS API can help streamline candidate sourcing and screening.

Centralized data management and communication

All candidate details from all job boards and portals can be automatically collected and stored at one centralized place for communication and processing and future leverage. 

Automated profile import

ATS APIs ensure real time, automated candidate profile import, reducing manual data entry errors and risk of duplication. 

Customize screening workflows 

ATS APIs can help automate screening workflows by automating resume parsing and screening as well as ensuring that once a step like background checks is complete, assessments and then interview set up are triggered automatically. 

Automated candidate updates within the ATS in real time

ATS APIs facilitate real time data sync and event-based triggers between different applications to ensure that all candidate information available with the company is always up to date and all application updates are captured ASAP.  

Candidate engagement data, insights and patterns using ATS data

ATS APIs help analyze and draw insights from ATS engagement data — like application rate, response to job postings, interview scheduling — to finetune future screening.

Integrations with assessment, interview scheduling and onboarding applications

ATS API can further integrate with other assessment, interview scheduling and onboarding applications enabling faster movement of candidates across different  recruitment stages. 

Personalized outreach based on historical ATS data

ATS API integrations can help companies with automated, personalized and targeted outreach and candidate communication to improve candidate engagement, improve hiring efficiency and facilitate better employer branding. 

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Undoubtedly, using ATS API integration can effectively streamline the candidate sourcing and screening process by automating several parts of the way. However, there are several roadblocks to integrating ATS APIs at scale because of which companies refrain from leveraging the benefits that come along. Try our ROI calculator to see how much building integrations in-house can he.

In the next section we will discuss how to solve the common challenges for SaaS products trying to scale and accelerate their ATS integration strategy.

Addressing challenges of ATS API integration with Unified API

Let's discuss how the roadblocks can be removed with unified ATS API: just one API for all ATS integrations. Learn more about unified APIs here

Challenge 1: Loss of data during data transformation 

When data is being exchanged between different ATS applications and your system, it needs to be normalized and transformed. Since the same details from different applications can have different fields and nuances, chances are if not normalized well, you will end up losing critical data which may not be mapped to specific fields between systems. 

This will hamper centralized data storage, initiate duplication and require manual mapping not to mention screening workflow disruption. At the same time, normalizing each data field from each different API requires developers to understand the nuances of each API. This is a time and resource intensive process and can take months of developer time.

How unified ATS API solves this: One data model to prevent data loss

Unified APIs like Knit help companies normalize different ATS data by mapping different data schemas from different applications into a single, unified data model for all ATS APIs. Data normalization takes place in real time and is almost 10X faster, enabling companies to save tech bandwidth and skip the complex processes that might lead to data loss due to poor mapping.

Bonus: Knit also offers an custom data fields for data that is not included in the unified model, but you may need for your specific use case. It also allows you to to request data directly from the source app via its Passthrough Request feature. Learn more

Challenge 2: Delayed recruitment due to inability of real-time sync and bulk transfers

Second, some ATS API integration has a polling infrastructure which requires recruiters to manually request candidate data from time to time. This lack of automated data updation in real time can lead to delayed sourcing and screening of applicants, delaying the entire recruitment process. This can negatively impact the efficiency that is expected from ATS integration. 

Furthermore, Most ATS platforms receive 1000s of applications in a matter of a few minutes. The data load for transfer can be exceptionally high at times, especially when a new role is posted or there is any update.

As your number of integrated platforms increases, managing such bulk data transfers efficiently as well as eliminating delays becomes a huge challenge for engineering teams with limited bandwidth

How unified ATS API solves this: Sync data in real-time irrespective of data load/ volume

Knit as a unified ATS API ensures that you don’t lose out on even one candidate application or be delayed in receiving them. To achieve this, Knit works on a  webhooks based system with event-based triggers. As soon as an event happens, data syncs automatically via webhooks. 

Read: How webhooks work and how to register one?

Knit manages all the heavy lifting of polling data from ATS apps, dealing with different API calls, rate limits, formats etc. It automatically retrieves new applications from all connected ATS platforms, eliminating the need to make API calls or manual data syncs for candidate sourcing and screening. 

At the same time, Knit comes with retry and resiliency guarantees to ensure that no application is missed irrespective of the data load. Thus, handling data at scale. 

This ensures that recruiters get access to all candidate data in real time to fill positions faster with automated alerts as and when new applications are retrieved for screening. 

Challenge 3: Compliance and candidate privacy concerns

Since the ATS and other connected platforms have access to sensitive data, protecting candidate data from attacks, ensuring constant monitoring and right permission/ access is crucial yet challenging to put in practice.

How unified ATS API solves this: Secure candidate data effectively

Knit unified ATS API enables companies to effectively secure the sensitive candidate data they have access to in multiple ways. 

• First, all data is doubly encrypted, both at rest and in transit. At the same time, all PII and user credentials are encrypted with an additional layer of application security. 

• Second, having an events-driven webhooks architecture, Knit is the only unified ATS API which does not store any copy of the customer data in its server. Thus, reducing changes of data misuse further. 

• Third, Knit is GDPR, SOC II and ISO27001 compliant to make sure all industry security standards are met. So, there’s one less thing for you to worry about.

Challenge 4: Long deployment duration and resource intensive maintenance

Finally, ATS API integration can be a long drawn process. It can take 2 weeks to 3 months and thousands of dollars to build integration with  just a single ATS provider. 

With different end points, data models, nuances, documentation etc. ATS API integration can be a long deployment project, diverting away engineering resources from core functions.

It’s not uncommon for companies to lose valuable deals due to this delay in setting up customer requested ATS integrations. 

Furthermore, the maintenance, documentation, monitoring as well as error handling further drains engineering bandwidth and resources. This can be a major deterrent for smaller companies that need to scale their integration stack to remain competitive.  

How unified ATS API solves this: Instant scalability

A unified ATS API like Knit allows you to connect with 30+ ATS platforms in one go helping you expand your integration stack overnight. 

All you have to do is embed Knit’s UI component into your frontend once. All heavy lifting of auth, endpoints, credential management, verification, token generations, etc. is then taken care of by Knit. 

Other benefits of using a Unified ATS API

Fortunately, companies can easily address the challenges mentioned above and streamline their candidate sourcing and screening process with a unified ATS API. Here are some of the top benefits you get with a unified ATS API:

Effective monitoring and logging for all APIs

Once you have scaled your integrations, it can be difficult to monitor the health of each integration and stay on top of user data and security threats. Unified API like Knit provides a detailed Logs and Issues dashboard i.e. a one page overview of all your integrations, webhooks and API calls. With smart filtering options for Logs and Issues,  Knit helps you get a quick glimpse of the API's status, extract historical data and take necessary action as needed.

Extensive range of Read and Write APIs

Along with Read APIs, Knit also provides a range of Write APIs for ATS integrations so that you can not only fetch data from the apps, you can also update the changes — updating candidate’s stage, rejecting an application etc. — directly into the ATS application's system. See docs

Save countless developer hours and cost

For an average SaaS company, each new integration takes about 6 weeks to 3 months to build and deploy. For maintenance, it takes minimum of 10 developer hours per week. Thus, building each new integration in-house can cost a SaaS business ~USD 15,000. Imagine doing that for 30+ integrations or 200!

On the other hand, by building and maintaining integrations for you, Knit can bring down your annual cost of integrations by as much as 20X. Calculate ROI yourself

In short, an API aggregator is non negotiable if you want to scale your ATS integration stack without compromising valuable in-house engineering bandwidth.

How to improve your screening workflow with Knit unified ATS API

Get Job details from different job boards

Fetch job IDs from your users Applicant Tracking Systems (ATS) using Knit’s job data models along with other necessary job information such as departments, offices, hiring managers etc.

Get applicant details

Use the job ID to fetch all and individual applicant details associated with the job posting. This would give you information about the candidate such as contact details, experience, links, location, experience, current stage etc. These data fields will help you screen the candidates in one easy step.

Complete screening activities

Next is where you take care of screening activities on your end after getting required candidate and job details. Based on your use case, you parse CVs, conduct background checks and/or administer assessment procedures.

Push back results into the ATS

Once you have your results, you can progmmatically push data back directly within the ATS system of your users using Knit’s write APIs to ensure a centralized, seamless user experience. For example, based on screening results, you can —

• Update candidate stage using <update stage> API See docs
• Match scores for CV parsing or add a quick tag to your applicant See docs
• Reject an application See docs and much more

Thus, Knit ensures that your entire screening process is smooth and requires minimum intervention.

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Get started with Unified ATS API

If you are looking to quickly connect with 30+ ATS applications — including Greenhouse, Lever, Jobvite and more — get your Knit API keys today.

You may talk to our one of our experts to help you build a customized solution for your ATS API use case. 

The best part? You can also make a specific ATS integration request. We would be happy to prioritize your request. 

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Today, recruitment without ATS applications seems almost impossible. From candidate sourcing and screening to communication and onboarding — every part of the recruitment workflow is tied to ATS apps. 

Research shows that 78% of recruiters using an ATS report that it has improved the quality of the candidates they hire. 

Hiring qualified talent for an organization can be a resource intensive and long drawn process. The entire recruitment workflow has multiple steps and layers, which when accomplished manually can be extremely time consuming. However, companies which leverage recruitment workflow automation by using ATS APIs can save 100s of hours spent in heavy lifting. 

Recruitment workflow and automation with ATS APIs

Let’s start with understanding the various stages of recruitment workflow and how automation with ATS APIs can help. 

Job requisition and posting

The first step involves creating job requisitions based on hiring needs across different teams. This is followed by creating appropriate job descriptions and posting on job boards to attract candidates. 

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With ATS APIs, this entire process can be automated. ATS APIs come with pre-defined templates to create job requisitions and job descriptions. They also have integrations with leading job boarding to facilitate automatic posting and role promotion of job boards. 

Candidate sourcing and screening

Next, most recruitment professionals focus on collecting data on candidate profiles from different job boards. Then, they engage in screening and shortlisting the resumes following a manual process, which takes a long time. 

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ATS APIs automate the collection of candidate data, resume and other basic information. It goes a step beyond with resume parsing to automate extraction of relevant candidate data from the resume and facilitate storage in a ready to use format for easy screening. 

Interview scheduling

Once the screening is complete, interview scheduling for the shortlisted candidates is the next step. Manually, the process requires a lot of back and forth with interviewers and interviewees, managing schedules, sending invitations and reminders, etc. 

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ATS APIs led automation takes care of all scheduling struggles and automates the process of sending invitations, reminders and other candidate communication in the process. 

Candidate assessment

Scheduling interviews/ tests is followed by conducted assessments to gauge the candidate's aptitude, skills, knowledge, personality and cognitive abilities for the role. 

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ATS APIs can easily automate test assessment via online proctored solutions and even record scores and present it to the decision makers in a streamlined and easy to understand format. 

Decision making

When it comes to decision making, ATS APIs can collate evaluation, assessment results and feedback of all candidates and even rank them based on comprehensive scores to help decision makers with data-driven insights on the best candidate for the role. 

Job offer and onboarding

Once a candidate has been selected, the ATS API can automatically send the offer letter based on pre-defined templates. Acceptance of the offer letter by the candidate can automatically trigger document signing digitally, thereby automating the entire onboarding process. Bi-directional data sync will ensure that all steps of employee onboarding are conducted automatically. 

Managing information

An ATS API also enables recruitment professionals to automatically capture, manage and update all the relevant information about the candidate, application and status in a common platform, which can be accessed as and when needed. 

Candidate communication

Throughout the recruitment workflow, there are several touchpoints with the candidate. ATS API: can help recruitment professionals with personalized communication templates for candidates based on their application status, interview performance, feedback, etc. 

Post-recruitment evaluation

Finally, the ATS API can provide recruitment professionals with key data points and metrics to gauge recruitment performance. Metrics like time to hire, source, open positions, candidate diversity, interview to hire ratio, can all be collated in one report by the ATS API and presented. 

Process of automating the recruitment workflow with ATS APIs 

With understanding of the recruitment workflow, let’s understand the process of automating the same with ATS API. 

Identify the recruitment stages to automate

To begin with, you need to understand the recruitment stages in your organization and identify the ones which require a lot of heavy lifting and can be automated. For instance, while conducting the interviews cannot be automated, scheduling them and compiling the feedback and evaluation can be. Thus, identify the stages to automate and what benefits you seek to achieve as a result of automation. 

Choose the ATS APIs 

There are multiple ATS APIs in the market today. While each one of them comes with multiple functionalities across the recruitment workflow, some are likely to be better over others for particular use cases. Therefore, to leverage automation with ATS API, choose the ones that best suit your industry and requirements. You might even choose multiple ATS APIs and integrate them to your system for different purposes, while also integrating one with another. 

Obtain the necessary API credentials from the ATS provider.

Once you have selected the ATS APIs, it’s time to get into the technical aspects of getting the integration in place. To integrate the ATS API, you need to get access to specific credentials and authentication from the ATS provider. These include API key, access tokens, client ID, client secret, endpoints, etc. Once you have these, only then can the integration process begin. Also, ensure you understand the authentication process well.  

Integrate with the ATS APIs

Once you have the necessary credentials, get started with the integration. This will require coding and engineering effort as you will be building the integration from scratch. Understand the data models, endpoints, authorization by going through the API documentation for each ATS API you choose. Simultaneously get started with data mapping, authentication, error handling, etc. followed by testing to gauge the effectiveness of your integration. Each integration can take anywhere between a few weeks to a few months. 

Monitor, manage and maintain your ATS APIs

Post integration, you need to keep track of your data exchange and transformation process. Ensure that data synchronization is happening as per your expectations. Your need to keep track of unstable APIs or any updates in the same, error logging challenges, expiry or deactivation of webhooks, management of large data volume, among others. At the same time, monitor any security threats or unauthorized access push. 

Optimize processes

Finally, optimize your ATS API integration process. Identify the major challenges from the maintenance and management standpoint and focus on fixing the issues to create a better integration experience for your teams. 

Limitations of using different ATS APIs for automating recruitment workflow  

While using multiple ATS APIs to power different functionalities is enticing, it can be challenging and a major burden on your engineering and other teams. Here are a few limitations that might face while trying to integrate different ATS API for recruitment workflow automation. 

Scale and optimization challenges 

Each ATS API comes with different data fields, documentation and processes that need to be followed for integration. Integrating each one requires a steep learning curve for the engineering team. From a resource standpoint, each ATS API integration can take an average of four weeks, costing ~USD 10K. As you scale, there is an exponential time and monetary cost that comes along, which is applicable to each API you add. After a certain time, chances are that the costs and efforts associated with integration scale will significantly surpass the savings and benefits from automation. 

Data transformation challenges 

Each API, even within the same category of ATS will have different data models. For instance, the field of candidate name may be categorized as cand_name for one ATS API, while candidate_name for another one. To ensure that data from all APIs is consolidated for processing, you need to engage in data normalization and data transformation to process the data from different ATS APIs. 

Data synchronization challenges 

Next, data synchronization in real time can be a big challenge. If you are using a polling infrastructure, you will have to request data sync time and again, that too across multiple APIs. At the same time, data sync can be a challenge with scalability, when the data load becomes unmanageable. The inability to facilitate real time data synchronization can lead to delays in the entire recruitment process or exclusion of applications during a particular round. 

Monitoring and management 

Error handling, monitoring and management is extremely resource intensive. It is extremely important to maintain the health of your integrations, by constantly logging their performance. It is important to keep track of API calls, log errors, data sync requests, etc. This is required to catch any potential errors early on and manage integrations better. However, monitoring each API for every second is manually very burdensome. 

Compliance and security challenges

Compliance and security is a big challenge when it comes to integrations. Since you are dealing with a lot of personal data, you need to be on your toes when it comes to security. At the same time, each API will have a different authentication methodology as well as separate policies that you need to keep pace with.  

Custom workflows 

Finally, you might need custom workflows from your ATS APIs, especially during data exchange between them. Building these custom workflows can be an engineering nightmare, let alone maintaining and monitoring them. 

How unified API can help integrate with multiple ATS APIs for recruitment automation 

Don’t get apprehensive about using different ATS APIs for automating your recruitment workflows. A unified API like Knit can help you integrate different ATS APIs effortlessly and in less than half the time. Here are the top benefits of using a unified API.

Easy scalability

Unified API enables you to scale product integrations faster. You can easily add hundreds of ATS applications to your systems by just learning about the unified API. You no longer have to go through the API documentation of multiple applications or understand the nuances, processes, etc. It is highly time and cost effective from a scale and optimization lens.  

Seamless data transformation with custom fields

A unified API like Knit can provide you with a common data model. You can easily eliminate the data transformation nuances and complex processes for different APIs. It enables you to map different data schemas from different ATS applications into a single, unified data model as normalized data. In addition, you can also incorporate custom data fields i.e. you can access any non-standard data you need, which may not be included in the common ATS data model.

Real time data sync

Following a webhooks based event driven architecture, unified APIs like Knit ensure real time data sync. Without the need for any polling infrastructure or request, Knit facilitates assured real time data sync, irrespective of the data load. Furthermore, it also sends automatic notifications and alerts when new data has been updated. 

High security

Knit, as a unified API, helps companies leveraging ATS integration ensure high levels of security. It is the only unified API which doesn’t store a copy of the customer data. Furthermore, being 100% webhook-based architecture, it facilitates greater security. You don’t have to navigate through different security policies for different APIs and can access OAuth, API key or a username-password based authentication. Finally, all data with our unified API is doubly encrypted, when in rest and when in transit. 

Easy integration management

With a unified API like Knit, integration management also becomes seamless. It enables you to monitor and manage all ATS integrations using a detailed Logs, Issues, Integrated Accounts and Syncs page. ‍Furthermore, the fully searchable Logs keep track of API calls, data syncs and requests and status of each webhook registered. This effectively streamlines integration management and error resolution 5x faster. 

TL:DR

Recruitment professionals and leaders involved in different stages of the recruitment lifecycle can leverage ATS integrations to automate their workflows. With the right ATS API, each stage of the recruitment workflow can be automated to a certain extent to save time and effort. However, building and maintaining different ATS API can be challenging with issues of scale, data transformation, synchronization, etc. Fortunately, with a unified API, companies can address these issues for seamless scalability, data transformation with a unified data model supported by custom data fields, high security with double encryption, webhook architecture for real time data sync, irrespective of workload and easy integration management with detailed logs, issues, etc.  Get started with a unified API to integrate all your preferred ATS applications to automate and streamline your recruitment workflows.

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Marketing automation tools are like superchargers for marketers, propelling their campaigns to new heights. Yet, there's a secret ingredient that can take this power to the next level: the right audience data. 

What better than an organization’s CRM to power it? 

The good news is that many marketing automation tools are embracing CRM API integrations to drive greater adoption and results. However, with the increasing number of CRM systems underplay, building and managing CRM integrations is becoming a huge challenge. 

Fortunately, the rise of unified CRM APIs is bridging this gap, making CRM integration seamless for marketing automation tools. But, before delving into how marketing automation tools can power integrations with unified CRM APIs, let’s explore the business benefits of CRM APIs. 

10 ways marketing automation tools can maximize results with CRM API integration

Here’s a quick snapshot of how CRM APIs can bring out the best of marketing automation tools, making the most of the audience data for customers. 

1. Customer segmentation and content personalization  

Research shows that 72% of customers will only engage with personalized messaging. CRM integration with marketing automation tools can enable the users to create personalized messaging based on customer segmentation. 

Users can segment customers based on their likelihood of conversion and personalize content for each campaign. Slicing and dicing of customer data, including demographics, preferences, interactions, etc. can further help in customizing content with higher chances of consumption and engagement. Customer segmentation powered by CRM API data can help create content that customers resonate with. 

2. Enhanced lead nurturing for higher conversion 

CRM integration provides the marketing automation tool with every tiny detail of every lead to adjust and customize communication and campaigns that facilitate better nurturing. At the same time, real time conversation updates from CRM can help in timely marketing follow-ups for better chances of closure. 

2. Churn prediction and customer retention

As customer data from CRM and marketing automation tools is synched in real time, any early signs of churn like reduced engagement or changed consumer behavior can be captured. 

Real time alerts can also be automatically updated in the CRM for sales action. At the same time, marketing automation tools can leverage CRM data to predict which customers are more likely to churn and create specific campaigns to facilitate retention. 

3. Upsell and cross-sell campaigns

Users can leverage customer preferences from the CRM data to design campaigns with specific recommendations and even identify opportunities for upselling and cross-selling. 

For instance, customers with high engagement might be interested in upgrading their relationships and the marketing automation tools can use this information and CRM details on their historical trends to propose best options for upselling. 

Similarly, when details of customer transactions are captured in the CRM, they can be used to identify opportunities for complementary selling with dedicated campaigns. This leads to a clear increased revenue line. 

4. Automated campaign workflow to reduce operational overheads

In most marketing campaigns as the status of a lead changes, a new set of communication and campaign takes over. With CRM API integration, marketing automation tools can easily automate the campaign workflow in real time as soon as there is a status change in the CRM. This ensures greater engagement with the lead when their status changes. 

5. Event triggered campaigns for faster TAT

Marketing communication after events is an extremely important aspect of sales. With CRM integration in marketing automation tools, automated post-event communication or campaigns can be triggered based on lead status for attendance and participation in the event. 

This facilitates a faster turnaround time for engaging the customers just after the event, without any delays due to manual follow ups. 

6. Lead source automation

The integration can help automatically map the source of the lead from different marketing activities like webinars, social media posts, newsletters, etc. in your CRM to understand where your target audience engagement is higher. 

At the same time, it can facilitate tagging of leads to the right teams or personnels for follow ups and closures. With automated lead source tracking, users can track the ROI of different marketing activities. 

7. Tailored social media campaigns and multi-channel marketing

With CRM API integration, users can get access to customer preference insights to define their social media campaigns and audience. At the same time, they can customize scheduling based on customer’s geographical locations from CRM to facilitate maximum efficiency. 

8. Data enrichment for enhancing lead profiles

With bi-directional sync, CRM API integration with marketing automation tools can lead to enhancement of lead profiles. With more and more lead data coming in across both the platforms, users can have a rich and comprehensive profile of their customers, updates in real time across the CRM and marketing tools. 

9. Lifecycle marketing automation

Overall, integrating CRM API with marketing automation tools can help in automating the entire marketing lifecycle. It starts with getting a full customer view to stage-based automated marketing campaigns to personalized nurturing and lead scoring, predictive analytics and much more. Most of the aspects of marketing based on the sales journey of the customer can be automated and triggered in real time with CRM changes. 

10. Customer reporting and analytics for decision making

Data insights from CRM API integrated with those from marketing automation tools can greatly help in creating reports to analyze and track customer behavior. 

It can help ensure to understand consumer trends, identify the top marketing channels, improve customer segmentation and overall enhance the marketing strategy for more engagement. 

Real-world Struggles of CRM Integration in Marketing Automation

While the benefits of CRM API integration with marketing automation tools are many, there are also some roadblocks on the way. Since each CRM API is different and your customers might be using different CRM systems, building and maintaining a plethora of CRM APIs can be challenging due to:

Data transformation inconsistency and campaign blunders

When data is exchanged between two applications, it needs to undergo transformation to become normalized with data fields compatible across both. Since each CRM API has diverse data models, syntax and nuances, inconsistency during data transfer is a big challenge. 

