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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. 

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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TL;DR

If you are exploring Unified APIs or Embedded iPaaS solutions to scale your integrations offerings, evaluate them closely on two aspects - API coverage and developer efficiency. While Unified API solutions hold great promise to reduce developer effort, they struggle to provide 100% API coverage within the APPs they support, which limits the use cases you can build with them. On the other hand, embedded iPaaS tools offer great API coverage, but expect developers to spend time in API discovery for each tool and build and maintain separate integrations for each, requiring a lot more effort from your developers than Unified APIs.

Knit’s AI driven integrations agent combines the best of both worlds to offer 100% API coverage while still expecting no effort from developers in API discovery and building and maintaining separate integrations for each tool.

Let’s dive in.

Solutions for embedded integrations

Hi there! Welcome to Knit - one of the top ranked integrations platforms out there (as per G2).

Just to set some context, we are an embedded integration platform. We offer a white labelled solution which SaaS companies can embed into their SaaS product to scale the integrations they offer to their customers out of the box.

The embedded integrations space started over the past 3-4 years, and today, is settling down into two kinds of solutions - Unified APIs and Embedded iPaaS Tools.

You might have been researching solutions in this space, and already know what both solutions are, but for the uninitiated, here’s a (very) brief download.

Unified APIs help organisations deliver a high number of category-specific integrations to market quickly and are most useful for standardised integrations applicable across most customers of the organisation. For Example: I want to offer all my customers the ability to connect their CRM of choice (Salesforce, HubSpot, Pipedrive, etc.) to access all their customer information in my product.

Embedded iPaaS solutions are embedded workflow automation tools. These cater to helping organisations deliver one integration at a time and are most useful for bespoke automations built at a customer level. For Example: I want to offer one of my customers the ability to connect their Salesforce CRM to our product for their specific, unique needs.

Knit started its life as a Unified API player, and as we spoke to hundreds of SaaS companies of all sizes, we realised that both the currently popular approaches make some tradeoffs which either put limitations on the use cases you can solve with them or fall short on your expectations of saving engineering time in building and maintaining integrations.

But before we get to the tradeoffs, what exactly should you be looking for when evaluating an embedded integration solution?

While there will of course be nuances like data security, authentication management, ability to filter data, data scopes, etc. the three key aspects which top the list of our customers are:

1. Whether the solution covers the APP you want to integrate with
2. Whether the solution covers the APIs within those APPs to solve for YOUR use case, and
3. How much effort does it take for YOUR developers to build and maintain integrations on the platform

Now let’s try and understand the tradeoffs which current solutions take and their impact on the three aspects above.

The Coverage problem with Unified APIs

The idea of providing a single API to connect with every provider is extremely powerful because it greatly reduces developer effort in building each integration individually. However, the increase in developer efficiency comes with the tradeoff of coverage.

Unifying all APPs within a SaaS category is hard work. As a Unified API vendor, you need to understand the APIs of each APP, translate the various fields available within each APP into a common schema, and then build a connector which can be added into the platform catalogue. At times, unification is not even possible, because APIs for some use cases are not available in all APPs.

This directly leads to low API coverage. For example, while Hubspot exposes a total of 400+ APIs, the oldest and most well-funded Unified API provider today offers a Unified CRM API which covers only 20 of them, inherently limiting its usefulness to a subset of the possible integration use cases.

Coverage is added based on frequency of customer demand and as a stop gap workaround, all Unified API platforms offer a ‘passthrough’ feature, which allows working with the native APIs of the source APP directly when it is not covered in the Unified model. This essentially dilutes the Unified promise as developers are required to learn the source APIs to build the connector and then maintain it anyways, leading to a hit on developer productivity.

So, when you are evaluating any Unified API provider, beyond the first conversation, do dig deep into whether or not they cover for the APIs you will need for your use case.

If they don’t, your alternative is to either use the pass throughs, or work with embedded iPaaS tools - both can give you added coverage, but they tradeoff coverage with developer efficiency, as we will learn below.