If the data is not correctly normalized or transformed, chances are it might get corrupt or lost, leading to gaps in integration. At the same time, any inconsistency in data transformation and sync might lead to sending incorrect campaigns and triggers to customers, compromising on the experience. 

Delays in campaigns 

While inconsistency in data transformation is one challenge, a related concern comes in the form of delays or limited real-time sync capabilities. 

If the data sync between the CRM and the marketing automation tool is not happening in real time (across all CRMs being used), chances are that communication with end customers is being delayed, which can lead to loss of interest and lower engagement. 

Customer data privacy and security concerns

Any CRM is the beacon of sensitive customer data, often governed by GDPR and other compliances. However, integration and data transfer is always vulnerable to security threats like man in the middle attacks, DDoS, etc. which can lead to compromised privacy. This can lead to monetary and reputational risks. 

Scalability 

With the increasing number of CRM applications, scalability of integration becomes a huge challenge. Building new CRM integrations can be very time and resource consuming — building one integration from scratch can take up to 3 months or more — which either means compromising on the available CRM integrations or choking of engineering bandwidth. 

Moreover, as integrated CRM systems increase, the requirements for API calls and data exchange also grow exponentially, leading to delays in data sync and real time updates with increased data load. Invariably, scalability becomes a challenge.  

Integration management

Managing and maintaining integrations is a big challenge in itself. When end customers are using integrations, there are likely to be issues that require immediate action. 

At the same time, maintaining detailed logs, tracking API calls, API syncs manually can be very tedious. However, any lag in this can crumble the entire integration system. 

Vendor management

Finally, when integrating with different CRM APIs, managing the CRM vendors is a big challenge. Understanding API updates, managing different endpoints, ensuring zero downtime, error handling and coordinating with individual response teams is highly operational and time consuming. 

How Unified CRM API ensures maximum integration ROI

Don’t let the CRM API integration challenges prevent you from leveraging the multiple benefits mentioned above. A unified CRM API like the one offered by Knit, can help you access the benefits without breaking sweat over the challenges. 

If you want to know the technical details of how a unified API works, this will help

Integrate in minutes with multiple CRM APIs

A unified CRM API facilitates integration with marketing automation tools within minutes, not months, which is usually what it takes to build integrations. 

At the same time, it enables connecting with various CRM applications in one go. When it comes to Knit, marketing automation tools have to simply embed Knit’s UI component in their frontend to get access to Knit’s full catalog of CRM applications.

Consistent data transfer guaranteed with normalized data models

A unified CRM API can address all data transformation and normalization challenges easily. For instance, with Knit, different data models, nuances and schemas across CRM applications are mapped into a single and unified data model, facilitating data normalization in real time. 

At the same time, Knit allows users to map custom data fields to access non-standard data. 

Real time campaigns and data exchange

The right unified CRM API can help you sync data in real time, without any external polling requests. 

Take Knit for example, its webhooks and events driven architecture periodically polls data from all CRM applications, normalizing them and making them ready for use by the marketing automation tool. The latter doesn’t have to worry about the engineering intensive tasks of polling data, managing API calls, rate limits, data normalization, etc. 

Furthermore, this ensures that as soon as details about a customer are updated on the CRM, the associated campaigns or triggers are automatically set in motion for marketing success. 

Never miss a data update

There can be multiple CRM updates within a few minutes and as data load increases, a unified CRM API ensures guaranteed data sync in real time. As with Knit, its in-built retry mechanisms facilitate resilience and ensure that the marketing automation tools don’t miss out on any CRM updates, even at scale, as each lead is important. 

Moreover, as a user, you can set up sync frequency as per your convenience.

Scale as you go

With a unified CRM API, you only need to integrate once. As mentioned above, once you embed the UI component, every time you need to use a new CRM application or a new CRM API is added to Knit’s catalog, you can access it automatically with sync capabilities, without spending any engineering capabilities from your team. 

This ensures that you can scale in the most resource-lite and efficient manner, without diverting engineering productivity from your core product. From a data sync perspective as well, a unified CRM API ensures guaranteed scalability, irrespective of the data load. 

Security at scale

One of the biggest concerns of security and vulnerability to cyberattacks can be easily addressed with a unified CRM API across multiple facts. Let’s take the security provisions of Knit for example. 

• First, Knit ensures double encryption, i.e. it encrypts data at rest as well as when in transit for exchange. It also encrypts data with an additional layer of application security.
• Second, Knit is the only unified API that doesn’t store any copy of the data and acts as a pure passthrough proxy. Data is only processed in Knit’s server and is directly sent to the customer’s webhooks. Protection of end-user data like this helps you easily gain customer confidence during sales conversations.
• Third, Knit has wide ranging authorization capabilities, including, OAuth, API key or a username-password based authentication. Irrespective of what authorization protocol the vendor has, it can integrate with Knit.

Catch potential errors early on

Finally, integration management to ensure that all your CRM APIs are healthy is well taken care of by a unified CRM API. 

• A unified CRM API like Knit provides access to a detailed Logs, Issues, Integrated Accounts and Syncs page for all integrations to monitor and track them along with possible RCA and solutions. This empowers your CX team to solve customer issues immediately without involving the tech team.
• Furthermore, it enables you to track every API call, data sync, etc. as well as the status of webhooks registered for real time visibility in errors — ensuring that you are always on top of your data and minimizes the chances of any errors.  

Constant monitoring and on demand customer support

Finally, when you are using a unified API, you don’t have to deal with multiple vendors, endpoints, etc. Rather, the heavy lifting is done by the unified CRM API provider. 

For instance, with Knit, you can access 24/7 support to securely manage your integrations. It also provides detailed documentation, links and easy to understand product walkthroughs for your developers and end users to ensure a smooth integration process.

Get started with unified CRM API

If you are looking to integrate multiple CRM APIs with your product, get your Knit API keys and see unified API in action. (Getting started with Knit is completely free)

You can also talk to one of our experts to see how you can customize Knit to solve your specific integration challenges.

In the world of APIs, it's not enough to implement security measures and then sit back, hoping everything stays safe. The digital landscape is dynamic, and threats are ever-evolving. 

Why do you need to monitor your APIs regularly

Real-time monitoring provides an extra layer of protection by actively watching API traffic for any anomalies or suspicious patterns.

For instance - 

• It can spot a sudden surge in requests from a single IP address, which could be a sign of a distributed denial-of-service (DDoS) attack. 
• It can also detect multiple failed login attempts in quick succession, indicating a potential brute-force attack. 

In both cases, real-time monitoring can trigger alerts or automated responses, helping you take immediate action to safeguard your API and data.

API Logging

Now, on similar lines, imagine having a detailed diary of every interaction and event within your home, from visitors to when and how they entered. Logging mechanisms in API security serve a similar purpose - they provide a detailed record of API activities, serving as a digital trail of events.

Logging is not just about compliance; it's about visibility and accountability. By implementing logging, you create a historical archive of who accessed your API, what they did, and when they did it. This not only helps you trace back and investigate incidents but also aids in understanding usage patterns and identifying potential vulnerabilities.

To ensure robust API security, your logging mechanisms should capture a wide range of information, including request and response data, user identities, IP addresses, timestamps, and error messages. This data can be invaluable for forensic analysis and incident response. 

API monitoring

Combining logging with real-time monitoring amplifies your security posture. When unusual or suspicious activities are detected in real-time, the corresponding log entries provide context and a historical perspective, making it easier to determine the extent and impact of a security breach.

Based on factors like performance monitoring, security, scalability, ease of use, and budget constraints, you can choose a suitable API monitoring and logging tool for your application.

Access Logs and Issues in one page

This is exactly what Knit does. Along with allowing you access to data from 50+ APIs with a single unified API, it also completely takes care of API logging and monitoring. 

It offers a detailed Logs and Issues page that gives you a one page historical overview of all your webhooks and integrated accounts. It includes a number of API calls and provides necessary filters to choose your criterion. This helps you to always stay on top of user data and effectively manage your APIs.

Ready to build?

Get your API keys to try these API monitoring best practices for real

If you are looking to unlock 40+ HRIS and ATS integrations with a single API key, check out Knit API. If not, keep reading

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Note: This is our master guide on API Pagination where we solve common developer queries in detail with common examples and code snippets. Feel free to visit the smaller guides linked later in this article on topics such as page size, error handling, pagination stability, caching strategies and more.

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In the modern application development and data integration world, APIs (Application Programming Interfaces) serve as the backbone for connecting various systems and enabling seamless data exchange. 

However, when working with APIs that return large datasets, efficient data retrieval becomes crucial for optimal performance and a smooth user experience. This is where API pagination comes into play.

In this article, we will discuss the best practices for implementing API pagination, ensuring that developers can handle large datasets effectively and deliver data in a manageable and efficient manner. (We have linked bite sized how-to guides on all API pagination FAQs you can think of in this article. Keep reading!)

But before we jump into the best practices, let’s go over what is API pagination and the standard pagination techniques used in the present day.

What is API Pagination

API pagination refers to a technique used in API design and development to retrieve large data sets in a structured and manageable manner. When an API endpoint returns a large amount of data, pagination allows the data to be divided into smaller, more manageable chunks or pages. 

Each page contains a limited number of records or entries. The API consumer or client can then request subsequent pages to retrieve additional data until the entire dataset has been retrieved.
Pagination typically involves the use of parameters, such as offset and limit or cursor-based tokens, to control the size and position of the data subset to be retrieved. 

These parameters determine the starting point and the number of records to include on each page.

Advantages of API Pagination

By implementing API pagination, developers as well as consumers can have the following advantages - 

1. Improved Performance

Retrieving and processing smaller chunks of data reduces the response time and improves the overall efficiency of API calls. It minimizes the load on servers, network bandwidth, and client-side applications.

2. Reduced Resource Usage 

Since pagination retrieves data in smaller subsets, it reduces the amount of memory, processing power, and bandwidth required on both the server and the client side. This efficient resource utilization can lead to cost savings and improved scalability.

3. Enhanced User Experience

Paginated APIs provide a better user experience by delivering data in manageable portions. Users can navigate through the data incrementally, accessing specific pages or requesting more data as needed. This approach enables smoother interactions, faster rendering of results, and easier navigation through large datasets.

4. Efficient Data Transfer

With pagination, only the necessary data is transferred over the network, reducing the amount of data transferred and improving network efficiency.

5. Scalability and Flexibility

Pagination allows APIs to handle large datasets without overwhelming system resources. It provides a scalable solution for working with ever-growing data volumes and enables efficient data retrieval across different use cases and devices.

6. Error Handling

With pagination, error handling becomes more manageable. If an error occurs during data retrieval, only the affected page needs to be reloaded or processed, rather than reloading the entire dataset. This helps isolate and address errors more effectively, ensuring smoother error recovery and system stability.

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Common examples of paginated APIs 

Some of the most common, practical examples of API pagination are: 

• Platforms like Twitter, Facebook, and Instagram often employ paginated APIs to retrieve posts, comments, or user profiles. 
• Online marketplaces such as Amazon, eBay, and Etsy utilize paginated APIs to retrieve product listings, search results, or user reviews.
• Banking or payment service providers often provide paginated APIs for retrieving transaction history, account statements, or customer data.
• Job search platforms like Indeed or LinkedIn Jobs offer paginated APIs for retrieving job listings based on various criteria such as location, industry, or keywords.

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API pagination techniques

There are several common API pagination techniques that developers employ to implement efficient data retrieval. Here are a few useful ones you must know:

1. Offset and limit pagination
2. Cursor-based pagination
3. Page-based pagination
4. Time-based pagination
5. Keyset pagination

Read: Common API Pagination Techniques to learn more about each technique

Best practices for API pagination

When implementing API pagination in Python, there are several best practices to follow. For example,  

1. Use a common naming convention for pagination parameters

Adopt a consistent naming convention for pagination parameters, such as "offset" and "limit" or "page" and "size." This makes it easier for API consumers to understand and use your pagination system.

2. Always include pagination metadata in API responses

Provide metadata in the API responses to convey additional information about the pagination. 

This can include the total number of records, the current page, the number of pages, and links to the next and previous pages. This metadata helps API consumers navigate through the paginated data more effectively.

For example, here’s how the response of a paginated API should look like -

        
{
 "data": [
   {
     "id": 1,
     "title": "Post 1",
     "content": "Lorem ipsum dolor sit amet.",
     "category": "Technology"
   },
   {
     "id": 2,
     "title": "Post 2",
     "content": "Praesent fermentum orci in ipsum.",
     "category": "Sports"
   },
   {
     "id": 3,
     "title": "Post 3",
     "content": "Vestibulum ante ipsum primis in faucibus.",
     "category": "Fashion"
   }
 ],
 "pagination": {
   "total_records": 100,
   "current_page": 1,
   "total_pages": 10,
   "next_page": 2,
   "prev_page": null
 }
}
        
    

3. Determine an appropriate page size

Select an optimal page size that balances the amount of data returned per page. 

A smaller page size reduces the response payload and improves performance, while a larger page size reduces the number of requests required.

Determining an appropriate page size for a paginated API involves considering various factors, such as the nature of the data, performance considerations, and user experience. 

Here are some guidelines to help you determine the optimal page size.

Read: How to determine the appropriate page size for a paginated API 

4. Implement sorting and filtering options

Provide sorting and filtering parameters to allow API consumers to specify the order and subset of data they require. This enhances flexibility and enables users to retrieve targeted results efficiently. Here's an example of how you can implement sorting and filtering options in a paginated API using Python:

        
# Dummy data
products = [
    {"id": 1, "name": "Product A", "price": 10.0, "category": "Electronics"},
    {"id": 2, "name": "Product B", "price": 20.0, "category": "Clothing"},
    {"id": 3, "name": "Product C", "price": 15.0, "category": "Electronics"},
    {"id": 4, "name": "Product D", "price": 5.0, "category": "Clothing"},
    # Add more products as needed
]


@app.route('/products', methods=['GET'])
def get_products():
    # Pagination parameters
    page = int(request.args.get('page', 1))
    per_page = int(request.args.get('per_page', 10))


    # Sorting options
    sort_by = request.args.get('sort_by', 'id')
    sort_order = request.args.get('sort_order', 'asc')


    # Filtering options
    category = request.args.get('category')
    min_price = float(request.args.get('min_price', 0))
    max_price = float(request.args.get('max_price', float('inf')))


    # Apply filters
    filtered_products = filter(lambda p: p['price'] >= min_price and p['price'] <= max_price, products)
    if category:
        filtered_products = filter(lambda p: p['category'] == category, filtered_products)


    # Apply sorting
    sorted_products = sorted(filtered_products, key=lambda p: p[sort_by], reverse=sort_order.lower() == 'desc')


    # Paginate the results
    start_index = (page - 1) * per_page
    end_index = start_index + per_page
    paginated_products = sorted_products[start_index:end_index]


    return jsonify(paginated_products)

        
    

5. Preserve pagination stability

Ensure that the pagination remains stable and consistent between requests. Newly added or deleted records should not affect the order or positioning of existing records during pagination. This ensures that users can navigate through the data without encountering unexpected changes.

Read: 5 ways to preserve API pagination stability

6. Handle edge cases and error conditions

Account for edge cases such as reaching the end of the dataset, handling invalid or out-of-range page requests, and gracefully handling errors. 

Provide informative error messages and proper HTTP status codes to guide API consumers in handling pagination-related issues.

Read: 7 ways to handle common errors and invalid requests in API pagination

7. Consider caching strategies

Implement caching mechanisms to store paginated data or metadata that does not frequently change. 

Caching can help improve performance by reducing the load on the server and reducing the response time for subsequent requests.

Here are some caching strategies you can consider: 

1. Page level caching

Cache the entire paginated response for each page. This means caching the data along with the pagination metadata. This strategy is suitable when the data is relatively static and doesn't change frequently.

2. Result set caching

Cache the result set of a specific query or combination of query parameters. This is useful when the same query parameters are frequently used, and the result set remains relatively stable for a certain period. You can cache the result set and serve it directly for subsequent requests with the same parameters.

3. Time-based caching

Set an expiration time for the cache based on the expected freshness of the data. For example, cache the paginated response for a certain duration, such as 5 minutes or 1 hour. Subsequent requests within the cache duration can be served directly from the cache without hitting the server.

4. Conditional caching

Use conditional caching mechanisms like HTTP ETag or Last-Modified headers. The server can respond with a 304 Not Modified status if the client's cached version is still valid. This reduces bandwidth consumption and improves response time when the data has not changed.

5. Reverse proxy caching

Implement a reverse proxy server like Nginx or Varnish in front of your API server to handle caching. 

Reverse proxies can cache the API responses and serve them directly without forwarding the request to the backend API server. 

This offloads the caching responsibility from the application server and improves performance.

Simplify API pagination 

In conclusion, implementing effective API pagination is essential for providing efficient and user-friendly access to large datasets. But it isn’t easy, especially when you are dealing with a large number of API integrations.

Using a unified API solution like Knit ensures that your API pagination requirements is handled without you requiring to do anything anything other than embedding Knit’s UI component on your end. 

Once you have integrated with Knit for a specific software category such as HRIS, ATS or CRM, it automatically connects you with all the APIs within that category and ensures that you are ready to sync data with your desired app. 

In this process, Knit also fully takes care of API authorization, authentication, pagination, rate limiting and day-to-day maintenance of the integrations so that you can focus on what’s truly important to you i.e. building your core product.

By incorporating these best practices into the design and implementation of paginated APIs, Knit creates highly performant, scalable, and user-friendly interfaces for accessing large datasets. This further helps you to empower your end users to efficiently navigate and retrieve the data they need, ultimately enhancing the overall API experience.

Sign up for free trial today or talk to our sales team

If you are looking to unlock 40+ HRIS and ATS integrations with a single API key, check out Knit API. If not, keep reading

‍

Note: This is a part of our series on API Pagination where we solve common developer queries in detail with common examples and code snippets. Please read the full guide here where we discuss page size, error handling, pagination stability, caching strategies and more.

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Ensure that the pagination remains stable and consistent between requests. Newly added or deleted records should not affect the order or positioning of existing records during pagination. This ensures that users can navigate through the data without encountering unexpected changes.

5 ways for pagination stability

To ensure that API pagination remains stable and consistent between requests, follow these guidelines:

1. Use a stable sorting mechanism

If you're implementing sorting in your pagination, ensure that the sorting mechanism remains stable. 

This means that when multiple records have the same value for the sorting field, their relative order should not change between requests. 

For example, if you sort by the "date" field, make sure that records with the same date always appear in the same order.

2. Avoid changing data order

Avoid making any changes to the order or positioning of records during pagination, unless explicitly requested by the API consumer. 

If new records are added or existing records are modified, they should not disrupt the pagination order or cause existing records to shift unexpectedly.

3. Use unique and immutable identifiers

It's good practice to use unique and immutable identifiers for the records being paginated. T

This ensures that even if the data changes, the identifiers remain constant, allowing consistent pagination. It can be a primary key or a unique identifier associated with each record.

4. Handle record deletions gracefully

If a record is deleted between paginated requests, it should not affect the pagination order or cause missing records. 

Ensure that the deletion of a record does not leave a gap in the pagination sequence.

For example, if record X is deleted, subsequent requests should not suddenly skip to record Y without any explanation.

5. Use deterministic pagination techniques

Employ pagination techniques that offer deterministic results. Techniques like cursor-based pagination or keyset pagination, where the pagination is based on specific attributes like timestamps or unique identifiers, provide stability and consistency between requests.

Also Read: 5 caching strategies to improve API pagination performance

HRIS or Human Resources Information Systems have become commonplace for organizations to simplify the way they manage and use employee information. For most organizations, information stored and updated in the HRIS becomes the backbone for provisioning other applications and systems in use. HRIS enables companies to seamlessly onboard employees, set them up for success and even manage their payroll and other functions to create an exemplary employee experience.

However, integration of HRIS APIs with other applications under use is essential to facilitate workflow automation. Essentially, HRIS API integration can help businesses connect diverse applications with the HRIS to ensure seamless flow of information between the connected applications. HRIS API integrations can either be internal or customer-facing. In internal HRIS integrations, businesses connect their HRIS with other applications they use, like ATS, Payroll, etc. to automate the flow of information between the same. On the other hand, with customer-facing HRIS integrations, businesses can connect their application or product with the end customer’s HR applications for data exchange. 

This article seeks to serve as a comprehensive repository on HRIS API integration, covering the benefits, best practices, challenges and how to address them, use cases, data models, troubleshooting and security risks, among others. 

Benefits of HRIS API integration

Here are some of the top reasons why businesses need HRIS API integration, highlighting the benefits they bring along:

• Higher employee productivity: HRIS API integration ensures that all data exchange between HRIS and other applications is automated and doesn’t require any human intervention. This considerably reduces the time and effort spent on manually updating all platforms with HR related data. This ensures that employees are able to focus more on value add tasks, leading to increased productivity and an improved employee experience.

• Reduced errors: Manual data entry is prone to errors. For instance, if during payroll updation, the compensation of an employee is entered incorrectly and differently from HRIS data, the employee will receive incorrect compensation, leading to regulatory/ financial discrepancies and employee displeasure. 

• End customer satisfaction: This is specifically for customer-facing HRIS integrations. By facilitating integration with your end customer’s HRIS applications and your product, you can foster automated data sync between the applications, eliminating the need for the customer to manually give you access to the data needed. This considerably augments customer experience and satisfaction.

• Expanded customer base: The ability to offer integrations with associated applications like payroll, attendance, etc. is something that most HR professionals seek. Therefore, when an application offers integrations with a wide range of HRIS, the total addressable market or TAM, significantly increases, augmenting the overall reach and potential customers. 

HRIS API Data Models Explained

The different HRIS tools you use are bound to come with different data models or fields which will capture data for exchange between applications. It is important for HR professionals and those building and managing these integrations to understand these data models, especially to ensure normalization and transformation of data when it moves from one application to another. 

Employees/ Employee Profiles

This includes details of all employees whether full time or contractual, including first and last name, contact details, date of birth, email ID, etc. At the same time, it covers other details on demographics and employment history including status, start date, marital status, gender, etc. In case of a former employee, this field also captures termination date. 

Employee Contact Details

This includes personal details of the employee, including personal phone number, address, etc. which can be used to contact employees beyond work contact information. 

Employee Profile Picture

Employee profile picture object or data model captures the profile picture of the employees that can be used across employee records and purposes. 

Employment Type

The next data model in discussion focuses on the type or the nature of employment. An organization can hire full time employees, contractual workers, gig workers, volunteers, etc. This distinction in employment type helps differentiate between payroll specifications, taxation rules, benefits, etc. 

Location

Location object or data model refers to the geographical area for the employee. Here, both the work location as well as the residential or native/ home location of the employee is captured. This field captures address, country, zip code, etc. 

Leave Request

Leave request data model focuses on capturing all the time off or leave of absence entries made by the employee. It includes detailing the nature of leave, time period, status, reason, etc.

Leave Balance

Each employee, based on their nature of employment, is entitled to certain time off in a year. The leave balance object helps organizations keep a track of the remaining balance of leave of absence left with the employee. With this, organizations can ensure accurate payroll, benefits and compensation. 

Attendance 

This data model captures the attendance of employees, including fields like time in, time out, number of working hours, shift timing, status, break time, etc. 

Organizational Structure

Each organization has a hierarchical structure or layers which depict an employee’s position in the whole scheme of things. The organizational structure object helps understand an employee’s designation, department, manager (s), direct reportees, etc. 

Bank Details

This data model focuses on capturing the bank details of the employee, along with other financial details like a linked account for transfer of salary and other benefits that the employee is entitled to. In addition, it captures routing information like Swift Code, IFSC Code, Branch Code, etc. 

Dependents

Dependents object focuses on the family members of an employee or individuals who the employee has confirmed as dependents for purposes of insurance, family details, etc. This also includes details of employees’ dependents including their date of birth, relation to the employee, among others. 

KYC

This includes the background verification and other details about an employee with some identification proof and KYC (know your customer) documents. This is essential for companies to ensure their employees are well meaning citizens of the country meeting all compliances to work in that location. It captures details like Aadhar Number, PAN Number or unique identification number for the KYC document. 

Compensation

This data model captures all details related to compensation for an employee, including total compensation/ cost to company, compensation split, salary in hand, etc. It also includes details on fixed compensation, variable pay as well as stock options. Compensation object also captures the frequency of salary payment, pay period, etc. 

HRIS API Integration Best Practices for Developers

To help you leverage the benefits of HRIS API integrations, here are a few best practices that developers and teams that are managing integrations can adopt:

Prioritize which HRIS integrations are needed for efficient resource allocation

This is extremely important if you are building integrations in-house or wish to connect with HRIS APIs in a 1:1 model. Building each HRIS integration or connecting with each HR application in-house can take four weeks on an average, with an associated cost of ~$10K. Therefore, it is essential to prioritize which HRIS integrations are pivotal for the short term versus which ones can be pushed to a later period. If developers focus all their energy in building all HRIS integrations at once, it may lead to delays in other product features. 

Understand the HRIS API before integrating with it

Developers should spend sufficient time in researching and understanding each individual HRIS API they are integrating with, especially in a 1:1 case. For instance, REST vs SOAP APIs have different protocols and thus, must be navigated in different ways. Similarly, the API data model, URL and the way the HRIS API receives and sends data will be distinct across each application. Developers must understand the different URLs and API endpoints for staging and live environments, identify how the HRIS API reports errors and how to respond to them, the supported data formats (JSON/ XML), etc.  

Stay up to date with API versioning

As HRIS vendors add new features, functionalities and update the applications, the APIs keep changing. Thus, as a best practice, developers must support API versioning to ensure that any changes can be updated without impacting the integration workflow and compatibility. To ensure conducive API versioning, developers must regularly update to the latest version of the API to prevent any disruption when the old version is removed. Furthermore, developers should eliminate the reliance on or usage of deprecated features, endpoints or parameters and facilitate the use of fallbacks or system alter notifications for unprecedented changes. 

Set appropriate rate limits and review them regularly

When building and managing integrations in-house, developers must be conscious and cautious about rate limiting. Overstepping the rate limit can prevent API access, leading to integration workflow disruption. To facilitate this, developers should collaboratively work with the API provider to set realistic rate limits based on the actual usage. At the same time, it is important to constantly review rate limits against the usage and preemptively upgrade the same in case of anticipated exhaustion. Also, developers should consider scenarios and brainstorm with those who use the integration processes the maximum to identify ways to optimize API usage.

Document HR integration process for each HRIS

Documenting the integration process for each HRIS is extremely important. It ensures there is a clear record of everything about that integration in case a developer leaves the organization, fostering integration continuity and seamless error handling. Furthermore, it enhances the long-term maintainability of the HRIS API integration. A comprehensive document generally captures the needs and objectives of the integration, authentication methods, rate limits, API types and protocols, testing environments, safety net in case the API is discontinued, common troubleshooting errors and handling procedures, etc. At the same time this documentation should be stored in a centralized repository which is easily accessible. 

Test HRIS integrations across different scenarios

HRIS integration is only complete once it is tested across different settings and they continue to deliver consistent performance. Testing is also an ongoing process, because everytime there is an update in the API of the third-party application, testing is needed, and so is the case whenever there is an update in one’s own application. To facilitate robust testing, automation is the key. Additionally, developers can set up test pipelines and focus on monitoring and logging of issues. It is also important to check for backward compatibility, evaluate error handling implementation and boundary values and keep the tests updated. 

Guides to popular HRIS APIs

Each HRIS API in the market will have distinct documentation highlighting its endpoints, authentication methods, etc. To make HRIS API integration for developers simpler, we have created a repository of different HR application directories, detailing how to navigate integrations with them:

• Workday API Integration Guide
• ADP API Integration Guide
• greytHR API Guide
• Zoho People API Guide
• Ceridian Dayforce API Directory
• Oracle HCM API Directory
• Gusto API Directory
• Zenefits API Directory
• Freshteam API Directory
• Keka API Directory
• Greenhouse API Directory
• Darwinbox API Directory
• SuccessFactors API Directory
• Namely API Directory

Common HRIS API Integration Challenges 

While there are several benefits of HRIS API integration, the process is fraught with obstacles and challenges, including:

Diversity of HRIS API providers

Today, there are 1000s of HR applications in the market which organizations use. This leads to a huge diversity of HRIS API providers. Within the HRIS category, the API endpoints, type of API (REST vs SOAP), data models, syntax, authentication measures and standards, etc. can vary significantly. This poses a significant challenge for developers who have to individually study and understand each HRIS API before integration. At the same time, the diversity also contributes to making the integration process time consuming and resource intensive.

Lack of public APIs and robust documentation

The next challenge comes from the fact that not all HRIS APIs are publicly available. This means that these gated APIs require organizations to get into partnership agreements with them in order to access API key, documentation and other resources. Furthermore, the process of partnering is not always straightforward either. It ranges from background and security checks to lengthy negotiations, and at times come at a premium cost associated. At the same time, even when APIs are public, their documentation is often poor, incomplete and difficult to understand, adding another layer of complexity to building and maintaining HRIS API integrations. 

Difficulty in testing across environments

As mentioned in one of the sections above, testing is an integral part of HRIS API integration. However, it poses a significant challenge for many developers. On the one hand, not every API provider offers testing environments to build against, pushing developers to use real customer data. On the other hand, even if the testing environment is available, running integrations against the same, requires thorough understanding and a steep learning curve for SaaS product developers. Overall, testing becomes a major roadblock, slowing down the process of building and maintaining integrations. 

Maintaining data quality and standardization

When it comes to HRIS API integration, there are several data related challenges that developers face across the way. To begin with, different HR providers are likely to share the same information in different formats, fields and names. Furthermore, data may also not come in a simple format, forcing developers to collect and calculate the data to decipher some values out of it. Data quality adds another layer of challenges. SInce standardizing and transforming data into a unified format is difficult, ensuring its accuracy, timeliness, and consistency is a big obstacle for developers.  

Scaling HRIS integrations

Scaling HRIS API integrations can be a daunting task, especially when integrations have to be built 1:1, in-house. Since building each integration requires developers to understand the API documentation, decipher data complexities, create custom codes and manage authentication, the process is difficult to scale. While building a couple of integrations for internal use might be feasible, scaling customer-facing integrations leads to a high level of inefficient resource use and developer fatigue. 

Post integration maintenance

Keeping up with third-party APIs and integration maintenance is another challenge that developers face. To begin with as the API versions update and change, HRIS API integration must reflect those changes to ensure usability and compatibility. However API documentation seldom reflects these changes, making it a cumbersome task for developers to keep pace with the changes. And, the inability to update API versioning can lead to broken integrations, endpoints and consistency issues. Furthermore, monitoring and logging, necessary to monitor the health of integrations can be a big challenge, with an additional resource allocation towards checking logs and addressing errors promptly. Managing rate limiting and throttling are some of the other post integration maintenance challenges that developers tend to face. 

Building Your First HRIS Integration with Knit: Step-by-Step Guide

Building Your First E-Signature Integration with Knit

Knit provides a unified HRIS API that streamlines the integration of HRIS solutions. Instead of connecting directly with multiple HRIS APIs, Knit allows you to connect with top providers like Workday, Successfactors, BambooHr, and many others through a single integration.

Learn more about the benefits of using a unified API.

Getting started with Knit is simple. In just 5 steps, you can embed multiple HRIS integrations into your APP.

Steps Overview:

1. Create a Knit Account: Sign up for Knit to get started with their unified API. You will be taken through a getting started flow.
2. Select Category: Select HRIS from the list of available option on the Knit dashboard
3. Register Webhook: Since one of the use cases of HRIS integrations is to sync data at frequent intervals, Knit supports scheduled data syncs for this category. Knit operates on a push based sync model, i.e. it reads data from the source system and pushes it to you over a webhook, so you don’t have to maintain a polling infrastructure at your end. In this step, Knit expects you to tell us the webhook over which it needs to push the source data.
4. Set up Knit UI to start integrating with APPs: In this step you get your API key and integrate with the HRIS APP of your choice from the frontend.
5. Fetch data and make API calls: That’s it! It’s time to start syncing data and making API calls and take advantage of Knit unified APIs and its data models. 

For detailed integration steps with the unified HRIS APIt, visit:

• Getting started with Knit

Security Considerations for HRIS API Integrations

Security happens to be one of the main tenets of HRIS API integration, determining its success and effectiveness. As HRIS API integration facilitates transmission, exchange and storage of sensitive employee data and related information, security is of utmost importance. 

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HRIS API endpoints are highly vulnerable to unauthorized access attempts. The lack of robust security protocols, these vulnerabilities can be exploited and attackers can gain access to sensitive HR information. On the one hand, this can lead to data breaches and public exposure of confidential employee data. On the other hand, it can disrupt the existing systems and create havoc. Here are the top security considerations and best practices to keep in mind for HRIS API integration. 

Broken authentication tokens and unauthorized access

Authentication is the first step to ensure HRIS API security. It seeks to verify or validate the identity of a user who is trying to gain access to an API, and ensures that the one requesting the access is who they claim to be. The top authentication protocols include:

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• OAuth: It is commonly used to grant third-party applications limited access to user data from other services without exposing user credentials with the third party. It uses access tokens, which are temporary and short lived. 
• Bearer tokens: They are stateless, time-bound access tokens which are simple for one time use, but need to be protected as anyone with access to them can access the API. 
• API keys: Facilitating server-to-server communication, these long-lived secret keys are ideal for trusted parties or internal use. 
• JSON Web Tokens: A token-based authentication method with a self-contained nature, facilitates scalable and secure access. 
• Basic Auth: Involves sending a username and password in the API request header in the form of Base64-encoded credentials.

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Most authentication methods rely on API tokens. However, when they are not securely generated, stored, or transmitted, they become vulnerable to attacks. Broken authentication can grant access to attackers, which can cause session hijacking, giving the attackers complete control over the API session. Hence, securing API tokens and authentication protocols is imperative. Practices like limiting the lifespan of your tokens/API keys, via time-based or event-based expiration as well as securing credentials in secret vault services can. 

Data exposure during transmission

As mentioned, HRIS API integration involves transmission and exchange of sensitive and confidential employee information. However, if the data is not encrypted during transmission it is vulnerable to attacker interception. This can happen when APIs use insecure protocols (HTTP instead of HTTPS), data is transmitted as plain text without encryption, there is insufficient data masking and validation. 

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To facilitate secure data transmission, it is important to use HTTPS, which uses Transport Layer Security (TLS) or its predecessor, Secure Sockets Layer (SSL), to encrypt data and can only be decrypted when it reaches the intended recipient. 

Input validation failure

Input validation failures can increase the incidence of injection attacks in HRIS API integrations. These attacks, primarily SQL injection and cross-site scripting (XSS), manipulate input data or untrusted data is injected into the database queries. This enables attackers to execute unauthorized database operations, potentially accessing or modifying sensitive information.

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Practices like input validation, output encoding, and the principle of least privilege, can help safeguard against injection vulnerabilities. Similarly, for database queries, using parameterized statements instead of injecting user inputs directly into SQL queries, can help mitigate the threat. 

Denial of service attacks and rate limiting

HRIS APIs are extremely vulnerable to denial of service (DoS) attacks where attackers flood your systems with excessive requests which it is not able to process, leading to disruption and temporarily restricts its functionality. Human errors, misconfigurations or even compromised third party applications can lead to this particular security challenge. 

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Rate limiting and throttling are effective measures that help prevent the incidence of DoS attacks, protecting APIs against excessive or abusive use and facilitating equitable request distribution between customers. While rate limiting restricts the number of requests or API calls that can be made in a specified time period, throttling slows down the processing of requests, instead of restricting them. Together, these act as robust measures to prevent excessive use attacks by perpetrators, and even protects against brute-force attacks. 

Third party security risks and ongoing threats

Third party security concerns i.e. how secure or vulnerable the third-party applications which you are integrating with, have a direct impact on the security posture of your HRIS API integration. Furthermore, threats and vulnerabilities come in without any prompt, making them unwanted guests. 

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To address the security concerns of third-party applications, it is important to thoroughly review the credibility and security posture of the software you integrate with. Furthermore, be cautious of the level of access you grant, sticking to the minimum requirement. It is equally important to monitor security updates and patch management along with a prepared contingency plan to mitigate the risk of security breaches and downtime in case the third-party application suffers a breach. 

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Furthermore, API monitoring and logging are critical security considerations for HRIS API integration. While monitoring involves continuous tracking of API traffic, logging entails maintaining detailed historical records of all API interactions. Together they are invaluable for troubleshooting, debugging, fostering trigger alerts in case security thresholds have been breached. In addition, regular security audits and penetration testing are extremely important. While security audits ensure the review of an API's design, architecture, and implementation to identify security weaknesses, misconfigurations, and best practice violations, penetration testing simulates cyberattacks to identify vulnerabilities, weaknesses, and potential entry points that malicious actors could exploit. These practices help mitigate ongoing security threats and facilitate API trustworthiness. 

Security with Knit’s HRIS API

When dealing with a large number of HRIS API integrations, security considerations and challenges increase exponentially. In such a situation, a unified API like Knit can help address all concerns effectively. Knit’s HRIS API ensures safe and high quality data access by:

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• Complying with industry best practices and security standards with SOC2, GDPR and ISO27001 certifications. 
• Monitoring Knit's infrastructure continuously with the finest intrusion detection systems. 
• Being the only unified API in the market that does not store any of your end user’s data in its servers.
• Encrypting all data doubly, when in transit and when at rest.
• Facilitating an additional layer of application security for encrypting PII and user credentials.
• Using a detailed Logs, Issues, Integrated Accounts and Syncs page to monitor and manage all integrations and keep track of every API request, call or data sync. 

HRIS API Use Cases: Real-World Examples

Here’s a quick snapshot of how HRIS integration can be used across different scenarios.

HRIS integration for ATS tools

ATS or applicant tracking system can leverage HRIS integration to ensure that all important and relevant details about new employees, including name, contact information, demographic and educational backgrounds, etc. are automatically updated into the customer’s preferred HRIS tool without the need to manually entering data, which can lead to inaccuracies and is operationally taxing. ATS tools leverage the write HRIS API and provide data to the HR tools in use.   

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Examples: Greenhouse Software, Workable, BambooHR, Lever, Zoho

HRIS integration for payroll software

Payroll software plays an integral role in any company’s HR processes. It focuses on ensuring that everything related to payroll and compensation for employees is accurate and up to date. HRIS integration with payroll software enables the latter to get automated and real time access to employee data including time off, work schedule, shifts undertaken, payments made on behalf of the company, etc. 

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At the same time, it gets access to employee data on bank details, tax slabs, etc. Together, this enables the payroll software to deliver accurate payslips to its customers, regarding the latter’s employees. With automated integration, data sync can be prone to errors, which can lead to faulty compensation disbursal and many compliance challenges. HRIS integration, when done right, can alert the payroll software with any new addition to the employee database in real time to ensure setting up of their payroll immediately. At the same time, once payslips are made and salaries are disbursed, payroll software can leverage HRIS integration to write back this data into the HR software for records. 

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‍Examples: Gusto, RUN Powered by ADP, Paylocity, Rippling

HRIS integration for employee onboarding/ offboarding software

Employee onboarding software uses HRIS integration to ensure a smooth onboarding process, free of administrative challenges. Onboarding tools leverage the read HRIS APIs to get access to all the data for new employees to set up their accounts across different platforms, set up payroll, get access to bank details, benefits, etc.

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With HRIS integrations, employee onboarding software can provide their clients with automated onboarding support without the need to manually retrieve data for each new joiner to set up their systems and accounts. Furthermore, HRIS integration also ensures that when an employee leaves an organization, the update is automatically communicated to the onboarding software to push deprovisioning of the systems, and services. This also ensures that access to any tools, files, or any other confidential access is terminated. Manually deprovisioning access can lead to some manual errors, and even cause delays in exit formalities. 

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Examples: Deel, Savvy, Sappling

Ease of communication and announcements

With the right HRIS integration, HR teams can integrate all relevant data and send out communication and key announcements in a centralized manner. HRIS integrations ensure that the announcements reach all employees on the correct contact information without the need for HR teams to individually communicate the needful. 

HRIS integration for LMS tools

LMS tools leverage both the read and write HRIS APIs. On the one hand, they read or get access to all relevant employee data including roles, organizational structure, skills demand, competencies, etc. from the HRIS tool being used. Based on this data, they curate personalized learning and training modules for employees for effective upskilling. Once the training is administered, the LMS tools again leverage HRIS integrations to write data back into the HRIS platform with the status of the training, including whether or not the employee has completed the same, how did they perform, updating new certifications, etc. Such integration ensures that all learning modules align well with employee data and profiles, as well as all training are captured to enhance the employee’s portfolio. 

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Example: TalentLMS, 360Learning, Docebo, Google Classroom

HRIS integration for workforce management and scheduling tools 

Similar to LMS, workforce management and scheduling tools utilize both read and write HRIS APIs. The consolidated data and employee profile, detailing their competencies and training undertaken can help workforce management tools suggest the best delegation of work for companies, leading to resource optimization. On the other hand, scheduling tools can feed data automatically with HRIS integration into HR tools about the number of hours employees have worked, their time off, free bandwidth for allocation, shift schedules etc. HRIS integration can help easily sync employee work schedules and roster data to get a clear picture of each employee’s schedule and contribution. 

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Examples: QuickBooks Time, When I Work

HRIS integration for benefits administration tools

HRIS integration for benefits administration tools ensures that employees are provided with the benefits accurately, customized to their contribution and set parameters in the organization. Benefits administration tools can automatically connect with the employee data and records of their customers to understand the benefits they are eligible for based on the organizational structure, employment type, etc. They can read employee data to determine the benefits that employees are entitled to. Furthermore, based on employee data, they feed relevant information back into the HR software, which can further be leveraged by payroll software used by the customers to ensure accurate payslip creation. 

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‍Examples: TriNet Zenefits, Rippling, PeopleKeep, Ceridian Dayforce

HRIS integration for workforce planning tools

Workforce planning tools essentially help companies identify the gap in their talent pipeline to create strategic recruitment plans. They help understand the current capabilities to determine future hiring needs. HRIS integration with such tools can help automatically sync the current employee data, with a focus on organizational structure, key competencies, training offered, etc. Such insights can help workforce planning tools accurately manage talent demands for any organization. At the same time, real time sync with data from HR tools ensures that workforce planning can be updated in real time. 

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HRIS API Integration Error Handling

There are several reasons why HRIS API integrations fail, highlighting that there can be a variety of errors. Invariably, teams need to be equipped to efficiently handle any integration errors, ensuring error resolution in a timely manner, with minimal downtime. Here are a few points to facilitate effective HRIS API integration error handling. 

Understand the types of errors

Start with understanding the types of errors or response codes that come in return of an API call. Some of the common error codes include:

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• 404 Not Found: The requested resource doesn’t exist or isn’t available
• 429 Too Many Requests: API call request rate limit has been reached or exceeded
• 401 Unauthorized: Lack of authorization or privileges to access the particular resource
• 500 Internal Server Error: Issue found at the server’s end

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While these are some, there are other error codes which are common in nature and, thus, proactive resolution should be available. 

Configure the monitoring system to incorporate all error details

All errors are generally captured in the monitoring system the business uses for tracking issues. For effective HRIS API error handling, it is imperative that the monitoring system be configured in such a way that it not only captures the error code but also any other relevant details that may be displayed along with it. These can include a longer descriptive message detailing the error, a timestamp, suggestion to address the error, etc. Capturing these can help developers with troubleshooting the challenge and resolve the issues faster. 

Use exponential back-offs to increase API call intervals

This error handling technique is specifically beneficial for rate limit errors or whenever you exceed your request quota. Exponential backoffs allow users to retry specific API calls at an increasing interval to retrieve any missed information. The request may be retrieved in the subsequent window. This is helpful as it gives the system time to recover and reduces the number of failed requests due to rate limits and even saves the costs associated with these unnecessary API calls. 

Test, document and review error handling process

It is very important to test the error handling processes by running sandbox experiments and simulated environment testing. Ideally, all potential errors should be tested for, to ensure maximum efficiency. However, in case of time and resource constraints, the common errors mentioned above, including HTTP status code errors, like 404 Not Found, 401 Unauthorized, and 503 Service Unavailable, must be tested for. 

In addition to robust testing, every step of the error handling process must be documented. Documentation ensures that even in case of engineering turnover, your HRIS API integrations are not left to be poorly maintained with new teams unable to handle errors or taking longer than needed. At the same time, having comprehensive error handling documentation can make any knowledge transfer to new developers faster. Ensure that the documentation not only lists the common errors, but also details each step to address the issues with case studies and provides a contingency plan for immediate business continuity. 

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Furthermore, reviewing and refining the error handling process is imperative. As APIs undergo changes, it is normal for initial error handling processes to fail and not perform as expected. Therefore, error handling processes must be consistently reviewed and upgraded to ensure relevance and performance. 

API error handling with Knit

Knit’s HRIS API simplifies the error handling process to a great extent. As a unified API, it helps businesses automatically detect and resolve HRIS API integration issues or provide the customer-facing teams with quick resolutions. Businesses do not have to allocate resources and time to identify issues and then figure out remedial steps. For instance, Knit’s retry and delay mechanisms take care of any API errors arising due to rate limits. 

TL:DR

It is evident that HRIS API integration is no longer a good to have, but an imperative for businesses to manage all employee related operations. Be it integrating HRIS and other applications internally or offering customer facing integrations, there are several benefits that HRIS API integration brings along, ranging from reduced human error to greater productivity, customer satisfaction, etc. When it comes to offering customer-facing integrations, ATS, payroll, employee onboarding/ offboarding, LMS tools are a few among the many providers that see value with real world use cases. 