The Developer Efficiency challenge with embedded iPaaS

While Unified APIs optimise for developer efficiency by offering standard 1: many APIs, embedded iPaaS tools optimise for coverage.

They offer almost all the native APIs available in source systems on their platforms for developers to build their integrations, without a unification layer. This means developers looking to build integrations on top of embedded iPaaS tools need to build a new integration for each new tool their customers could be using. Not only this requires developers to spend a lot of time in API discovery for their specific use case, but also then maintain the integration on the platform.

Perhaps this is the reason why embedded iPaaS tools are best suited for integrations which require bespoke customization for each new customer. In such scenarios, the value is not in reusing the integration across customers, but rather the ability to quickly customise the integration business logic for each new customer. And embedded iPaaS tools deliver on this promise by offering drag drop, no code integration logic builders - which in our opinion drive the most value for the users of these platforms.

**Do note, that integration logic customization is a bit different from the ability to handle customised end systems, where the data fields could be different and non-standard for different installations of the same APP. Custom fields are handled well even in Unified API platforms.

What sets Knit apart?

So, we now know that the two most prominent approaches to scale product integrations today, even though powerful for some scenarios, might not be the best overall solutions for your integration needs.

However, till recently, there didn’t seem to be a solution for these challenges. That changed with the rapid rise and availability of Generative AI. The ability of Gen AI technology to read and make sense of unstructured data, allowed us to build the first integration agent in the market, which can read and analyse API documentation, understand it, and orchestrate API calls to create unified connectors tailored for each developer's use case.

This not only gives developers access to 100% of the source APPs APIs but also requires negligible developer effort in API discovery since the agent discovers the right APIs on the developer's behalf.

What’s more, another advantage it gives us is that we are now able to add any missing APP in our pre-built catalogue in 2 days on request, as long as we have access to the API documentation. Most platforms take anywhere from 2-6 weeks for this, and ‘put it on the roadmap’ while your customers wait. We know that’s frustrating.

So, with Knit, you get a platform that is flexible enough to cover for any integration use case you want to build, yet doesn’t require the developer bandwidth required by embedded iPaaS tools in building and maintaining separate integrations for each APP.

This continues and builds upon our history (however small) of being pioneers in the integration space, right since inception.

We were the first to launch a 'no data storage' Unified API, which set new standards for data security and forced competition to catch up — and now, we’re the first to launch an AI agent for integrations. We know others will follow, like they did for the no caching architecture, but that’s a win for the whole industry. And by then, we’re sure to be pioneering the next step jump in this space.

It is our mission to make integrations simple for all.

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Organizations today adopt and deploy various applications, to make their work simpler, more efficient and enhance overall productivity. However, in most cases, the process of connecting with these applications is complex, time consuming and an ineffective use of the engineering team. Fortunately, over the years, different approaches or platforms have seen a rise, enabling companies to integrate applications for their internal use or to create customer facing interfaces. 

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In this article, we will discuss the different options available for companies to integrate with SaaS applications. We will detail the diverse approaches for different needs and use cases, along with a comparative analysis between the different platforms within each approach to help you make an informed choice. 

Types of SaaS integrations

As mentioned above, particularly, there are two types of SaaS integrations that most organizations use or need. Here’s a quick understanding of both:

Internal use integrations

Internal use integrations are generally created between two applications that a company uses or between internal systems to facilitate seamless and data flow. Consider that a company uses BambooHR as its HRMS systems and stores all its HR data there, while using ADPRun to manage all of its payroll functions. An internal integration will help connect these two applications to facilitate information flow and data exchange between them. 

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For instance, with integration, any new employee that is onboarded in BambooHR will be automatically reflected in ADPRun with all relevant details to process compensation at the end of the pay period. Similarly, any employees who leave will be automatically deleted, ensuring that the data across platforms being used internally is consistent and up to date. 