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However, HRIS API integration is fraught with challenges due to the diversity of HR providers and the different protocols, syntax, authentication models, etc. they use. Scalining integrations, testing across different environments, security considerations, data normalization, all create multidimensional challenges for businesses. Invariably, businesses are now going the unified API way to build and manage their HRIS API integration. Knit’s unified HRIS API ensures:

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• One unified API to connect with all HRIS tools you need
• Single unified data model for seamless data normalization and exchange
• Compliance to the highest security standards like SOC2, GDPR, ISO27001, HIPAA
• Option to authenticate the way you want, including, OAuth, API key or a username-password based authentication
• 100% webhooks architecture that send out notification whenever updated data is available
• Guaranteed scalability and delivery of HR data irrespective of data load
• High level of security as Knit doesn’t store a copy of your data
• Option to read and write data from any app from any HRIS category
• Option to limit data sync and API calls to only what you need
• Double encryption when in transit and when at rest with addition PII layer
• Detailed Logs, Issues, Integrated Accounts and Syncs page to easily monitor HRIS integrations
• Custom fields to manage any non-standard HRIS data

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Knit’s HRIS API ensures a high ROI for companies with a single type of authentication, pagination, rate limiting, and automated issue detection making the HRIS API integration process simple.

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Top 5 Alternatives to tryfinch

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TL:DR:

Finch is a leading unified API player, particularly popular for its connectors in the employment systems space, enabling SaaS companies to build 1: many integrations with applications specific to employment operations. This translates to the ease for customers to easily leverage Finch’s unified connector to integrate with multiple applications in HRIS and payroll categories in one go. Invariably, owing to Finch, companies find connecting with their preferred employment applications (HRIS and payroll) seamless, cost-effective, time-efficient, and overall an optimized process. While Finch has the most exhaustive coverage for employment systems, it's not without its downsides - most prominent being the fact that a majority of the connectors offered are what Finch calls “assisted” integrations. Assisted essentially means a human-in-the-loop integration where a person has admin access to your user's data and is manually downloading and uploading the data as and when needed.

Pros and cons of Finch

Why chose Finch (Pros)

● Ability to scale HRIS and payroll integrations quickly

● In-depth data standardization and write-back capabilities

● Simplified onboarding experience within a few steps

However, some of the challenges include(Cons):

● Most integrations are human-assisted instead of being true API integrations

● Integrations only available for employment systems

● Limited flexibility for frontend auth component

● Requires users to take the onus for integration management

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Pricing: Starts at $35/connection per month for read only apis; Write APIs for employees, payroll and deductions are available on their scale plan for which you’d have to get in touch with their sales team.

Now let's look at a few alternatives you can consider alongside finch for scaling your integrations

Finch alternative #1: Knit

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Knit is a leading alternative to Finch, providing unified APIs across many integration categories, allowing companies to use a single connector to integrate with multiple applications. Here’s a list of features that make Knit a credible alternative to Finch to help you ship and scale your integration journey with its 1:many integration connector:

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Pricing: Starts at $2400 Annually

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Here’s when you should choose Knit over Finch:

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● Wide horizontal and deep vertical coverage: Knit not only provides a deep vertical coverage within the application categories it supports, like Finch, however, it also supports a wider horizontal coverage of applications, higher than that of Finch. In addition to applications within the employment systems category, Knit also supports a unified API for ATS, CRM, e-Signature, Accounting, Communication and more. This means that users can leverage Knit to connect with a wider ecosystem of SaaS applications.

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● Events-driven webhook architecture for data sync: Knit has built a 100% events-driven webhook architecture, which ensures data sync in real time. This cannot be accomplished using data sync approaches that require a polling infrastructure. Knit ensures that as soon as data updates happen, they are dispatched to the organization’s data servers, without the need to pull data periodically. In addition, Knit ensures guaranteed scalability and delivery, irrespective of the data load, offering a 99.99% SLA. Thus, it ensures security, scale and resilience for event driven stream processing, with near real time data delivery.

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● Data security: Knit is the only unified API provider in the market today that doesn’t store any copy of the customer data at its end. This has been accomplished by ensuring that all data requests that come are pass through in nature, and are not stored in Knit’s servers. This extends security and privacy to the next level, since no data is stored in Knit’s servers, the data is not vulnerable to unauthorized access to any third party. This makes convincing customers about the security potential of the application easier and faster.

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● Custom data models: While Knit provides a unified and standardized model for building and managing integrations, it comes with various customization capabilities as well. First, it supports custom data models. This ensures that users are able to map custom data fields, which may not be supported by unified data models. Users can access and map all data fields and manage them directly from the dashboard without writing a single line of code. These DIY dashboards for non-standard data fields can easily be managed by frontline CX teams and don’t require engineering expertise.  

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● Sync when needed: Knit allows users to limit data sync and API calls as per the need. Users can set filters to sync only targeted data which is needed, instead of syncing all updated data, saving network and storage costs. At the same time, they can control the sync frequency to start, pause or stop sync as per the need.

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● Ongoing integration management: Knit’s integration dashboard provides comprehensive capabilities. In addition to offering RCA and resolution, Knit plays a proactive role in identifying and fixing integration issues before a customer can report it. Knit ensures complete visibility into the integration activity, including the ability to identify which records were synced, ability to rerun syncs etc.

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As an alternative to Finch, Knit ensures:

● No-Human in the loop integrations

● No need for maintaining any additional polling infrastructure

● Real time data sync, irrespective of data load, with guaranteed scalability and delivery

● Complete visibility into integration activity and proactive issue identification and resolution

● No storage of customer data on Knit’s servers

● Custom data models, sync frequency, and auth component for greater flexibility

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Finch alternative #2: Merge

Another leading contender in the Finch alternative for API integration is Merge. One of the key reasons customers choose Merge over Finch is the diversity of integration categories it supports.

Pricing: Starts at $7800/ year and goes up to $55K

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Why you should consider Merge to ship SaaS integrations:

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● Higher number of unified API categories; Merge supports 7 unified API categories, whereas Finch only offers integrations for employment systems

● Supports API-based integrations and doesn’t focus only on assisted integrations (as is the case for Finch), as the latter can compromise customer’s PII data

● Facilitates data sync at a higher frequency as compared to Finch; Merge ensures daily if not hourly syncs, whereas Finch can take as much as 2 weeks for data sync

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However, you may want to consider the following gaps before choosing Merge:

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● Requires a polling infrastructure that the user needs to manage for data syncs

● Limited flexibility in case of auth component to customize customer frontend to make it similar to the overall application experience

● Webhooks based data sync doesn’t guarantee scale and data delivery

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Finch alternative #3: Workato

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Workato is considered another alternative to Finch, albeit in the traditional and embedded iPaaS category.

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Pricing: Pricing is available on request based on workspace requirement; Demo and free trial available

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Why you should consider Workato to ship SaaS integrations:

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● Supports 1200+ pre-built connectors, across CRM, HRIS, ticketing and machine learning models, facilitating companies to scale integrations extremely fast and in a resource efficient manner

● Helps build internal integrations, API endpoints and workflow applications, in addition to customer-facing integrations; co-pilot can help build workflow automation better

● Facilitates building interactive workflow automations with Slack, Microsoft Teams, with its customizable platform bot, Workbot

However, there are some points you should consider before going with Workato:

● Lacks an intuitive or robust tool to help identify, diagnose and resolve issues with customer-facing integrations themselves i.e., error tracing and remediation is difficult

● Doesn’t offer sandboxing for building and testing integrations

● Limited ability to handle large, complex enterprise integrations

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Finch alternative #4: Paragon

Paragon is another embedded iPaaS that companies have been using to power their integrations as an alternative to Finch.

Pricing: Pricing is available on request based on workspace requirement;

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Why you should consider Paragon to ship SaaS integrations:

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● Significant reduction in production time and resources required for building integrations, leading to faster time to market

● Fully managed authentication, set under full sets of penetration and testing to secure customers’ data and credentials; managed on-premise deployment to support strictest security requirements

● Provides a fully white-labeled and native-modal UI, in-app integration catalog and headless SDK to support custom UI

However, a few points need to be paid attention to, before making a final choice for Paragon:

● Requires technical knowledge and engineering involvement to custom-code solutions or custom logic to catch and debug errors

● Requires building one integration at a time, and requires engineering to build each integration, reducing the pace of integration, hindering scalability

● Limited UI/UI customization capabilities

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Finch alternative #5: Tray.io

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Tray.io provides integration and automation capabilities, in addition to being an embedded iPaaS to support API integration.

Pricing: Supports unlimited workflows and usage-based pricing across different tiers starting from 3 workspaces; pricing is based on the plan, usage and add-ons

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Why you should consider Tary.io to ship SaaS integrations:

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● Supports multiple pre-built integrations and automation templates for different use cases

● Helps build and manage API endpoints and support internal integration use cases in addition to product integrations

● Provides Merlin AI which is an autonomous agent to build automations via chat interface, without the need to write code

However, Tray.io has a few limitations that users need to be aware of:

● Difficult to scale at speed as it requires building one integration at a time and even requires technical expertise

● Data normalization capabilities are rather limited, with additional resources needed for data mapping and transformation

● Limited backend visibility with no access to third-party sandboxes

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TL:DR

We have talked about the different providers through which companies can build and ship API integrations, including, unified API, embedded iPaaS, etc. These are all credible alternatives to Finch with diverse strengths, suitable for different use cases. Undoubtedly, the number of integrations supported within employment systems by Finch is quite large, there are other gaps which these alternatives seek to bridge:

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● Knit: Providing unified apis for different categories, supporting both read and write use cases. A great alternative which doesn’t require a polling infrastructure for data sync (as it has a 100% webhooks based architecture), and also supports in-depth integration management with the ability to rerun syncs and track when records were synced.

● Merge: Provides a greater coverage for different integration categories and supports data sync at a higher frequency than Finch, but still requires maintaining a polling infrastructure and limited auth customization.

● Workato: Supports a rich catalog of pre-built connectors and can also be used for building and maintaining internal integrations. However, it lacks intuitive error tracing and remediation.

● Paragon: Fully managed authentication and fully white labeled UI, but requires technical knowledge and engineering involvement to write custom codes.

● Tray.io: Supports multiple pre-built integrations and automation templates and even helps in building and managing API endpoints. But, requires building one integration at a time with limited data normalization capabilities.

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Thus, consider the following while choosing a Finch alternative for your SaaS integrations:

● Support for both read and write use-cases

● Security both in terms of data storage and access to data to team members

● Pricing framework, i.e., if it supports usage-based, API call-based, user based, etc.

● Features needed and the speed and scope to scale (1:many and number of integrations supported)

Depending on your requirements, you can choose an alternative which offers a greater number of API categories, higher security measurements, data sync (almost in real time) and normalization, but with customization capabilities.

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As hiring needs for organizations become more complex, assessing candidates in a holistic and comprehensive manner is more critical than ever. Fortunately, multiple assessment software have surfaced in the recent past, enabling organizations to carry out assessments in the most effective and efficient manner. Leveraging technology, gamification and other advances, such tools are able to help organizations ensure that a candidate is a perfect fit for the role, skills, company culture and all other parameters. 

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However, to make the best use of assessment software, it is important to integrate data and information from them across other platforms being used for operational efficiency and faster turnaround in recruitment and onboarding. Here, assessment API integration plays a major role. 

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When organizations integrate data from the assessment API with other applications, including ATS, HRIS, interview scheduling, etc., they are able to optimize their recruitment workflow with a high degree of automation. 

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In this article, we will discuss the different aspects of assessment API, its integration use cases, key data models and the different ways in which you can accomplish seamless integration. 

Assessment API Data Models

To ensure that you understand the different assessment APIs well, it is important to comprehend the data models or fields that are commonly used. One of the major reasons that the knowledge of data models is imperative is to facilitate data transformation and normalization during data sync. Here are the common data models for assessment APIs:

Candidate name

This data model focuses on the name of the candidate to whom a particular assessment will be administered and all records pertaining to the candidate will be stored. It can also be associated with a unique candidate ID to prevent any confusion in case of duplication of names. 

Candidate profile

The next data model captures the profile of the candidate. From an assessment software perspective, the focus is on a candidate’s professional profile, prior work experience, qualifications, certifications, competencies, etc. Such details help in determining the right assessments for each candidate based on their experience and the role for which they are being assessed.  

Candidate contact information

This data field keeps the details or contact information for all candidates, including phone number, email address, etc. The contact information ensures that the candidate’s can be easily informed about their assessment schedule, any changes in the schedule, results, status, etc. it facilitates smooth communication between the assessment software and the candidate. 

Candidate profile picture

Most assessment software capture candidate pictures to ensure authenticity during assessments or training. Candidate profile pictures in assessment software databases help the latter to prevent proxy attendance during interviews or assessments and address any potential foul play. 

Job type

The next data model captures the nature of employment or the type of job. Today, in addition to full-time employees, organizations are increasingly hiring consultants, gig workers and even contractual employees. The assessment requirements for each one of them can be varied. Thus, the assessment software has a data model to capture the job type to ensure appropriate assessments. 

Job information

Assessment API captures job information or job details as an important data model. Put simply, this model has all details about the role being assessed for, the requirements, skills, competencies, and other aspects which need to be assessed. As a data model or field, job information contains all aspects of the job that need to be matched when candidates are assessed. 

Job department and managers

Next in line is the data model which focuses on the job department and managers. This particular field captures the department for which the candidate has applied for and the hiring managers. The details of hiring managers are important because the results of the assessment tests have to be sent to them. 

Assessment stages

Most assessment software have a few stages that a candidate undergoes. It can start from a normal personality test and go on to psychometric evaluations, coding tests, to personal interviews. As a data model, assessment stages help hiring managers understand where the candidates stand in the hiring pipeline and how close or far they are from closing a particular role at hand. 

Assessment list

The next data model captures all the types of assessments that are available as a part of the assessment software. This field has a repository of different assessments that can be administered. 

Scorecard

Once the assessment is administered, an important data model is the scorecard. This captures how the candidate performed for a particular assessment. The scorecard format or type can be different and unique for each assessment type. In some, it can be an absolute and objective score, while some others might give a more subjective outcome, determining the suitability of the candidate for the role. 

Assessment result

The assessment result as a data model captures the final verdict for the candidate. More often than not, hiring managers can update the result as selected, rejected or any other based on the scorecard and other evaluations undertaken, post which the data can be integrated into the next workflow software. 

Assessment attachment

This data field or data model captures any attachments that come along with a particular assessment test. Some tests might require candidates to submit their assessments as an attachment or external document. This field contains all such attachments which can be consulted during final hiring decisions. 

Assessment status

The assessment status data model captures the status of the assessment test for a particular candidate. It captures if the test has been provided to the candidate, whether or not they have completed the same, etc. 

Top Assessment Applications

Now that there is a clear understanding of the different assessment software data models, let’s quickly look at some of the top assessment applications available in the market today, which can be integrated with different software like ATS, HRIS, LMS, etc. 

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Assessment API Integration: Top Use Cases

Assessment software is a part of the larger ecosystem of software that companies today use to manage their people's operations. Invariably, there are several other tools and software in the market today, which when integrated with assessment APIs can lead to operational efficiency and smooth HR and related processes. There are several categories of tools out there which either feed data into assessment APIs (write APIs) or get access to data from assessment APIs (read APIs). Integration ensures that such data syncs are automated and do not require any manual interview, which can be prone to errors, time consuming and operationally taxing. Here are some of the top use cases for assessment API integration across different software. 

Assessment API integration for ATS tools

Assessment API integration is very critical for ATS or applicant tracking systems. ATS tools and platforms have all the required information about candidates, including their name, profile, pictures, contact information, etc. Assessment API integration with ATS tools ensures that the assessment read API can get access to all these details automatically without any manual intervention. At the same time, integration also facilitates real-time information updation in assessment tools, which can set up assessments for new applicants almost immediately. This leads to faster turnaround. Furthermore, the assessment write APIs can feed information back to the ATS tools with the assessment results and scorecards to help update the candidate’s status in the recruitment flow. 

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Examples: Greenhouse Software, Workable, BambooHR, Lever, Zoho

Assessment API integration for candidate screening tools

Candidate screening tools help organizations determine whether or not a candidate is ideal or right for the role in question. Integration with assessment software ensures that data about a candidate’s performance in an assessment test is automatically synced for screening managers to assess the skills, competencies and abilities of the candidate and its relevance to the open position. Furthermore, assessment API integration with candidate screening tools ensures that the latter have real time access to candidate assessment results for immediate hiring decision making, based on evidence backed data for smart hiring. 

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Examples: 

Assessment API integration for HRIS tools

Assessment API integration with HRIS tools is a no brainer. Once a candidate clears the assessments and is offered a job at an organization, it is essential to capture the results from the assessments in the HRIS platform. Here, the assessment write APIs play an important role. They help HR teams get access to all the relevant information about an employee based on different personality, psychometric, behavioral, cognitive tests to help them capture employee records which are robust and comprehensive. Automated integration of data from assessment tools to HRIS platforms ensures that no human error or bias crawls in when assessment data is being entered into HRIS portals. Furthermore, since many parts of an assessment test can be sensitive, such integration ensures that data exchange is confidential and on a need to know basis only. 

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Examples: BambooHR, Namely, SAP SuccessFactors, Gusto

Assessment API integration for interview scheduling tools

Most companies today leverage interview scheduling tools to automate their entire interview processes, including blocking calendars, managing schedules, etc. For interview scheduling tools, integration with assessment APIs is important to ensure that all interviews with candidates can be scheduled effectively, keeping in my mind both interviewer and interviewee schedules. Interview scheduling tools can leverage assessment read APIs to understand the assessment availability and dates to schedule the interview. Furthermore, once the interview is scheduled, assessment write APIs can help provide updates on whether or not the candidate attended the interview, status, next steps to help interview scheduling tools effectively conduct interactions with candidates as needed. 

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Examples: Calendly, Sense, GliderAI, YouCanBookMe, Paradox

Assessment API integration for LMS tools 

While most assessment software have use cases in the pre-employment stages, their utility can also transcend into post employment phases as well. The LMS tools can easily leverage assessment read APIs to understand the type of assessment tests available which can be used for internal training purposes. Furthermore, candidate performance in pre-employment assessment tests can be used as a baseline to define the types of training required and areas for upskilling. Overall, this integration can help identify the learning needs for the organization and clarify the assessments available for further investigation. At the same time, once the assessments are administered, the assessment write API can automatically sync the relevant data and results for post employment assessment on whether or not employees participated in the assessments, results, gaps, etc. to the LMS tools for better decision making on employee training and development. 

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Example: TalentLMS, 360Learning, Docebo, Google Classroom 

Assessment API integration for talent management tools

Talent management and workforce planning tools are integral when it comes to succession planning for any organization. Assessments conducted, both pre and post employment can greatly help in determining the talent needs for any organization. Talent management tools can leverage assessment read APIs to understand how their existing or potential talent is performing along areas critical to the organization. Any gaps in the talent or consistent poor performance in a particular area of assessment can then be identified to adopt corrective measures. Assessment API integration can help talent management tools effectively understand the talent profile in their organization, which can further help in better succession planning and talent analytics. 

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Examples: ClearCompany, Deel, ActivTrak

Unified Assessment API Integration: All You Need to Know

There are several ways companies can achieve assessment API integration to suit their use cases. Right from building integrations in-house for each assessment tool to practices like workflow automation tools, there are several ways to integrate. However, as the number of customers and integration needs increase exponentially, going for a unified assessment API for integration is the best move. Here are a few instances when choosing a unified API for assessment software integration makes sense. Use unified assessment API when you:

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• Wish to integrate with multiple assessment software based on your customer needs and developing point to point integration with each one is not viable
• Have a large customer base and their integration requests for new assessment software is increasing
• Need to deliver integrations in a short span of time and you lack dedicated engineering bandwidth in-house
• Want to achieve assessment API integration at a lower cost with faster time to market
• Don’t want to take the burden of ongoing maintenance and management of each integration
• Want to skip the effort that goes into reading through API documentation for each integration
• Want to normalize data across assessment platforms and ensure that this transformation process is 10X faster than you internal processes
• Want to ensure complete security, especially due to the nature of sensitive information in question
• Seek real-time sync and exchange of data for immediate action or the flexibility to customize syncs as per your needs
• Want to skip the process of learning about different authentication keys, rate limits, etc. for different APIs

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Now that you know a unified assessment API is the best and the most effective for you to build integrations with assessment software, go through the following questions to choose the best unified assessment API for your organization. 

What are the data models?

The ideal unified API normalizes and syncs data into a unified data model and facilitates data transformation 10x faster. While most fields are common and a unified model works, choose a unified assessment API which also gives you the flexibility to add some custom data models which may not align with the standard data models available. 

What are the rate limits?

Each unified API will offer rate limits, which is the number of API requests or data sync requests you can make in a given period of time. Having an optimum rate limit is extremely important. Having a very high rate limit, in which many requests can be made can lead to potential DDoS attacks and other vulnerabilities. Whereas, having a very low rate limit, where only a handful API requests can be made, might lead to inefficiencies and data inaccuracies. Therefore, gauge the rate limits offered to check if they align with your needs or if they can be customized for you. 

How secure is the integration process?

Next, any unified assessment API you choose should be high on security. On the one hand, check for compliance with all certifications and global standards. On the other hand, look out for comprehensive data encryption, which involves encrypting data at rest and in transit. When looking at security, do check the level of authentication and authorization available.  

What kind of post integration support is available?

Building integrations is followed by the operationally and technically draining tasks of managing integrations. Integration maintenance and management can take anywhere between 5-10 hours of your engineering bandwidth. Therefore, choose a unified assessment API provider which provides you with maintenance support. You should be able to manage the health of all your integrations with a robust track of all API calls, requests, etc. 

What is the sync frequency?

As data sync is the most important part of assessment API integration, check the sync frequency offered by the unified API. While real-time sync, powered by a webhook architecture which ensures real-time data transfer, without any polling infrastructure is ideal. It is equally important to have something which can be customized and allows you to set the sync frequency as per your needs. 

How easy is it to scale?

The key purpose of a unified assessment API is to scale as fast as possible and ensure all customer assessment tools are integrated with. Therefore, you must check the breadth of assessment API integrations being offered. At the same time, explore how open and forthcoming the unified API provider is to custom integrations for you if needed. This also needs to be weighted against the time taken for each new integration and any cost associated with the same. 

Can it take a high data load?

Finally, as you add more assessment API integrations and the number of customers using the same increase, the data load for sync will experience an exponential rise. Thus, your unified assessment API must facilitate guaranteed scalability with quality sync, irrespective of the data load. Without the same, there are chances of data corruption. 

Knit: Unified Assessment API

As a leading unified assessment API, Knit has the right tick mark for all the considerations mentioned above and much more. Here’s why you should consider Knit for your assessment API integration needs:

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• Unified data model which normalizes data with option to customize some fields
• Double encryption of data with encryption for PII and user credentials
• Compliance with  SOC2, GDPR, ISO27001
• No storage of a copy of any data that passes through Knit
• Webhook architecture for real time data sync irrespective of data load
• Option to customize sync frequency whenever needed
• Availability of OAuth, API key or a username-password based authentication
• Bi-directional data sync to read and write from any assessment software
• Detailed Logs, Issues, Integrated Accounts and Syncs page for integration management

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Book a demo today to learn about the other ways in which Knit can be your ideal unified assessment API partner, how it works and anything else you need to know!

Wrapping up: TL:DR

Integrating with assessment APIs can help different companies and platforms unlock value to better streamline their operations. Assessment API integration can facilitate bi-directional sync of data between assessment tools and other applications. While there are several ways to achieve such integration, a unified API is one of the top contenders as it facilitates data normalization, high levels of security, guaranteed scalability, seamless maintenance and management and real time data syncs. 

With organizations increasingly prioritizing seamless issue resolution—whether for internal teams or end customers—ticketing tools have become indispensable. The widespread adoption of these tools has also amplified the demand for streamlined integration workflows, making ticketing integration a critical capability for modern SaaS platforms.

By integrating ticketing systems with other enterprise applications, businesses can enhance automation, improve response times, and ensure a more connected user experience. In this article, we will explore the different facets of ticketing integration, covering what it entails, its benefits, real-world use cases, and best practices for successful implementation.

Decoding Ticketing Integration

Ticketing integration refers to the seamless connection between a ticketing platform and other software applications, allowing for automated workflows, data synchronization, and enhanced operational efficiency. These integrations can broadly serve two key functions—internal process optimization and customer-facing enhancements.

Internally, ticketing integration helps businesses streamline their operations by connecting ticketing systems with tools such as customer relationship management (CRM) platforms, enterprise resource planning (ERP) systems, human resource information systems (HRIS), and IT service management (ITSM) solutions. For example, when a customer support ticket is created, integrating it with a CRM ensures that all relevant customer details and past interactions are instantly accessible to support agents, enabling faster and more personalized responses.

Beyond internal workflows, ticketing integration plays a vital role in customer-facing interactions. SaaS providers, in particular, benefit from integrating their applications with the ticketing platforms used by their customers. This allows for seamless issue tracking and resolution, reducing the friction caused by siloed systems. 

Benefits of Ticketing Integration

Faster resolution time

By automating ticket workflows and integrating support systems, teams can respond to and resolve customer issues much faster. Automated routing ensures that tickets reach the right department instantly, reducing delays and improving overall efficiency.

Example: A telecom company integrates its ticketing system with a chatbot, allowing customers to report issues 24/7. The chatbot categorizes and assigns tickets automatically, reducing average resolution time by 30%.

Eliminates manual data entry and incidence of errors

Manual ticket logging can lead to data discrepancies, miscommunication, and human errors. Ticketing integration automatically syncs information across platforms, minimizing mistakes and ensuring that all stakeholders have accurate and up-to-date records.

Example: A SaaS company integrates its CRM with the ticketing system so that customer details and past interactions auto-populate in new tickets. This reduces duplicate entries and prevents errors like assigning cases to the wrong agent.

Streamlined communication

Integration breaks down silos between teams by ensuring everyone has access to the same ticketing information. Whether it’s support, sales, or engineering, all departments can collaborate effectively, reducing response times and improving the overall customer experience.

Increased customer acquisition and retention rates

SaaS applications that integrate with customers' ticketing systems offer a seamless experience, making them more attractive to potential users. Customers prefer apps that fit into their existing workflows, increasing adoption rates. Additionally, once users experience the efficiency of ticketing integration, they are more likely to continue using the product, driving customer retention.

Example: A project management SaaS integrates with Jira Service Management, allowing customers to convert project issues into tickets instantly. This integration makes the SaaS tool more appealing to Jira users, leading to higher sign-ups and long-term retention.‍

Real-time update on ticket status across different platforms

Customers and internal teams benefit from instant updates on ticket progress, reducing uncertainty and frustration. This real-time visibility helps teams proactively address issues, avoid duplicate work, and provide timely responses to customers.

Ticketing API Data Models

Here are a few common data models for ticketing integration data models:

• Ticket – Stores details of support requests, including ID, status, priority, and assigned agent.
• User – Represents customers or internal users with attributes like name, email, role, and organization.
• Agent – Tracks support agents, their workload, expertise, and assigned tickets.
• Organization – Groups users under companies or departments for streamlined support.
• Comment – Logs ticket updates, internal notes, and customer responses.
• Attachment – Stores files, images, or media linked to tickets.
• Tag – Assigns labels to tickets for categorization and filtering.
• Time Tracking – Logs the time spent on each ticket or task for billing and performance monitoring.
• Priority Rule – Defines conditions for auto-assigning ticket priority levels.
• Team – Represents agent groups handling specific ticket categories.
• Notification – Defines email, SMS, or in-app alerts triggered by ticket updates.
• Access Control – Manages permissions and visibility settings for users, agents, and admins.

Ticketing Integration Best Practices for Developers

Integrating ticketing systems effectively requires a structured approach to ensure seamless functionality, optimized performance, and long-term scalability. Here are the key best practices developers should follow when implementing ticketing system integrations.

Choose the ticketing tools and use cases for integration

Choosing the appropriate ticketing system is a critical first step in the integration process, as it directly impacts efficiency, customer satisfaction, and overall workflow automation. Developers must evaluate ticketing platforms like Jira, Zendesk, and ServiceNow based on key factors such as automation capabilities, reporting features, third-party integration support, and scalability. A well-chosen tool should align not only with internal team workflows but also with customer-facing requirements, particularly for integrations that enhance user experience and service delivery. Additionally, preference should be given to widely adopted ticketing solutions that are frequently used by customers, as this increases compatibility and reduces friction in external integrations. Beyond tool selection, it is equally important to define clear use cases for integration.

Understand the ticketing system API

A deep understanding of the ticketing system’s API is crucial for successful integration. Developers should review API documentation to comprehend authentication mechanisms (API keys, OAuth, etc.), rate limits, request-response formats, and available endpoints. Some ticketing APIs offer webhooks for real-time updates, while others require periodic polling. Being aware of these aspects ensures a smooth integration process and prevents potential performance bottlenecks.

Choose the most appropriate ticketing integration methodology

Choosing the right ticketing integration methodology is crucial for aligning with business objectives, security policies, and technical capabilities. The integration approach should be tailored to meet specific use cases and performance requirements. Common methodologies include direct API integration, middleware-based solutions, and Integration Platform as a Service (iPaaS), including embedded iPaaS or unified API solutions. The choice of methodology should depend on several factors, including the complexity of the integration, the intended audience (internal teams vs. customer-facing applications), and any specific security or compliance requirements. By evaluating these factors, developers can choose the most effective integration approach, ensuring seamless connectivity and optimal performance.

Optimize API calls for performance and efficiency

Efficient API usage is critical to maintaining system performance and preventing unnecessary overhead. Developers should minimize redundant API calls by implementing caching strategies, batch processing, and event-driven triggers instead of continuous polling. Using pagination for large data sets and adhering to API rate limits prevents throttling and ensures consistent service availability. Additionally, leveraging asynchronous processing for time-consuming operations enhances user experience and backend efficiency.

Test and sandbox ticketing integrations

Thorough testing is essential before deploying ticketing integrations to production. Developers should utilize sandbox environments provided by ticketing platforms to test API calls, validate workflows, and ensure proper error handling. Implementing unit tests, integration tests, and load tests helps identify potential issues early. Logging mechanisms should be in place to monitor API responses and debug failures efficiently. Comprehensive testing ensures a seamless experience for end users and reduces the risk of disruptions.

Design for scalability and data load

As businesses grow, ticketing system integrations must be able to handle increasing data volumes and user requests. Developers should design integrations with scalability in mind, using cloud-based solutions, load balancing, and message queues to distribute workloads effectively. Implementing asynchronous processing and optimizing database queries help maintain system responsiveness. Additionally, ensuring fault tolerance and setting up monitoring tools can proactively detect and resolve issues before they impact operations.

Popular Ticketing API

In today’s SaaS landscape, numerous ticketing tools are widely used by businesses to streamline customer support, issue tracking, and workflow management. Each of these platforms offers its own set of APIs, complete with unique endpoints, authentication methods, and technical specifications. Below, we’ve compiled a list of developer guides for some of the most popular ticketing platforms to help you integrate them seamlessly into your systems:

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• Asana API integration guide
• Freshdesk API guide
• GitHub REST API documentation
• Zendesk API for ticketing
• Jira API integration guide
• Trello REST API documentation
• Zoho Desk API Documentation
• ServiceNow API documentation

Ticketing Integration: Use Cases and Examples

Bidirectional sync between a ticketing platform and a CRM to ensure both sales and support teams have up-to-date information

CRM-ticketing integration ensures that any change made in the ticketing system (such as a new support request or status change) will automatically be reflected in the CRM, and vice versa. This ensures that all customer-related data is current and consistent across the board. For example, when a customer submits a support ticket via a ticketing platform (like Zendesk or Freshdesk), the system automatically creates a new entry in the CRM, linking the ticket directly to the customer’s profile. The sales team, which accesses the CRM, can immediately view the status of the issue being reported, allowing them to be aware of any ongoing concerns or follow-up actions that might impact their next steps with the customer.

As support agents work on the ticket, they might update its status (e.g., “In Progress,” “Resolved,” or “Awaiting Customer Response”) or add important resolution notes. Through bidirectional sync, these changes are immediately reflected in the CRM, keeping the sales team updated. This ensures that the sales team can take the customer’s issues into account when planning outreach, upselling, or renewals. Similarly, if the sales team updates the customer’s contact details, opportunity stage, or other key information in the CRM, these updates are also synchronized back into the ticketing system. This means that when a support agent picks up the case, they are working with the most accurate and recent information. 

Integrating a ticketing platform with collaboration tools for real time communication of issues and resolution 

Collaboration tool-ticketing integration ensures that when a customer submits a support ticket through the ticketing system, a notification is automatically sent to the relevant team’s communication tool (such as Slack or Microsoft Teams). The support agent or team is alerted in real-time about the new ticket, and they can immediately begin the troubleshooting process. As the agent works on the ticket—changing its status, adding comments, or marking it as resolved—updates are automatically pushed to the communication tool. 

The integration may also allow for direct communication with customers through the ticketing platform. Support agents can update the ticket in real-time based on communication happening within the chat, keeping customers informed of progress, or even resolving simple issues via a direct message.

Automating ticket creation from AI chatbot interactions to streamline customer support

Integrating an AI-powered chatbot with a ticketing system enhances customer support by enabling seamless automation for ticket creation, tracking, and resolution, all while providing real-time assistance to customers. When a customer interacts with the chatbot on the support portal or website, the chatbot uses NLP to analyze the query. If the issue is complex, the chatbot automatically creates a support ticket in the ticketing system, capturing the relevant customer details and issue description. This integration ensures that no query goes unresolved, and no customer issue is overlooked.

Once the ticket is created, the chatbot continuously engages with the customer, providing real-time updates on the status of their ticket. As the ticket progresses through various stages (e.g., from “Open” to “In Progress”), the chatbot retrieves updates from the ticketing system and informs the customer, reducing the need for manual follow-ups. When the issue is resolved and the ticket is closed by the support agent, the chatbot notifies the customer of the resolution, asks if further assistance is needed, and optionally triggers a feedback request or satisfaction survey. 

Streamlining employee support with HRIS-ticketing integration 

Ticketing integration with a HRIS offers significant benefits for organizations looking to streamline HR operations and enhance employee support. For example, when an employee raises a ticket to inquire about their leave balance, the integration allows the ticketing platform to automatically pull relevant data from the HRIS, enabling the HR team to provide accurate and timely responses. 

The workflow begins with the employee submitting a ticket through the ticketing platform, which is then routed to the appropriate HR team based on predefined rules or triggers. The integration ensures that employee data, such as job role, department, and contact details, is readily available within the ticketing system, allowing HR teams to address queries more efficiently. Automated responses can be triggered for common inquiries, such as leave balances or policy questions, further speeding up resolution times. Once the issue is resolved, the ticket is closed, and any updates, such as approved leave requests, are automatically reflected in the HRIS.

Read more: Everything you need to know about HRIS API Integration 

Enhancing payroll efficiency and employee satisfaction through ticketing-payroll integration

Integrating a ticketing platform with a payroll system can automate data retrieval, streamline workflows, and provide employees with faster, more accurate responses. It begins when an employee submits a ticket through the ticketing platform, such as a query about a missing payment or a discrepancy in their paycheck. The integration allows the ticketing platform to automatically pull the employee’s payroll data, including payment history, tax details, and direct deposit information, directly from the payroll system. This eliminates the need for manual data entry and ensures that the HR or payroll team has all the necessary information at their fingertips. The ticket is then routed to the appropriate payroll specialist based on predefined rules, such as the type of issue or the employee’s department.

Once the ticket is assigned, the payroll specialist reviews the employee’s payroll data and investigates the issue. For example, if the employee reports a missing payment, the specialist can quickly verify whether the payment was processed and identify any errors, such as incorrect bank details or a missed payroll run. After resolving the issue, the specialist updates the ticket with the resolution details and notifies the employee. If any changes are made to the payroll system, such as reprocessing a payment or correcting tax information, these updates are automatically reflected in both systems, ensuring data consistency. Similarly, if an employee asks about their upcoming pay date, the ticketing platform can automatically generate a response using data from the payroll system, reducing the workload on the payroll team. 

Simplifying e-commerce customer support with order management and ticketing integration

Ticketing-e-commerce order management system integration can transform how businesses handle customer inquiries related to orders, shipping, and returns. When a customer submits a ticket through the ticketing platform, such as a query about their order status, a request for a return, or a complaint about a delayed shipment, the integration allows the ticketing platform to automatically pull the customer’s order details—such as order number, purchase date, shipping status, and tracking information—directly from the order management system. 

The ticket is then routed to the appropriate support team based on the type of inquiry, such as shipping, returns, or billing. Once the ticket is assigned, the support agent reviews the order details and takes the necessary action. For example, if a customer reports a delayed shipment, the agent can check the real-time shipping status and provide the customer with an updated delivery estimate. After resolving the issue, the agent updates the ticket status and notifies the customer with bi-directional sync, ensuring transparency throughout the process.

Common Ticketing Integration Challenges

As you embark on your integration journey, it is integral to understand the roadblocks that you may encounter. These challenges can hinder productivity, delay response times, and lead to frustration for both engineering teams and end-users. Below, we explore some of the most common ticketing integration challenges and their implications.

1. Lack of Comprehensive Documentation and Support

A critical factor in the success for ticketing integration is the availability of clear, comprehensive documentation. The integration of ticketing platforms with other systems depends heavily on well-documented API and integration guides. Unfortunately, many ticketing platforms provide limited or outdated documentation, leaving developers to navigate challenges with minimal guidance.

The implications of inadequate documentation are far-reaching:

• Incomplete or outdated API documentation: This slows down the integration process, as developers have to spend additional time figuring out how the API works. Without up-to-date details, developers might face difficulties with deprecated functions or changes in API behavior that were not clearly communicated.
• Limited customer support from ticketing providers: In many cases, ticketing providers offer minimal or low-quality customer support, which can leave developers and IT teams without the necessary guidance. When issues arise, support teams might be slow to respond, and troubleshooting might take longer than necessary.
• Restricted availability of API documentation: Some platforms require developers to pay additional fees for access to documentation or even restrict access entirely. In some cases, the documentation is difficult to find, or it is only available in specific languages, making it inaccessible to a broader range of developers. 
• Trial-and-error debugging: Without detailed documentation and support, developers are often forced to resort to trial-and-error methods to resolve integration issues. This increases both the time and cost of development. 

2. Inadequate Error Handling and Logging Mechanisms

Error handling is an essential part of any system integration. When integrating ticketing systems with other platforms, it is important for developers to be able to quickly identify and resolve errors to prevent disruptions in service. Unfortunately, many ticketing systems fail to provide detailed and effective error-handling and logging mechanisms, which can significantly hinder the integration process.

Key challenges include:

• Poorly structured error messages: In many cases, error messages generated by the ticketing system are vague or poorly structured, which makes it difficult for developers to understand the nature of the problem. Without clear error messages, developers may waste valuable time attempting to troubleshoot the issue based on limited or unclear information.
• Lack of real-time logging capabilities: Real-time logging is essential for tracking issues as they occur and for identifying the root causes of integration problems. Without real-time logging, teams are forced to rely on static logs, which may not provide the necessary information to quickly resolve the issue.
• Minimal documentation on error resolution: Many ticketing systems fail to offer adequate documentation on how to resolve errors that may arise during integration. Without this guidance, developers are left to figure out solutions on their own, which can increase the time needed to resolve problems and cause unnecessary downtime.

Read more: API Monitoring and Logging

3. Scalability Issues Due to High Data Load

As organizations grow, so does the volume of data generated through ticketing systems. When an integration is not designed to handle large volumes of data, businesses may experience performance issues such as slowdowns, data loss, or bottlenecks in the system. Scalability is therefore a key concern when integrating ticketing systems with other platforms.

Some of the key scalability challenges include:

• Limited API rate limits: Many ticketing platforms impose rate limits on the number of API calls that can be made in a given period. When the volume of tickets increases, these rate limits can lead to delays in processing requests, which can slow down the overall system and create backlogs.
• Inefficient data sync methods: Some ticketing systems use data synchronization methods that require excessive API calls, leading to inefficiencies. When large volumes of data need to be synced, the integration process can become sluggish, causing delays in ticket updates or ticket status changes.
• Increased ticket volume leading to database overload: As more tickets are created and processed, the underlying databases can become overloaded, resulting in performance degradation. If the system is not designed to handle such growth, it can cause significant slowdowns in retrieving and updating ticket data.

4. Managing Multiple Ticketing Tools and Use Cases

In many organizations, different teams use different ticketing tools that are tailored to their specific workflows. Integrating multiple ticketing systems can create complexity, leading to potential data inconsistencies and synchronization challenges. 

Key challenges include:

• Market fragmentation: The expanding ticketing ecosystem means that organizations may have to integrate with multiple platforms that cater to different needs. This can lead to a fragmented approach to managing tickets, which can overwhelm internal engineering resources and create integration backlogs.
• High integration costs: Integrating multiple ticketing systems typically costs around $10K USD per integration and takes 4-6 weeks. This includes development, customization, and ongoing maintenance, which can strain resources, delay other initiatives, and escalate costs across the organization.
• Synchronizing updates across systems: Keeping different ticketing systems synchronized can be difficult, especially when updates are made to one system but not immediately reflected in others. This can lead to delays, duplication of data, or inconsistent information across platforms.
• Customization needs: Each integration may require unique customizations based on the specific features of the systems involved. This adds to the complexity of the integration process and increases development time and costs.

5. Limited Testing Environments and Sandbox Access

Testing the integration of ticketing systems is critical before deploying them into a live environment. Unfortunately, many ticketing platforms offer limited or restricted access to testing environments, which can complicate the integration process and delay project timelines.

Key challenges include:

• Limited access to test data: Many platforms do not provide sufficient test data or environments that simulate real-world scenarios. This makes it difficult for developers to accurately assess how the integration will perform under typical operating conditions.
• Lack of rollback options: If an integration fails or produces unintended results, it is important to have a way to roll back the changes. Unfortunately, many ticketing platforms do not offer rollback features, making it harder to recover from failed integrations.
• Restricted sandbox functionality: Sandbox environments often lack the full functionality of a live environment, which means that testing can be incomplete. Without the ability to fully test the integration, organizations risk deploying an incomplete or flawed solution.
• Complicated testing agreements: Some ticketing vendors require lengthy agreements or monetary engagements to provide access to testing environments. This process can be time-consuming and might delay the integration process, especially if it is not part of the initial contract.

6. Compatibility Challenges Between Systems

Another common challenge in ticketing system integration is compatibility between different systems. Ticketing platforms often use varying data formats, authentication methods, and API structures, making it difficult for systems to communicate effectively with each other.

Some of the key compatibility challenges include:

• Varying authentication protocols: Different platforms use different authentication methods, such as OAuth, API keys, or other proprietary methods. Integrating these systems requires developers to understand and implement the appropriate authentication protocols, which can add complexity to the integration process.
• Differences in data structures and formats: Ticketing systems may use different data structures or formats, which can lead to difficulties in mapping data correctly between systems. Inconsistent data types or mismatched fields can cause data inconsistencies or truncation during the integration process.

7. Ongoing Maintenance and Management

Once an integration is completed, the work is far from finished. Ongoing maintenance and management are essential to ensure that the integration continues to function smoothly as both ticketing systems and other integrated platforms evolve.

Some of the key maintenance challenges include:

• API updates: API providers may update their APIs, which can break existing integrations if the changes are not properly managed. If ticketing platforms undergo updates or changes to their workflows, these modifications may require frequent adjustments to the integration.
• Deprecated APIs: As APIs evolve, older versions may be deprecated. Organizations must retire deprecated APIs to ensure that their integrations continue to work smoothly. Failing to do so can result in integration failures or poor performance.
• Incompatible changes: Occasionally, API providers may make backward-incompatible changes to their APIs. If the response format or structure changes unexpectedly, it can cause data corruption or system failures.
• Regular monitoring: Continuous monitoring of the integration is required to ensure that data is flowing properly and that performance is maintained. Without regular oversight, issues may go unnoticed until they escalate into major problems.

Building Your First Ticketing Integration with Knit: Step-by-Step Guide

Knit provides a unified ticketing API that streamlines the integration of ticketing solutions. Instead of connecting directly with multiple ticketing APIs, Knit’s AI allows you to connect with top providers like Zoho Desk, Freshdesk, Jira, Trello and many others through a single integration.

Getting started with Knit is simple. In just 5 steps, you can embed multiple CRM integrations into your App.

Steps Overview:

1. Create a Knit Account: Sign up for Knit to get started with their unified API. You will be taken through a getting started flow.
2. Select Category: Select Ticketing API from the list of available option on the Knit dashboard
3. Register Webhook: Since one of the use cases of Ticketing integrations is to sync data at frequent intervals, Knit supports scheduled data syncs for this category. Knit operates on a push based sync model, i.e. it reads data from the source system and pushes it to you over a webhook, so you don’t have to maintain a polling infrastructure at your end. In this step, Knit expects you to tell us the webhook over which it needs to push the source data.
4. Set up Knit UI to start integrating with Apps: In this step you get your API key and integrate with the Ticketing App of your choice from the frontend. If we don't support an App yet, we will add it in 2 days.
5. Get started with your use case: Let Knit AI know whether you want to read or write ticketing data, the data you want to work on, & whether you want to run scheduled syncs or call APIs on demand. Knit will build a connector for you.
6. Public your connector: Test the connector Knit AI has built with your or Knit's available sandboxes. If all looks good, just publish. 
7. Fetch data and make API calls: That’s it! It’s time to start syncing data and making API calls and take advantage of Knit unified APIs and its data models. 

Read more: Getting started with Knit

Knit's Ticketing API vs. Direct Connector APIs: A Comparison

Choosing the ideal approach to building and maintaining ticketing integration requires a clear comparison. While traditional custom connector APIs require significant investment in development and maintenance, a unified ticketing API like Knit offers a more streamlined approach with faster integration and greater flexibility. Below is a detailed comparison of these two approaches based on several crucial parameters:

Read more: How Knit Works

Security Considerations for Ticketing Integrations

Below are key security risks and mitigation strategies to safeguard ticketing integrations.

Challenges

1. Unauthorized Access to sensitive customer information, including personally identifiable information (PII). A lack of robust authentication and authorization controls can result in compromised data, potentially exposing confidential details to malicious actors. Without proper access management, attackers could gain entry to systems, viewing or modifying tickets and customer profiles.
2. Injection Attacks are a common vulnerability in API integrations, where attackers inject malicious code through user input fields or API calls. This could allow them to execute unauthorized actions, such as manipulating data, altering configurations, or launching further attacks. In some cases, injection attacks can lead to severe system compromise, leaving the entire infrastructure vulnerable.
3. Data Exposure can result from insufficient encryption and weak transmission protocols. Without adequate data masking, validation, or encryption, information such as customer payment details and communication histories can be intercepted during transmission or accessed by unauthorized individuals. This type of exposure can result in severe consequences, including identity theft and financial fraud.
4. DDoS Attacks are another significant threat to ticketing integrations. By overwhelming an API with a flood of requests, attackers can render the service unavailable, impacting customer support and damaging reputation. If the API lacks sufficient protection mechanisms, the service could suffer extended downtimes, resulting in lost productivity and customer trust.

Mitigation Strategies

To safeguard ticketing integrations and ensure a secure environment, organizations should employ several mitigation strategies:

1. Strong Authentication and Authorization: Implement robust authentication mechanisms, such as OAuth, JWT (JSON Web Tokens), Bearer tokens, and API keys, to ensure only authorized users can access ticketing data. Additionally, enforcing proper role-based access control (RBAC) ensures users only have access to necessary data based on their responsibilities.
2. Secure Data Transmission: Use HTTPS for secure data transmission between clients and servers, ensuring that all data is encrypted. For sensitive customer data, implement end-to-end encryption to prevent interception during communication.
3. Input Validation and Parameter Sanitization: Protect APIs by leveraging input validation and parameter sanitization techniques to prevent injection attacks. These techniques ensure that only valid data is processed and malicious inputs are blocked before they can cause harm to your systems.
4. Rate Limiting and Throttling: Implement rate limiting and throttling to prevent DDoS attacks. These mechanisms can control the number of requests made to the API within a specific timeframe, ensuring that the service remains available even during high traffic or malicious attack attempts.

Evaluating Security for Ticketing Integrations

When evaluating the security of a ticketing integration, consider the following key factors:

1. Check compliance: Ensure that the third-party API complies with industry standards and regulations, such as GDPR, HIPAA, SOC2, or PCI DSS, depending on your specific requirements.
2. Data Privacy and Platform Choice: Choose a platform that does not cache sensitive customer data or store it unnecessarily. This reduces the attack surface and minimizes the risk of exposure. Ensure the platform complies with data privacy regulations like GDPR or CCPA.
3. Security Frameworks and Best Practices Make sure that the integration follows security principles such as the "least privilege" approach, ensuring users have only the permissions necessary to perform their job functions. Implement role-based access controls (RBAC) and maintain an audit trail of all user activities for transparency and accountability.
4. Documentation and Incident Response Evaluate the platform’s documentation and ensure it provides clear guidance on security best practices. Additionally, review the incident response plan to ensure that the organization is prepared for potential security breaches, minimizing downtime and mitigating damage.

Read more: API Security 101: Best Practices, How-to Guides, Checklist, FAQs

TL:DR

Ticketing integration connects ticketing systems with other software to automate workflows, improve response times, enhance user experiences, reduce manual errors, and streamline communication. Developers should focus on selecting the right tools, understanding APIs, optimizing performance, and ensuring scalability to overcome challenges like poor documentation, error handling, and compatibility issues.

Solutions like Knit’s unified ticketing API simplify integration, offering faster setup, better security, and improved scalability over in-house solutions. Knit’s AI-driven integration agent guarantees 100% API coverage, adds missing applications in just 2 days, and eliminates the need for developers to handle API discovery or maintain separate integrations for each tool.

Integrations are becoming a mainstream requirement for organizations using many SaaS applications. Invariably, organizations seek robust third-party solutions as alternatives to building and managing all integrations in-house (because it is time—and cost-intensive and diverts engineering bandwidth). Workflow automation, embedded iPaaS, ETL, and unified API are a few options that organizations are increasingly adopting. 

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Which integration approach to choose?

As mentioned above, you can ship and scale SaaS integrations in several ways. Here is a quick snapshot of the different approaches and their viability for different scenarios:

If you’d like to learn more about different approaches, you could consider reading a detailed article here

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While Merge.dev has become one of the popular solutions in the unified API section, there are alternatives to Merge that support native integration development and management for SaaS applications. Each with their own advantages and drawbacks. 

In this article, we will discuss in detail Merge.dev and other market players who stand as credible alternatives to help companies scale their integration roadmap. A comprehensive comparison detailing the strengths and weaknesses of each alternative will enable businesses to make an informed choice for their integration journey. 

Building and managing SaaS integrations with Merge.dev

Merge.dev is a unified API that helps businesses to build 1: many integrations with SaaS applications. This means that Merge enables companies to build native integrations with multiple applications within the same category (ex., ATS, HRIS) in a single go, using one connector which Merge provides. Invariably, this makes the integration development and management process extremely lucid, saves time and resources, and makes integration scalability robust and effective. Let’s quickly look at the top strengths and weaknesses of Merge.dev as an integration solution for SaaS businesses, and how it compares with other alternatives.

Pricing: Starts at $7800/ year and goes up to $55K+

Merge.dev strengths

Coverage within integration categories

One of the most prominent features in favor of Merge as a preferred integration solution is the number of integrations it supports within different categories. Overall, SaaS businesses can integrate 150+ third-party applications once they connect with Merge’s unified API for different categories. This coverage or the potential integration pool that companies can leverage is significantly high as per current market standards. 

Managed authentication

Second, Merge offers managed authentication to its customers. Most applications today are based on OAuth for authentication and authorization, which require access and refresh tokens. By supporting managed authentication, Merge takes care of the authentication process for each application and keeps track of expiry rules to ensure a safe but hassle-free authentication process.  

Simplified processes

Overall, customers who have used Merge to integrate with third-party applications claim that the entire setup and integration process is quite smooth and simple. At the same time, responsiveness to feedback is high, and even the integration process for end customers is rather seamless. 

Merge.dev weaknesses

Limited integration categories

While the integrations within the unified API categories represent decent coverage for Merge, the total number of categories (6+1 in Beta) is considered to be limited by many organizations. This means that organizations that wish to integrate with applications that don’t fall into those categories have to look for alternatives. Thus, the vertical categories are a limitation that customers find with Merge, and unless this is sufficient critical mass, the addition of a new unified API category may not be justified.  

Auth component

Merge offers limited flexibility when it comes to designing and styling the auth component or branding the end user experience. It uses an iframe for its frontend auth component, which has limited customization capabilities compared to other alternatives in the market. This limits organizations' ability to ensure that the auth component that the end customers interact with looks and feels like their own application.  

Data sync model

When it comes to data sync, Merge uses a pull model, which requires organizations to build and maintain a polling infrastructure for each connected customer. The application is expected to poll the Merge’s copy of the data periodically. For data syncs needed at a higher or ad-hoc frequency, organizations can write sync functions and pull data that has changed since the last sync. While the data load is reduced in this option; however, the requirement for a polling infrastructure remains. 

On the other hand, Merge offers webhooks for data sync in two ways, i.e., sync notifications and changed data webhooks. While in the former, i.e., sync notification, organizations are notified about a potential change in the data but have to fall back on polling infrastructure to sync the changed data. Changed data webhooks exist with Merge. However, the scale and data delivery via these webhooks are not guaranteed. Depending on the data load, potential downtime, or failed processing, changed data webhooks might fail for Merge, persuading organizations to maintain a polling infrastructure. Pulling data and maintaining a polling infrastructure becomes an added responsibility for organizations, which become inclined toward identifying alternative solutions. 

Integration management

Merge’s support for integration management is robust. However, the customer success dashboards are considered technical for some organizations. This means that customer success executives and client-facing personnel have to rely on engineering teams and resources to understand the dashboards. At the same time, no tools can give visibility into integration activity, further increasing the reliance on engineering teams. This invariably slows the integration maintenance process as engineering teams generally prioritize product development and enhancements over integration management. 

TL:DR: Pros and cons of Merge

Why choose Merge

• Offers comprehensive coverage of integrations within chosen categories
• Facilitates hassle-free authentication
• Simplifies the integration process with quick response and seamless customer experience

However, some of the challenges include:

• Limited number of integration categories
• Limited flexibility for frontend auth component
• Requires users to maintain a polling infrastructure
• Lack of visibility into integration activity
• Webhooks based data sync doesn’t guarantee scale and data delivery

There is no doubt that Merge provides a considerably large integration catalog, enabling integration with multiple applications across the defined API categories. However, there are certain other features and parameters that have been pushing businesses to seek alternative solutions to scale their SaaS integration journey. Below is a list of top integration platforms that are being considered as alternatives to Merge.dev:

Merge.dev alternative #1: Knit

One of the top alternatives to Merge has been Knit. As a unified API, Knit supports integration development and management, enabling businesses to integrate with multiple applications within the same category with its 1:many connector. While there are several features which make Knit a preferred unified API provider, below mentioned are the top few which make it a sustainable and scalable Merge alternative. 

Pricing: Starts at $4800 Annually

Data security

Since integrations focus majorly on data exchange between applications, security is of paramount importance. Knit adheres to the industry’s highest standards in terms of its policies, processes and practices, complying with SOC2, GDPR, and ISO27001. In addition, all the data is doubly encrypted, at rest and in transit and all PII and user credentials are encrypted with an additional layer of application security. 

However, Knit's most significant security feature as a Merge alternative is that it doesn’t store a copy of the data. All data requests are pass through in nature, which means that no data is stored in Knit’s servers. This also translates to the fact that no third party can get access to any customer data for any organization via Knit. Since most data that passes through integrations is PII, Knit’s functionality to simply process data in its servers and not store any of it goes a long way in establishing data security and credibility. 

Customizable auth component

Knit has chosen Javascript SDK as its frontend auth component which is built as a true web component for easy customizability. Thus, it offers a lot of flexibility in terms of design and styling. This ensures that the auth component which end customers ultimately interact with has a look and feel similar to the application. Knit provides an easy drop-in frontend and embedded integrations and allows organizations to personalize every component of the auth screen, including text, T&Cs, color, font and logo on the intro screen.

Knit also enables the customization of emails (signature, text and salutations) that are sent to admin in the non-admin integration flow as well as filter the apps/categories that organizations want end customers to see on the auth screen. Finally, all types of authorization capabilities, including OAuth, API key or a username-password based authentication, are supported by Knit. 

Webhooks architecture for data sync

As a Merge alternative, Knit comes with a 100% webhooks architecture. This means that whenever data updates happen, they are dispatched to the organization’s servers in real time and the relevant folks are notified about the sync. In addition to ensuring near real time data sync, Knit has a push based data-sync model which eliminates the need for organizations to maintain a full polling infrastructure. Developers don’t need to pull data updates periodically. 

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Furthermore, unlike certain other unified API providers in the market, Knit’s webhook based architecture ensures guaranteed scalability and delivery irrespective of data load. This means that irrespective of the amount of data being synced between the source and destination apps, data sync with Knit will not fail, offering a 99.99 SLA. Thus, Knit’s approach to data sync with a webhook architecture ensures security, scale and resilience for event driven stream processing. Companies get guaranteed data delivery in real time. 

Customizable data sync frequency

While Knit ensures real time data sync and notifications whenever data gets updated, it also provides organizations with the flexibility and option to limit data sync and API calls as per the need. Firstly, Knit enables organizations to work with targeted data by setting filters to retrieve only the data that is needed, instead of syncing all the data which has been updated. By restricting the data being synced to only what is needed, Knit helps organizations save network and storage costs. 

At the same time, organizations can customize the sync frequency and control when syncs happen. They can start, stop or pause syncs directly from the dashboard, having full authority of when to sync and what to sync. 

Vertical and horizontal coverage

Knit also supports a more diverse portfolio of integration categories. For instance, Knit provides unified APIs for communication, e-signature, expense management and assessment integrations, which Merge is yet to bring to the table. Within the unified API categories, Knit supports a large catalog covering 100+ integrations. Furthermore, the integration catalog sees new integrations being added every month along with new unified API categories also being introduced as per market demands. Overall, Knit provides a significant coverage of HRIS, ATS, Accounting, CRM, E-Sign, Assessment, Communication integrations, covering applications across the popularity and market share spectrum. 

Comprehensive integration management support

Another capability that positions Knit as a credible Merge alternative is the comprehensive integration support it provides post development. Knit provides deep RCA and resolution including ability to identify which records were synced, ability to rerun syncs etc. It also proactively identifies and fixes any integration issues itself. With Knit, organizations get access to a detailed Logs, Issues, Integrated Accounts and Syncs page to monitor and manage integrations. Organizations find it extremely easy to keep a track of API calls, data sync requests as well as status of each webhook registered. 

Furthermore, Knit provides integration management dashboards which make it seamless for frontline customer experience (CX) teams to solve customer issues without getting engineering involved. This ensures that engineering teams can focus on new product development/ enhancements, while the CX team can manage the frequency of syncs from the dashboard without any engineering intervention. 

Custom data models

Finally, Knit supports custom data fields, which are not included in the common unified model. It allows users to access and map all data fields and manage them directly from the dashboard without writing a single line of code. These DIY dashboards for non-standard data fields can easily be managed by frontline CX teams and don’t require engineering expertise. At the same time, Knit gives end users control to authorize granular read/write access at the time of integration. 

Thus, Knit is a definite alternative to Merge which ensures:

• Data security by not storing a copy of the data in its servers
• Near real time data sync without a polling infrastructure
• Comprehensive integration management providing greater control to CX teams
• Guaranteed data scalability and delivery irrespective of data load
• Custom data fields 
• Customizable auth component and sync frequency

Merge.dev alternative #2: Finch

Another alternative to Merge is Finch. Another unified API, Finch stands a popular unified API powering employment integrations, particularly for HRIS and Payroll. 

Pricing: Starts at $600 / connected account annually with limited features‍

Here are some of the key reasons to choose Finch over Merge:

• Higher coverage for HRIS and Payroll integrations (200+)
• Standardized data coverage, i.e. standardizes all employment data across top HRIS and Payroll providers, like Quickbooks, ADP, and Paycom
• Allows users to read and write benefits data, including payroll deductions and contributions programmatically

However, there are certain factors that need to be considered before making Finch the final integration choice:

• Focused only on employment systems, i.e. HRIS and Payroll and doesn’t support integrations for other categories
• A lot of applications supported are what Finch calls “assisted” integrations which means a Finch team member or associate would manually sync data on your behalf.
• Stores a copy of the customer data, which not only adds to the storage cost, but also becomes a data privacy and security concern for end customers
• Low data fields support limited data fields available in the source system
• Provides limited integration management support with no provision for RCA and resolution 
• APIs are relatively simplistic, e.g., doesn’t give employee level payroll data

Merge.dev alternative #3: Apideck

Another Merge alternative in the unified API space is Apideck. One of the major differentiators is its focus on going broad instead of going deep in terms of integrations provided, unlike Merge. 

‍Pricing: Starts at $299/mo for each unified API with a quota of 10,000 API calls‍

Some of the top reasons for integrating with Apideck include:

• Rich category portfolio, supporting more API categories than most of the other options available in the market, including file storage, e-commerce, issue tracking, POS, SMS, etc. 
• A built-in integration marketplace, a portal for displaying integration on the users website
• Simplified UI and integration experience, without going much into technical depths

While the number of categories accessible with Apideck increase considerably, there are some concerns on the way:

• Limited depth within each category i.e. number of integrations within each unified API category is narrow
• Limited customization like custom field/ object mapping per customer and lack of specialization 

Merge.dev alternative #4: Kombo

One of the Merge alternatives for the European market is Kombo. As a unified API, it focuses primarily on helping users build and manage HRIS and ATS integrations. 

Pricing: Kombo’s pricing is not publicly available and can be requested‍

Here are some of the key reasons why certain users choose Kombo as an alternative to Merge:

• Better coverage for European applications. Originating from Germany, Kombo supports HR and recruiting related integrations for European companies
• It is a straightforward, easy to use and secure way to integrate with applications
• Has a decent depth in terms of integrations available in the categories it supports

Nonetheless, there are certain constraints which limit Kombo’s popularity outside Europe:

• Limited integration categories or vertical coverage, limited to only HRIS and ATS integrations
• Limited data coverage and security/ compliance requirements needed for companies beyond Europe 

Merge.dev alternative #5: Integration.app

The final name in this last of Merge alternatives for scaling SaaS integrations is Integration.app. As a unified API, it offers an AI powered integration framework to help businesses scale their in-house integration process. 

Pricing: Starting at $500/mo, along with  per-customer, per-integration, per-connection, and other pricing options‍

Here is a quick look why users are preferring Integration.app as a suitable Merge alternative:

• Offers pre-built integrations along with customization capabilities
• Provides comprehensive documentation, enabling developers to come up the curve quickly, without much effort
• Allows users to read and write data in external applications along with API logs to facilitate integration transparency

However, there are certain limitation with Integration.app, including:

• Very steep learning curve

TL:DR

Each of the unified API providers mentioned above are popular alternatives to Merge and have been adopted by several organizations to accelerate the pace of shipping and scaling integrations. While Merge provides great depth in the integration categories it supports, some of the alternatives bring the following strengths to the table:

• Knit: The only other product recognised as a leader on the G2 grid for 2025. Knit offers a secure alternative and 100% webhooks based architecture, eliminating the need for polling infrastructure and supporting customizable data fields, auth components and data syncs.
• Finch: Specializes in HRIS and Payroll integrations, offering standardized data coverage and benefits data management but lacks in integration depth and management support.
• Apideck: Offers a broad portfolio of API categories, an integration marketplace, and a simplified UI, but lacks depth within each category and customization options.
• Kombo: Ideal for European markets with focus on HRIS and ATS integrations, featuring easy integration with decent depth, but limited to HR-related categories.
• Integration.app: Employs AI for integration framework, offers pre-built integrations with customization, and comprehensive documentation, but has a steep learning curve.

Thus, depending on the use case, pricing framework (usage based, API call based, user based, etc.), features needed and scale, organizations can choose from different Merge alternatives. While some offer greater depth within categories, others offer a greater number of API categories, providing a wide choice for users.

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In today's AI-driven world, AI agents have become transformative tools, capable of executing tasks with unparalleled speed, precision, and adaptability. From automating mundane processes to providing hyper-personalized customer experiences, these agents are reshaping the way businesses function and how users engage with technology. However, their true potential lies beyond standalone functionalities—they thrive when integrated seamlessly with diverse systems, data sources, and applications.

This integration is not merely about connectivity; it’s about enabling AI agents to access, process, and act on real-time information across complex environments. Whether pulling data from enterprise CRMs, analyzing unstructured documents, or triggering workflows in third-party platforms, integration equips AI agents to become more context-aware, action-oriented, and capable of delivering measurable value.

This article explores how seamless integrations unlock the full potential of AI agents, the best practices to ensure success, and the challenges that organizations must overcome to achieve seamless and impactful integration.

Rise of AI Agents

The rise of Artificial Intelligence (AI) agents marks a transformative shift in how we interact with technology. AI agents are intelligent software entities capable of performing tasks autonomously, mimicking human behavior, and adapting to new scenarios without explicit human intervention. From chatbots resolving customer queries to sophisticated virtual assistants managing complex workflows, these agents are becoming integral across industries.

This rise of use of AI agents has been attributed to factors like:

• Advances in AI and machine learning models, and access to vast datasets which allow AI agents to understand natural language better and execute tasks more intelligently.
• Demand for automating routine tasks, reducing the burden on human resources, and improving efficiency, driving operational efficiency

Understanding How AI Agents Work

AI agents are more than just software programs; they are intelligent systems capable of executing tasks autonomously by mimicking human-like reasoning, learning, and adaptability. Their functionality is built on two foundational pillars: 

Contextual Knowledge

For optimal performance, AI agents require deep contextual understanding. This extends beyond familiarity with a product or service to include insights into customer pain points, historical interactions, and updates in knowledge. However, to equip AI agents with this contextual knowledge, it is important to provide them access to a centralized knowledge base or data lake, often scattered across multiple systems, applications, and formats. This ensures they are working with the most relevant and up-to-date information. Furthermore, they need access to all new information, such as product updates, evolving customer requirements, or changes in business processes, ensuring that their outputs remain relevant and accurate.

For instance, an AI agent assisting a sales team must have access to CRM data, historical conversations, pricing details, and product catalogs to provide actionable insights during a customer interaction.

Strategic Action

AI agents’ value lies not only in their ability to comprehend but also to act. For instance, AI agents can perform activities such as updating CRM records after a sales call, generating invoices, or creating tasks in project management tools based on user input or triggers. Similarly, AI agents can initiate complex workflows, such as escalating support tickets, scheduling appointments, or launching marketing campaigns. However, this requires seamless connectivity across different applications to facilitate action. 

For example, an AI agent managing customer support could resolve queries by pulling answers from a knowledge base and, if necessary, escalating unresolved issues to a human representative with full context.

The capabilities of AI agents are undeniably remarkable. However, their true potential can only be realized when they seamlessly access contextual knowledge and take informed actions across a wide array of applications. This is where integrations play a pivotal role, serving as the key to bridging gaps and unlocking the full power of AI agents.

Enter Integrations: Powering AI Agents

The effectiveness of an AI agent is directly tied to its ability to access and utilize data stored across diverse platforms. This is where integrations shine, acting as conduits that connect the AI agent to the wealth of information scattered across different systems. These data sources fall into several broad categories, each contributing uniquely to the agent's capabilities:

Access to Data Across Platforms: The Foundation of AI Agent Functionality

Structured Data Sources

Platforms like databases, Customer Relationship Management (CRM) systems (e.g., Salesforce, HubSpot), and Enterprise Resource Planning (ERP) tools house structured data—clean, organized, and easily queryable. For example, CRM integrations allow AI agents to retrieve customer contact details, sales pipelines, and interaction histories, which they can use to personalize customer interactions or automate follow-ups.

Unstructured Data Sources

The majority of organizational knowledge exists in unstructured formats, such as PDFs, Word documents, emails, and collaborative platforms like Notion or Confluence. Cloud storage systems like Google Drive and Dropbox add another layer of complexity, storing files without predefined schemas. Integrating with these systems allows AI agents to extract key insights from meeting notes, onboarding manuals, or research reports. For instance, an AI assistant integrated with Google Drive could retrieve and summarize a company’s annual performance review stored in a PDF document.

Streaming Data Sources

Real-time data streams from IoT devices, analytics tools, or social media platforms offer actionable insights that are constantly updated. AI agents integrated with streaming data sources can monitor metrics, such as energy usage from IoT sensors or engagement rates from Twitter analytics, and make recommendations or trigger actions based on live updates.

Third-Party Applications

APIs from third-party services like payment gateways (Stripe, PayPal), logistics platforms (DHL, FedEx), and HR systems (BambooHR, Workday) expand the agent's ability to act across verticals. For example, an AI agent integrated with a payment gateway could automatically reconcile invoices, track payments, and even issue alerts for overdue accounts.

The Role of Data Ingestion 

To process this vast array of data, AI agents rely on data ingestion—the process of collecting, aggregating, and transforming raw data into a usable format. Data ingestion pipelines ensure that the agent has access to a broad and rich understanding of the information landscape, enhancing its ability to make accurate decisions.

However, this capability requires robust integrations with a wide variety of third-party applications. Whether it's CRM systems, analytics tools, or knowledge repositories, each integration provides an additional layer of context that the agent can leverage.

Without these integrations, AI agents would be confined to static or siloed information, limiting their ability to adapt to dynamic environments. For example, an AI-powered customer service bot lacking integration with an order management system might struggle to provide real-time updates on a customer’s order status, resulting in a frustrating user experience.

The Case for Real-Time Integrations

In many applications, the true value of AI agents lies in their ability to respond with real-time or near-real-time accuracy. Integrations with webhooks and streaming APIs enable the agent to access live data updates, ensuring that its responses remain relevant and timely.

Consider a scenario where an AI-powered invoicing assistant is tasked with generating invoices based on software usage. If the agent relies on a delayed data sync, it might fail to account for a client’s excess usage in the final moments before the invoice is generated. This oversight could result in inaccurate billing, financial discrepancies, and strained customer relationships.

Empowering Action Across Applications

Integrations are not merely a way to access data for AI agents; they are critical to enabling these agents to take meaningful actions on behalf of other applications. This capability is what transforms AI agents from passive data collectors into active participants in business processes. 

Integrations play a crucial role in this process by connecting AI agents with different applications, enabling them to interact seamlessly and perform tasks on behalf of the user to trigger responses, updates, or actions in real time. 

For instance, A customer service AI agent integrated with CRM platforms can automatically update customer records, initiate follow-up emails, and even generate reports based on the latest customer interactions. SImilarly, if a popular product is running low, the AI agent for e-commerce platform can automatically reorder from the supplier, update the website’s product page with new availability dates, and notify customers about upcoming restocks. Furthermore, A marketing AI agent integrated with CRM and marketing automation platforms (e.g., Mailchimp, ActiveCampaign) can automate email campaigns based on customer behaviors—such as opening specific emails, clicking on links, or making purchases. 

Integrations allow AI agents to automate processes that span across different systems. For example, an AI agent integrated with a project management tool and a communication platform can automate task assignments based on project milestones, notify team members of updates, and adjust timelines based on real-time data from work management systems.

For developers driving these integrations, it’s essential to build robust APIs and use standardized protocols like OAuth for secure data access across each of the applications in use. They should also focus on real-time synchronization to ensure the AI agent acts on the most current data available. Proper error handling, logging, and monitoring mechanisms are critical to maintaining reliability and performance across integrations. Furthermore, as AI agents often interact with multiple platforms, developers should design integration solutions that can scale. This involves using scalable data storage solutions, optimizing data flow, and regularly testing integration performance under load.

Leveraging RAG (Retrieval-Augmented Generation) for AI Agents

Retrieval-Augmented Generation (RAG) is a transformative approach that enhances the capabilities of AI agents by addressing a fundamental limitation of generative AI models: reliance on static, pre-trained knowledge. RAG fills this gap by providing a way for AI agents to efficiently access, interpret, and utilize information from a variety of data sources. Here’s how RAG enhances integration for AI agents:

Access to Diverse Data Sources

Traditional APIs are optimized for structured data (like databases, CRMs, and spreadsheets). However, many of the most valuable insights for AI agents come from unstructured data—documents (PDFs), emails, chats, meeting notes, Notion, and more. Unstructured data often contains detailed, nuanced information that is not easily captured in structured formats.

RAG enables AI agents to access and leverage this wealth of unstructured data by integrating it into their decision-making processes. By integrating with these unstructured data sources, AI agents:

• Employ Optical Character Recognition (OCR) for scanned documents and Natural Language Processing (NLP) for text extraction.
• Use NLP techniques like keyword extraction, named entity recognition, sentiment analysis, and topic modeling to parse and structure the information.
• Convert text into vector embeddings using models such as Word2Vec, GloVe, and BERT, which represent words and phrases as numerical vectors capturing semantic relationships between them.
• Use similarity metrics (e.g., cosine similarity) to find relevant patterns and relationships between different pieces of data, allowing the AI agent to understand context even when information is fragmented or loosely connected.

Unified Retrieval Layer

RAG involves not only the retrieval of relevant data from these sources but also the generation of responses based on this data. It allows AI agents to pull in information from different platforms, consolidate it, and generate responses that are contextually relevant. 

For instance, an HR AI agent might need to pull data from employee records, performance reviews, and onboarding documents to answer a question about benefits. RAG enables this agent to access the necessary context and background information from multiple sources, ensuring the response is accurate and comprehensive  through a single retrieval mechanism.

Real-Time Contextual Understanding

RAG empowers AI agents by providing real-time access to updated information from across various platforms with the help of Webhooks. This is critical for applications like customer service, where responses must be based on the latest data. 

For example, if a customer asks about their recent order status, the AI agent can access real-time shipping data from a logistics platform, order history from an e-commerce system, and promotional notes from a marketing database—enabling it to provide a response with the latest information. Without RAG, the agent might only be able to provide a generic answer based on static data, leading to inaccuracies and customer frustration.

Key Benefits of RAG for AI Agents

1. Enhanced Accuracy
   By incorporating real-time information retrieval, RAG reduces the risk of hallucinations—a common issue with LLMs—ensuring responses are accurate and grounded in authoritative data sources.
2. Scalability Across Domains and Use Cases
   With access to external knowledge repositories, AI agents equipped with RAG can seamlessly adapt to various industries, such as healthcare, finance, education, and e-commerce.
3. Improved User Experience
   RAG-powered agents offer detailed, context-aware, and dynamic responses, elevating user satisfaction in applications like customer support, virtual assistants, and education platforms.
4. Cost Efficiency
   By offloading the need to encode every piece of knowledge into the model itself, RAG allows smaller LLMs to perform at near-human accuracy levels, reducing computational costs.
5. Future-Proofing AI Systems
   Continuous learning becomes effortless as new information can be integrated into the retriever without retraining the generator, making RAG an adaptable solution in fast-evolving industries.

Challenges in Implementing RAG (Retrieval-Augmented Generation)

While RAG presents immense opportunities to enhance AI capabilities, its implementation comes with a set of challenges. Addressing these challenges is crucial to building efficient, scalable, and reliable AI systems.

1. Latency and Performance Bottlenecks: Real-time retrieval and generation involve multiple computational steps, including embedding queries, retrieving data, and generating responses. This can introduce delays, especially when handling large-scale queries or deploying RAG on low-powered devices.some text
   • Mitigation Strategies:some text
     • Approximate Nearest Neighbor (ANN) Search: Use ANN techniques in retrievers (e.g., FAISS or ScaNN) to speed up vector searches without sacrificing too much accuracy.
     • Caching Frequent Queries: Cache the most common retrieval results to bypass the retriever for repetitive queries.
     • Parallel Processing: Leverage parallelism in data retrieval and model inference to minimize bottlenecks.
     • Model Optimization: Use quantized or distilled models for faster inference during embedding generation or response synthesis.
2. Data Quality and Bias in Knowledge Bases: The quality and relevance of retrieved data heavily depend on the source knowledge base. If the data is outdated, incomplete, or biased, the generated responses will reflect those shortcomings.some text
   • Mitigation Strategies:some text
     • Regular Data Updates: Ensure the knowledge base is periodically refreshed with the latest and most accurate information.
     • Source Validation: Use reliable, vetted sources to build the knowledge base.
     • Bias Mitigation: Perform audits to identify and correct biases in the retriever’s dataset or the generator’s output.
     • Content Moderation: Implement filters to exclude low-quality or irrelevant data during the retrieval phase.
3. Scalability with Large Datasets: As datasets grow in size and complexity, retrieval becomes computationally expensive. Indexing, storage, and retrieval from large-scale knowledge bases require robust infrastructure.some text
   • Mitigation Strategies:some text
     • Hierarchical Retrieval: Use multi-stage retrievers where a lightweight model filters down the dataset before passing it to a heavier, more precise retriever.
     • Distributed Systems: Deploy distributed retrieval systems using frameworks like Elasticsearch clusters or AWS-managed services.
     • Efficient Indexing: Use optimized indexing techniques (e.g., HNSW) to handle large datasets efficiently.
4. Alignment Between Retrieval and Generation: RAG systems must align retrieved information with user intent to generate coherent and contextually relevant responses. Misalignment can lead to confusing or irrelevant outputs.some text
   • Mitigation Strategies:some text
     • Query Reformulation: Preprocess user queries to align them with the retriever’s capabilities, using NLP techniques like rephrasing or entity extraction.
     • Context-Aware Generation: Incorporate structured prompts that explicitly guide the generator to focus on the retrieved context.
     • Feedback Mechanisms: Enable end-users or moderators to flag poor responses, and use this feedback to fine-tune the retriever and generator.
5. Handling Ambiguity in Queries: Ambiguous user queries can lead to irrelevant or incomplete retrieval, resulting in suboptimal generated responses.some text
   • Mitigation Strategies:some text
     • Clarification Questions: Build mechanisms for the AI to ask follow-up questions when the user query lacks clarity.
     • Multi-Pass Retrieval: Retrieve multiple potentially relevant contexts and use the generator to combine and synthesize them.
     • Weighted Scoring: Assign higher relevance scores to retrieved documents that align more closely with query intent, using additional heuristics or context-based filters.
6. Integration Complexity: Seamlessly integrating retrieval systems with generative models requires significant engineering effort, especially when handling domain-specific requirements or legacy systems.some text
   • Mitigation Strategies:some text
     • Frameworks and Libraries: Use existing RAG frameworks like Haystack or LangChain to reduce development complexity.
     • API Abstraction: Wrap the retriever and generator in unified APIs to simplify the integration process.
     • Microservices Architecture: Deploy retriever and generator components as independent services, allowing for modular development and easier scaling.
7. Using Unreliable Sources: A Key Challenge in RAG Implementation**: The effectiveness of Retrieval-Augmented Generation (RAG) depends heavily on the quality of the knowledge base. If the system relies on unreliable, biased, or non-credible sources, it can lead to the generation of inaccurate, misleading, or harmful outputs. This undermines the reliability of the AI and can damage user trust, especially in high-stakes domains like healthcare, finance, or legal services.some text
   • Mitigation Strategies:some text
     • Source Vetting and Curation: Establish strict criteria for selecting sources, prioritizing those that are credible, authoritative, and up-to-date along with regular audit.
     • Trustworthiness Scoring: Assign trustworthiness scores to sources based on factors like citation frequency, domain expertise, and author credentials.
     • Multi-Source Validation: Cross-reference retrieved information against multiple trusted sources to ensure accuracy and consistency.

Steps to Build a RAG Pipeline

1. Define the Use Casesome text
   • Identify the specific application for your RAG pipeline, such as answering customer support queries, generating reports, or creating knowledge assistants for internal use.
2. Select Data Sourcessome text
   • Determine the types of data your pipeline will access, including structured (databases, APIs) and unstructured (documents, emails, knowledge bases).
3. Choose Tools and Technologiessome text
   • Vectorization Tools: Select pre-trained models for creating text embeddings.
   • Databases: Use a vector database to store and retrieve embeddings.
   • Generative Models: Choose a model optimized for your domain and use case.
4. Develop and Deploy Retrieval Modelssome text
   • Train retrieval models to handle semantic queries effectively. Focus on accuracy and relevance, balancing precision with speed.
5. Integrate Generative AIsome text
   • Connect the retrieval mechanism to the generative model. Ensure input prompts include the retrieved context for highly relevant outputs.
6. Implement Quality Assurancesome text
   • Regularly test the pipeline with varied inputs to evaluate accuracy, speed, and the relevance of responses.
   • Monitor for potential biases or inaccuracies and adjust models as needed.
7. Optimize and Scalesome text
   • Fine-tune the pipeline based on user feedback and performance metrics.
   • Scale the system to handle larger datasets or higher query volumes as needed.

Real-World Use Cases

AI-Powered Customer Support for an eCommerce Platform

Integration of an AI-powered customer service agent with CRM systems, ticketing platforms, and other tools can help enhance contextual knowledge and take proactive actions, delivering a superior customer experience. 

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For instance, when a customer reaches out with a query—such as a delayed order—the AI agent retrieves their profile from the CRM, including past interactions, order history, and loyalty status, to gain a comprehensive understanding of their background. Simultaneously, it queries the ticketing system to identify any related past or ongoing issues and checks the order management system for real-time updates on the order status. Combining this data, the AI develops a holistic view of the situation and crafts a personalized response. It may empathize with the customer’s frustration, offer an estimated delivery timeline, provide goodwill gestures like loyalty points or discounts, and prioritize the order for expedited delivery.

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The AI agent also performs critical backend tasks to maintain consistency across systems. It logs the interaction details in the CRM, updating the customer’s profile with notes on the resolution and any loyalty rewards granted. The ticketing system is updated with a resolution summary, relevant tags, and any necessary escalation details. Simultaneously, the order management system reflects the updated delivery status, and insights from the resolution are fed into the knowledge base to improve responses to similar queries in the future. Furthermore, the AI captures performance metrics, such as resolution times and sentiment analysis, which are pushed into analytics tools for tracking and reporting.

Retail AI Agent with Omni-Channel Integration

In retail, AI agents can integrate with inventory management systems, customer loyalty platforms, and marketing automation tools for enhancing customer experience and operational efficiency. For instance, when a customer purchases a product online, the AI agent quickly retrieves data from the inventory management system to check stock levels. It can then update the order status in real time, ensuring that the customer is informed about the availability and expected delivery date of the product. If the product is out of stock, the AI agent can suggest alternatives that are similar in features, quality, or price, or provide an estimated restocking date to prevent customer frustration and offer a solution that meets their needs. 

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Similarly, if a customer frequently purchases similar items, the AI might note this and suggest additional products or promotions related to these interests in future communications. By integrating with marketing automation tools, the AI agent can personalize marketing campaigns, sending targeted emails, SMS messages, or notifications with relevant offers, discounts, or recommendations based on the customer’s previous interactions and buying behaviors. The AI agent also writes back data to customer profiles within the CRM system. It logs details such as purchase history, preferences, and behavioral insights, allowing retailers to gain a deeper understanding of their customers’ shopping patterns and preferences. 

Key challenges with integrations for AI agents

Integrating AI (Artificial Intelligence) and RAG (Recommendations, Actions, and Goals) frameworks into existing systems is crucial for leveraging their full potential, but it introduces significant technical challenges that organizations must navigate. These challenges span across data ingestion, system compatibility, and scalability, often requiring specialized technical solutions and ongoing management to ensure successful implementation.

Data Compatibility and Quality:

• Data Fragmentation: Many organizations operate in data-rich but siloed environments, where critical information is scattered across multiple tools and platforms. For instance, customer data may reside in CRMs, operational data in ERP systems, and communication data in collaboration tools like Slack or Google Drive. These systems often store data in incompatible formats, making it difficult to consolidate into a single, accessible source. This fragmentation obstructs AI's ability to deliver actionable insights by limiting its access to the complete context required for accurate recommendations and decisions. Overcoming this challenge is particularly difficult in organizations with legacy systems or highly customized architectures.
• Data Quality Issues: AI systems rely heavily on data accuracy, completeness, and consistency. Common issues such as duplicate records, missing fields, or outdated entries can severely undermine the performance of AI models. Inconsistent data formatting, such as differences in date structures, naming conventions, or measurement units across systems, can lead to misinterpretation of information by AI agents. Low-quality data not only reduces the effectiveness of AI but also erodes stakeholder confidence in the system's outputs, creating a cycle of distrust and underutilization.

Complexity of Integration:

• System Compatibility: Integrating AI frameworks with existing platforms is often hindered by discrepancies in system architecture, API protocols, and data exchange standards. Enterprise systems such as CRMs, ERPs, and proprietary databases are frequently designed without interoperability in mind. These compatibility issues necessitate custom integration solutions, which can be time-consuming and resource-intensive. Additionally, the lack of standardization across APIs complicates the development process, increasing the risk of integration failures or inconsistent data flow.
• Real-Time Integration: Real-time functionality is critical for AI systems that generate recommendations or perform actions dynamically. However, achieving this is particularly challenging when dealing with high-frequency data streams, such as those from IoT devices, e-commerce platforms, or customer-facing applications. Low-latency requirements demand advanced data synchronization capabilities to ensure that updates are processed and reflected instantaneously across all systems. Infrastructure limitations, such as insufficient bandwidth or outdated hardware, further exacerbate this challenge, leading to performance degradation or delayed responses.

Scalability Issues:

• High Volume or Large Data Ingestion: AI integrations often require processing enormous volumes of data generated from diverse sources. These include transactional data from e-commerce platforms, behavioral data from user interactions, and operational data from business systems. Managing these data flows requires robust infrastructure capable of handling high throughput while maintaining data accuracy and integrity. The dynamic nature of data sources, with fluctuating volumes during peak usage periods, further complicates scalability, as systems must be designed to handle both expected and unexpected surges.
• Third-Party Limitations and Data Loss: Many third-party systems impose rate limits on API calls, which can restrict the volume of data an AI system can access or process within a given timeframe. These limitations often lead to incomplete data ingestion or delays in synchronization, impacting the overall reliability of AI outputs. Additional risks, such as temporary outages or service disruptions from third-party providers, can result in critical data being lost or delayed, creating downstream effects on AI performance.

Building AI Actions for Automation:

• API Research and Management: AI integrations require seamless interaction with third-party applications through APIs, which involves extensive research into their specifications, capabilities, and constraints. Organizations must navigate a wide variety of authentication protocols, such as OAuth 2.0 or API key-based systems, which can vary significantly in complexity and implementation requirements. Furthermore, APIs are subject to frequent updates or deprecations, which may lead to breaking changes that disrupt existing integrations and necessitate ongoing monitoring and adaptation.
• Cost of Engineering Hours: Developing and maintaining AI integrations demands significant investment in engineering resources. This includes designing custom solutions, monitoring system performance, and troubleshooting issues arising from API changes or infrastructure bottlenecks. The long-term costs of managing these integrations can escalate as the complexity of the system grows, placing a strain on both technical teams and budgets. This challenge is especially pronounced in smaller organizations with limited technical expertise or resources to dedicate to such efforts.

Monitoring and Observability Gaps

• Lack of Unified Dashboards: Organizations often use disparate monitoring tools that focus on specific components, such as data pipelines, model health, or API integrations. However, these tools rarely offer a comprehensive view of the overall system performance. This fragmented approach creates blind spots, making it challenging to identify interdependencies or trace the root causes of failures and inefficiencies. The absence of a single pane of glass for monitoring hinders decision-making and proactive troubleshooting.
• Failure Detection: AI systems and their integrations are susceptible to several issues, such as dropped API calls, broken data pipelines, and data inconsistencies. These problems, if undetected, can escalate into critical disruptions. Without robust failure detection mechanisms—like anomaly detection, alerting systems, and automated diagnostics—such issues can remain unnoticed until they significantly impact operations, leading to downtime, loss of trust, or financial setbacks.

Versioning and Compatibility Drift

• API Deprecations: Third-party providers frequently update or discontinue APIs, creating potential compatibility issues for existing integrations. For example, a CRM platform might revise its API authentication protocols, making current integration setups obsolete unless they are swiftly updated. Failure to monitor and adapt to such changes can lead to disrupted workflows, data loss, or security vulnerabilities.
• Model Updates: AI models require periodic retraining and updates to improve performance, adapt to new data, or address emerging challenges. However, these updates can unintentionally introduce changes in outputs, workflows, or integration points. If not thoroughly tested and managed, such changes can disrupt established business processes, leading to inconsistencies or operational delays. Effective version control and compatibility testing are critical to mitigate these risks.

How to Add Integrations to AI Agents?

Adding integrations to AI agents involves providing these agents with the ability to seamlessly connect with external systems, APIs, or services, allowing them to access, exchange, and act on data. Here are the top ways to achieve the same:

Custom Development Approach

Custom development involves creating tailored integrations from scratch to connect the AI agent with various external systems. This method requires in-depth knowledge of APIs, data models, and custom logic. The process involves developing specific integrations to meet unique business requirements, ensuring complete control over data flows, transformations, and error handling. This approach is suitable for complex use cases where pre-built solutions may not suffice.

Pros:

• Highly Tailored Solutions: Custom development allows for precise control over the integration process, enabling specific adjustments to meet unique business requirements.
• Full Control: Organizations can implement specific data validation rules, security protocols, and transformations that best suit their needs.
• Complex Use Cases: Custom development is ideal for complex integrations involving multiple systems or detailed workflows that existing platforms cannot support.

Cons:

• Resource-Intensive: Building and maintaining custom integrations requires specialized skills in software development, APIs, and data integration.
• Time Consuming: Development can take weeks to months, depending on the complexity of the integration.
• Maintenance: Ongoing maintenance is required to adapt the integration to changes in APIs, business needs, or system upgrades.

Embedded iPaaS Approach

Embedded iPaaS (Integration Platform as a Service) solutions offer pre-built integration platforms that include no-code or low-code tools. These platforms allow organizations to quickly and easily set up integrations between the AI agent and various external systems without needing deep technical expertise. The integration process is simplified by using a graphical interface to configure workflows and data mappings, reducing development time and resource requirements.

Pros:

• Quick Deployment: Rapid implementation thanks to the use of visual interfaces and pre-built connectors, enabling organizations to integrate systems quickly.
• Scalability: Easy to adjust and scale as business requirements evolve, ensuring flexibility over time.
• Reduced Costs: Lower upfront costs and less need for specialized development teams compared to custom development.

Cons:

• Limited Customization: Some iPaaS solutions may not offer enough customization for complex or highly specific integration needs.
• Platform Dependency: Integration capabilities are restricted by the APIs and features provided by the chosen iPaaS platform.
• Recurring Fees: Subscription costs can accumulate over time, making this approach more expensive for long-term use.

Unified API Solutions (e.g., Knit) 

Unified API solutions provide a single API endpoint that connects to multiple SaaS products and external systems, simplifying the integration process. This method abstracts the complexity of dealing with multiple APIs by consolidating them into a unified interface. It allows the AI agent to access a wide range of services, such as CRM systems, marketing platforms, and data analytics tools, through a seamless and standardized integration process.

Pros:

• Speed: Quick deployment due to pre-built connectors and automated setup processes.
• 100% API Coverage: Access to a wide range of integrations with minimal setup, reducing the complexity of managing multiple API connections.
• Ease of Use: Simplifies integration management through a single API, reducing overhead and maintenance needs.

How Knit AI Can Power Integrations for AI Agents

Knit offers a game-changing solution for organizations looking to integrate their AI agents with a wide variety of SaaS applications quickly and efficiently. By providing a seamless, AI-driven integration process, Knit empowers businesses to unlock the full potential of their AI agents by connecting them with the necessary tools and data sources.

• Rapid Integration Deployment: Knit AI allows AI agents to deploy dozens of product integrations within minutes. This speed is achieved through a user-friendly interface where users can select the applications they wish to integrate with. If an application isn’t supported yet, Knit AI will add it within just 2 days. This ensures businesses can quickly adapt to new tools and services without waiting for extended development cycles.
• 100% API Coverage: With Knit AI, AI agents can access a wide range of APIs from various platforms and services through a unified API. This means that whether you’re integrating with CRM systems, marketing platforms, or custom-built applications, Knit provides complete API coverage. The AI agent can interact with these systems as if they were part of a single ecosystem, streamlining data access and management.
• Custom Integration Options: Users can specify their needs—whether they want to read or write data, which data fields they need, and whether they require scheduled syncs or real-time API calls. Knit AI then builds connectors tailored to these specifications, allowing for precise control over data flows and system interactions. This customization ensures that the AI agent can perform exactly as required in real-time environments.
• Testing and Validation: Before going live, users can test their integrations using Knit’s available sandboxes. These sandboxes allow for a safe environment to verify that the integration works as expected, handling edge cases and ensuring data integrity. This process minimizes the risk of errors and ensures that the integration performs optimally once it’s live.
• Publish with Confidence: Once tested and validated, the integration can be published with a single click. Knit simplifies the deployment process, enabling businesses to go from development to live integration in minutes. This approach significantly reduces the friction typically associated with traditional integration methods, allowing organizations to focus on leveraging their AI capabilities without technical barriers.

By integrating with Knit, organizations can power their AI agents to interact seamlessly with a wide array of applications. This capability not only enhances productivity and operational efficiency but also allows for the creation of innovative use cases that would be difficult to achieve with manual integration processes. Knit thus transforms how businesses utilize AI agents, making it easier to harness the full power of their data across multiple platforms.

Ready to see how Knit can transform your AI agents? Contact us today for a personalized demo!

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Microsoft Dynamics CRM is a comprehensive customer relationship management solution that helps businesses manage sales, customer service, and marketing activities. Part of the Microsoft Dynamics 365 suite, it offers tools for automating workflows, tracking customer interactions, and gaining actionable insights to drive growth.

Microsoft Dynamics CRM APIs provide developers with powerful tools to integrate and extend CRM functionalities. These APIs support operations like managing accounts, contacts, leads, and opportunities, as well as customizing workflows and accessing analytics. With RESTful endpoints, secure authentication via OAuth 2.0, and robust documentation, they enable seamless integration with other applications and services.

Key Highlights of Microsoft Dynamics CRM APIs are as follows:

1. Comprehensive Access: Manage entities like accounts, contacts, leads, and opportunities.
2. RESTful Design: Easy-to-use APIs with standardized HTTP methods.
3. Secure Authentication: Supports OAuth 2.0 for secure access.
4. Customizable Workflows: Enable custom processes and automations.
5. Integration Flexibility: Seamlessly connect with other Microsoft services and third-party applications.
6. Advanced Querying: Utilize OData queries for filtering, sorting, and retrieving data.
7. Real-Time Operations: Access and update CRM data instantly.
8. Rich Documentation: Comprehensive guides and resources for developers.
9. Scalable: Suitable for small-scale and enterprise-level applications.
10. Insights and Analytics: Retrieve reports and business intelligence for data-driven decisions.

This article gives an overview of the most commonly used Microsoft Dynamics CRM API endpoints.

Microsoft Dynamics CRM API Endpoints

General Entity Management

• GET /api/data/v9.0/accounts
• POST /api/data/v9.0/accounts
• PATCH /api/data/v9.0/accounts({account_id})
• DELETE /api/data/v9.0/accounts({account_id})
• GET /api/data/v9.0/contacts
• POST /api/data/v9.0/contacts
• PATCH /api/data/v9.0/contacts({contact_id})
• DELETE /api/data/v9.0/contacts({contact_id})
• GET /api/data/v9.0/leads
• POST /api/data/v9.0/leads
• PATCH /api/data/v9.0/leads({lead_id})
• DELETE /api/data/v9.0/leads({lead_id})
• GET /api/data/v9.0/opportunities
• POST /api/data/v9.0/opportunities
• PATCH /api/data/v9.0/opportunities({opportunity_id})
• DELETE /api/data/v9.0/opportunities({opportunity_id})
• GET /api/data/v9.0/tasks
• POST /api/data/v9.0/tasks
• PATCH /api/data/v9.0/tasks({task_id})
• DELETE /api/data/v9.0/tasks({task_id})
  
  

Activities

• GET /api/data/v9.0/activities
• POST /api/data/v9.0/activities
• PATCH /api/data/v9.0/activities({activity_id})
• DELETE /api/data/v9.0/activities({activity_id})

System Users

• GET /api/data/v9.0/systemusers
• GET /api/data/v9.0/systemusers({user_id})

Metadata and Custom Entities

• GET /api/data/v9.0/EntityDefinitions
• GET /api/data/v9.0/EntityDefinitions({entity_name})
• GET /api/data/v9.0/{custom_entity_name}
• POST /api/data/v9.0/{custom_entity_name}
• PATCH /api/data/v9.0/{custom_entity_name}({record_id})
• DELETE /api/data/v9.0/{custom_entity_name}({record_id})

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Metadata Management

• GET /api/data/v9.0/GlobalOptionSetDefinitions
• GET /api/data/v9.0/GlobalOptionSetDefinitions({optionset_id})
• GET /api/data/v9.0/AttributeMetadata
• GET /api/data/v9.0/EntityDefinitions({entity_name})/Attributes

Solutions and Customization

• GET /api/data/v9.0/Solutions
• GET /api/data/v9.0/Solutions({solution_id})
• GET /api/data/v9.0/PublishAllXml

Workflow Management

• GET /api/data/v9.0/workflows
• GET /api/data/v9.0/workflows({workflow_id})
• POST /api/data/v9.0/workflows({workflow_id})/Execute

Teams and Security Roles

• GET /api/data/v9.0/teams
• GET /api/data/v9.0/teams({team_id})
• GET /api/data/v9.0/securityroles
• GET /api/data/v9.0/securityroles({role_id})

Business Units

• GET /api/data/v9.0/businessunits
• GET /api/data/v9.0/businessunits({businessunit_id})

Audit Logs

• GET /api/data/v9.0/audits
• GET /api/data/v9.0/audits({audit_id})

Marketing Lists

• GET /api/data/v9.0/lists
• POST /api/data/v9.0/lists
• PATCH /api/data/v9.0/lists({list_id})
• DELETE /api/data/v9.0/lists({list_id})

Email and Communication

• GET /api/data/v9.0/emails
• POST /api/data/v9.0/emails
• PATCH /api/data/v9.0/emails({email_id})
• POST /api/data/v9.0/emails({email_id})/send

Cases and Service Management

• GET /api/data/v9.0/incidents
• POST /api/data/v9.0/incidents
• PATCH /api/data/v9.0/incidents({incident_id})
• DELETE /api/data/v9.0/incidents({incident_id})

Scheduling and Resources

• GET /api/data/v9.0/resources
• GET /api/data/v9.0/calendars
• GET /api/data/v9.0/resourcegroups

Reports and Dashboards

• GET /api/data/v9.0/reports
• GET /api/data/v9.0/reports({report_id})
• GET /api/data/v9.0/dashboards
• GET /api/data/v9.0/dashboards({dashboard_id})

Additional Miscellaneous Endpoints

• GET /api/data/v9.0/roles
• GET /api/data/v9.0/queues
• GET /api/data/v9.0/queueitems
• POST /api/data/v9.0/merge
• GET /api/data/v9.0/settings
• GET /api/data/v9.0/WhoAmI
• GET /api/data/v9.0/RetrieveDuplicates
• GET /api/data/v9.0/ExecuteFunction
• POST /api/data/v9.0/ExecuteAction

Here’s a detailed reference to all the MS Dynamics CRM API Endpoints.

MS Dynamics CRM API FAQs‍

Here are the frequently asked questions about MS Dynamics CRM APIs to help you get started:

1. How to retrieve more than 5000 records in Dynamics CRM using Web API? Answer
2. What is the attachment size limit for Dynamics CRM 365? Answer
3. What is the maximum number of users for Dynamics 365? Answer
4. What is the maximum length of multiple lines of text in CRM? Answer
5. What is the field limit in Dynamics 365? Answer‍

Find more FAQs here.

Get started with MS Dynamics CRM API‍

To access Microsoft Dynamics CRM APIs, register an application in Azure AD, configure API permissions, generate a client secret, authenticate using OAuth 2.0 to obtain an access token, and use the token to interact with the API endpoints.

However, if you want to integrate with multiple CRM APIs quickly along with MS Dynamics API, you can get started with Knit, one API for all top CRM integrations.

To sign up for free, click here. To check the pricing, see our pricing page.

NetSuite is a leading cloud-based Enterprise Resource Planning (ERP) platform that helps businesses manage finance, operations, customer relationships, and more from a unified system. Its robust suite of applications streamlines workflows automates processes and provides real-time data insights. 

To extend its functionality, NetSuite offers a comprehensive set of APIs that enable seamless integration with third-party applications, custom automation, and data synchronization. 

This article explores the NetSuite APIs, outlining the key APIs available, their use cases, and how they can enhance business operations.

‍Key Highlights of NetSuite APIs

The key highlights of NetSuite APIs are as follows:

1. SuiteTalk (SOAP & REST) – Provides programmatic access to NetSuite data and functionality for seamless integration with external applications. Supports both SOAP and REST web services.
2. SuiteScript – A JavaScript-based API that enables custom business logic and automation within NetSuite, including workflows, user event scripts, and scheduled scripts.
3. REST Web Services – A modern, lightweight API with JSON-based data exchange, ideal for real-time integrations and improved performance over SOAP.
4. SOAP Web Services – A robust API for complex integrations, offering structured XML-based communication and extensive support for NetSuite's data model.
5. SuiteAnalytics Connect – Enables direct access to NetSuite data via ODBC, JDBC, and ADO.NET for advanced reporting, analytics, and external BI tool integration.
6. Token-Based Authentication (TBA) – Enhances security and scalability by allowing API access without storing user credentials using OAuth-style token authentication.
7. OData Support—Integrates with business intelligence tools that support the OData protocol to facilitate easy data extraction for reporting and analytics.

These APIs empower developers to build custom solutions, automate workflows, and integrate NetSuite with external platforms, enhancing operational efficiency and business intelligence.

This article gives an overview of the most commonly used NetSuite API endpoints.

NetSuite API Endpoints

Here are the most commonly used NetSuite API endpoints:

‍Accounts‍

• GET /account
• POST /account
• DELETE /account/{id}
• GET /account/{id}
• PATCH /account/{id}
• PUT /account/{id}

Accounting Book

• GET /accountingBook
• POST /accountingBook
• DELETE /accountingBook/{id}
• GET /accountingBook/{id}
• PATCH /accountingBook/{id}
• PUT /accountingBook/{id}

Customers

• GET /customer
• POST /customer
• DELETE /customer/{id}
• GET /customer/{id}
• PATCH /customer/{id}
• PUT /customer/{id}

Vendors

• GET /vendor
• POST /vendor
• DELETE /vendor/{id}
• GET /vendor/{id}
• PATCH /vendor/{id}
• PUT /vendor/{id}

Transactions

• GET /transaction
• POST /transaction
• DELETE /transaction/{id}
• GET /transaction/{id}
• PATCH /transaction/{id}
• PUT /transaction/{id}

Items

• GET /item
• POST /item
• DELETE /item/{id}
• GET /item/{id}
• PATCH /item/{id}
• PUT /item/{id}

Employees

• GET /employee
• POST /employee
• DELETE /employee/{id}
• GET /employee/{id}
• PATCH /employee/{id}
• PUT /employee/{id}

Sales Orders

• GET /salesOrder
• POST /salesOrder
• DELETE /salesOrder/{id}
• GET /salesOrder/{id}
• PATCH /salesOrder/{id}
• PUT /salesOrder/{id}

Purchase Orders

• GET /purchaseOrder
• POST /purchaseOrder
• DELETE /purchaseOrder/{id}
• GET /purchaseOrder/{id}
• PATCH /purchaseOrder/{id}
• PUT /purchaseOrder/{id}

Invoices

• GET /invoice
• POST /invoice
• DELETE /invoice/{id}
• GET /invoice/{id}
• PATCH /invoice/{id}
• PUT /invoice/{id}

Payments

• GET /payment
• POST /payment
• DELETE /payment/{id}
• GET /payment/{id}
• PATCH /payment/{id}
• PUT /payment/{id}

Departments

• GET /department
• POST /department
• DELETE /department/{id}
• GET /department/{id}
• PATCH /department/{id}
• PUT /department/{id}

Locations

• GET /location
• POST /location
• DELETE /location/{id}
• GET /location/{id}
• PATCH /location/{id}
• PUT /location/{id}

Classes

• GET /classification
• POST /classification
• DELETE /classification/{id}
• GET /classification/{id}
• PATCH /classification/{id}
• PUT /classification/{id}

Currencies

• GET /currency
• POST /currency
• DELETE /currency/{id}
• GET /currency/{id}
• PATCH /currency/{id}
• PUT /currency/{id}

Tax Codes

• GET /taxCode
• POST /taxCode
• DELETE /taxCode/{id}
• GET /taxCode/{id}
• PATCH /taxCode/{id}
• PUT /taxCode/{id}

Subsidiaries

• GET /subsidiary
• POST /subsidiary
• DELETE /subsidiary/{id}
• GET /subsidiary/{id}
• PATCH /subsidiary/{id}
• PUT /subsidiary/{id}

Budget

• GET /budget
• POST /budget
• DELETE /budget/{id}
• GET /budget/{id}
• PATCH /budget/{id}
• PUT /budget/{id}

Expense Reports

• GET /expenseReport
• POST /expenseReport
• DELETE /expenseReport/{id}
• GET /expenseReport/{id}
• PATCH /expenseReport/{id}
• PUT /expenseReport/{id}

Time Entries

• GET /timeEntry
• POST /timeEntry
• DELETE /timeEntry/{id}
• GET /timeEntry/{id}
• PATCH /timeEntry/{id}
• PUT /timeEntry/{id}

Projects

• GET /project
• POST /project
• DELETE /project/{id}
• GET /project/{id}
• PATCH /project/{id}
• PUT /project/{id}

Work Orders

• GET /workOrder
• POST /workOrder
• DELETE /workOrder/{id}
• GET /workOrder/{id}
• PATCH /workOrder/{id}
• PUT /workOrder/{id}

Here’s a detailed reference to all the NetSuite API Endpoints.

NetSuite API FAQs‍

Here are the frequently asked questions about NetSuite APIs to help you get started:

1. What is the API limit for NetSuite? Answer
2. How do I connect to Oracle Netsuite using OAuth 2.0 Client Credentials Flow and JWT certificate with .net core? Answer
3. How to generate JWT Bearer Flow OAuth access tokens from a .net core client? Answer
4. How do create Vendor Bill in NetSuite using REST API? Answer
5. Was the ability to add Communication (Notes) to NetSuite invoices via REST API removed? Answer‍

Find more FAQs here.

Get started with NetSuite API‍

To access NetSuite APIs, enable API access in NetSuite, create an integration record to obtain consumer credentials, configure token-based authentication (TBA) or OAuth 2.0, generate access tokens, and use them to authenticate requests to NetSuite API endpoints.

However, if you want to integrate with multiple CRM, Accounting or ERP APIs quickly, you can get started with Knit, one API for all top integrations.

To sign up for free, click here. To check the pricing, see our pricing page.

‍

Salesforce is a leading cloud-based platform that revolutionizes how businesses manage relationships with their customers. It offers a suite of tools for customer relationship management (CRM), enabling companies to streamline sales, marketing, customer service, and analytics. 

With its robust scalability and customizable solutions, Salesforce empowers organizations of all sizes to enhance customer interactions, improve productivity, and drive growth. 

Salesforce also provides APIs to enable seamless integration with its platform, allowing developers to access and manage data, automate processes, and extend functionality. These APIs, including REST, SOAP, Bulk, and Streaming APIs, support various use cases such as data synchronization, real-time updates, and custom application development, making Salesforce highly adaptable to diverse business needs.

Key highlights of Salesforce APIs are as follows:

1. Versatile Options: Supports REST, SOAP, Bulk, and Streaming APIs for various use cases.
2. Scalability: Handles large data volumes with the Bulk API.
3. Real-time Updates: Enables event-driven workflows with the Streaming API.
4. Ease of Integration: Simplifies integration with external systems using REST and SOAP APIs.
5. Custom Development: Offers Apex APIs for tailored solutions.
6. Secure Access: Ensures data protection with OAuth 2.0.

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This article will provide an overview of the SalesForce API endpoints. These endpoints enable businesses to build custom solutions, automate workflows, and streamline customer operations. For an in-depth guide on building Salesforce API integrations, visit our Salesforce Integration Guide (In-Depth)

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‍SalesForce API Endpoints‍

Here are the most commonly used API endpoints in the latest REST API version (Version 62.0) -

Authentication

• /services/oauth2/token

Data Access

• /services/data/v62.0/sobjects/
• /services/data/v62.0/query/
• /services/data/v62.0/queryAll/

Search

• /services/data/v62.0/search/
• /services/data/v62.0/parameterizedSearch/

Chatter

• /services/data/v62.0/chatter/feeds/
• /services/data/v62.0/chatter/users/
• /services/data/v62.0/chatter/groups/

Metadata and Tooling

• /services/data/v62.0/tooling/
• /services/data/v62.0/metadata/

Analytics

• /services/data/v62.0/analytics/reports/
• /services/data/v62.0/analytics/dashboards/

Composite Resources

• /services/data/v62.0/composite/
• /services/data/v62.0/composite/batch/
• /services/data/v62.0/composite/tree/

Event Monitoring

• /services/data/v62.0/event/

Bulk API 2.0

• /services/data/v62.0/jobs/ingest/
• /services/data/v62.0/jobs/query/

Apex REST

• /services/apexrest/<custom_endpoint>

User and Profile Information

• /services/data/v62.0/sobjects/User/
• /services/data/v62.0/sobjects/Group/
• /services/data/v62.0/sobjects/PermissionSet/
• /services/data/v62.0/userInfo/
• /services/data/v62.0/sobjects/Profile/

Platform Events

• /services/data/v62.0/sobjects/<event_name>/
• /services/data/v62.0/sobjects/<event_name>/events/

Custom Metadata and Settings

• /services/data/v62.0/sobjects/CustomMetadata/
• /services/data/v62.0/sobjects/CustomObject/

External Services

• /services/data/v62.0/externalDataSources/
• /services/data/v62.0/externalObjects/

Process and Approvals

• /services/data/v62.0/sobjects/ProcessInstance/
• /services/data/v62.0/sobjects/ProcessInstanceWorkitem/
• /services/data/v62.0/sobjects/ApprovalProcess/

Files and Attachments

• /services/data/v62.0/sobjects/ContentVersion/
• /services/data/v62.0/sobjects/ContentDocument/

Custom Queries

• /services/data/v62.0/query/?q=<SOQL_query>
• /services/data/v62.0/queryAll/?q=<SOQL_query>

Batch and Composite APIs

• /services/data/v62.0/composite/batch/
• /services/data/v62.0/composite/tree/
• /services/data/v62.0/composite/sobjects/

Analytics (Reports and Dashboards)

• /services/data/v62.0/analytics/reports/
• /services/data/v62.0/analytics/dashboards/
• /services/data/v62.0/analytics/metrics/

Chatter (More Resources)

• /services/data/v62.0/chatter/topics/
• /services/data/v62.0/chatter/feeds/

Account and Contact Management

• /services/data/v62.0/sobjects/Account/
• /services/data/v62.0/sobjects/Contact/
• /services/data/v62.0/sobjects/Lead/
• /services/data/v62.0/sobjects/Opportunity/

Activity and Event Management

• /services/data/v62.0/sobjects/Event/
• /services/data/v62.0/sobjects/Task/
• /services/data/v62.0/sobjects/CalendarEvent/

Knowledge Management

• /services/data/v62.0/sobjects/KnowledgeArticle/
• /services/data/v62.0/sobjects/KnowledgeArticleVersion/
• /services/data/v62.0/sobjects/KnowledgeArticleType/

Custom Fields and Layouts

• /services/data/v62.0/sobjects/<object_name>/describe/
• /services/data/v62.0/sobjects/<object_name>/compactLayouts/
• /services/data/v62.0/sobjects/<object_name>/recordTypes/

Notifications

• /services/data/v62.0/notifications/
• /services/data/v62.0/notifications/v2/

Task and Assignment Management

• /services/data/v62.0/sobjects/Task/
• /services/data/v62.0/sobjects/Assignment/

Platform and Custom Objects

• /services/data/v62.0/sobjects/<custom_object_name>/
• /services/data/v62.0/sobjects/<custom_object_name>/fields/

Data Synchronization and External Services

• /services/data/v62.0/sobjects/ExternalDataSource/
• /services/data/v62.0/sobjects/ExternalObject/

AppExchange Resources

• /services/data/v62.0/appexchange/
• /services/data/v62.0/appexchange/packages/

Querying and Records

• /services/data/v62.0/sobjects/RecordType/
• /services/data/v62.0/sobjects/<object_name>/getUpdated/
• /services/data/v62.0/sobjects/<object_name>/getDeleted/

Security and Access Control

• /services/data/v62.0/sobjects/PermissionSetAssignment/
• /services/data/v62.0/sobjects/SharingRules/

Reports and Dashboards

• /services/data/v62.0/analytics/reports/
• /services/data/v62.0/analytics/dashboards/
• /services/data/v62.0/analytics/metricValues/

Data Import and Bulk Operations

• /services/data/v62.0/jobs/ingest/
• /services/data/v62.0/jobs/query/
• /services/data/v62.0/jobs/queryResults/

Content Management

• /services/data/v62.0/sobjects/ContentDocument/
• /services/data/v62.0/sobjects/ContentVersion/
• /services/data/v62.0/sobjects/ContentNote/

Platform Events

• /services/data/v62.0/sobjects/PlatformEvent/
• /services/data/v62.0/sobjects/PlatformEventNotification/

Task Management

• /services/data/v62.0/sobjects/Task/
• /services/data/v62.0/sobjects/Event/

Contract

• /services/data/v62.0/sobjects/Case/
• /services/data/v62.0/sobjects/Contract/
• /services/data/v62.0/sobjects/Quote/

Here’s a detailed reference to all the SalesForce API Endpoints.

SalesForce API FAQs‍

Here are the frequently asked questions about SalesForce APIs to help you get started:

‍

1. What are SalesForce API limits? Answer
2. What is the batch limit for Salesforce API? Answer
3. How many batches can run at a time in Salesforce? Answer
4. How do I see bulk API usage in Salesforce? Answer
5. Is Salesforce API limit inbound or outbound? Answer
6. How many types of API are there in Salesforce? Answer

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Find more FAQs here.

Get started with SalesForce API

To access Salesforce APIs, you need to create a Salesforce Developer account, generate an OAuth token, and obtain the necessary API credentials (Client ID and Client Secret) via the Salesforce Developer Console. However, if you want to integrate with multiple CRM APIs quickly, you can get started with Knit, one API for all top HR integrations.

To sign up for free, click here. To check the pricing, see our pricing page.

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