Customer facing integrations

On the other hand, customer-facing integrations are intrinsically created between your product and the applications used by your customer to facilitate seamless data exchange for maximum efficiency in operations. It ensures that all data updated in your customer’s application is synced with your product with high reliability and speed. 

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Let’s say that you offer candidate communication services for your customers. Using customer-facing integrations, you can easily connect with the ATS application that your customer uses to ensure that whenever there is any movement in the application status for any candidate, you promptly communicate to the candidate on the next steps. This will not only ensure regular flow of communication with the candidate, but will also eliminate any missed opportunities with real time data sync. 

Best SaaS integration platforms for different use cases

With differences in purposes and use cases, the best approach and platforms for different integrations also varies. Put simply, most internal integrations require automation of workflow and data exchange, while customer facing ones need more sophisticated functionalities. Even with the same purpose, the needs of developers and organizations can be varied, creating the need for diverse platforms which suit varying requirements. In the following section, we will discuss the three major kinds of integration platforms, including workflow automation tools, embedded iPaaS and unified APIs with specific examples within each. 

Internal integrations: Workflow automation tools/ iPaaS 

Essentially, internal integration tools are expected to streamline the workflow and data exchange between internally used applications for an organization to improve efficiency, accuracy and process optimization. Workflow automation tools or iPaaS are the best SaaS integration platforms to support this purpose. They come with easy to use drag and drop functionalities, along with pre-built connectors and available SDKs to easily power internal integrations. Some of the leaders in the space are:

Workato

An enterprise grade automation platform, Workato facilitates workflow automation and integration, enabling businesses to seamlessly connect different applications for internal use. 

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Benefits of Workato

• High number of pre-built connectors, making integration with any tool seamless
• Enterprise grade security functionalities, like encryption, role-based access, audit logs for data protection
• No-code/ low code iPaaS experience; option to make own connectors with simple SDKs

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Limitations of Workato 

• Expensive for organizations with budget constraints
• Limited offline functionality

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Ideal for enterprise-level customers that need to integrate with 1000s of applications with a key focus on security. 

Zapier

An iSaaS (integration software as a service) tool, Zapier allows software users to integrate with applications and automate tasks which are relatively simple, with Zaps. 

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Benefits of Zapier

• Easily accessible and can be used by non-technical teams to automate simple tasks via Zaps using a no code UI
• Provides 7000+ pre-built connectors and automation templates
• Has recently introduced a co-pilot which allows users to build their own Zaps using natural language

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Limitations of Zapier

• Runs the risk of introducing security risks into the system
• Relatively simple and may not support complex or highly sophisticated use cases

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Ideal for building simple workflow automations which can be developed and managed by all teams at large, using its vast connector library. 

Mulesoft

Mulesoft is a typical iPaaS solution that facilitates API-led integration, which offers easy to use tools to help organizations automate routine and repetitive tasks.

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Benefits of Mulesoft

• High focus on integration with Salesforce and Salesforce products, facilitating automation with CRM effectively
• Offers data integration, API management, and analytics with Anytime Platform
• Provides a powerful API gateway for security and policy management

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Limitations of Mulesoft

• Requires a steep learning curve as it is technically complex
• Higher on the pricing, making it unsuitable for smaller organizations

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Ideal for more complex integration scenarios with enterprise-grade features, especially for integration with Salesforce and allied products. 

Dell Boomi

With experience of powering integrations for multiple decades, Dell Boomi provides tools for iPaaS, API management and master data management. 

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Benefits of Dell Boomi

• Comes with a simple UI and multiple pre-built connectors for popular applications
• Can help with diverse use cases for different teams
• Adopted by several large enterprises due to their experience in the space

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Limitations of Dell Boomi

• Requires more technical expertise than some other workflow automation tools
• Support is limited to simpler integrations and may not be able to support complex scenarios

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Ideal for diverse use cases and comes with a high level of credibility owing to the experience garnered over the years. 

SnapLogic

The final name in the workflow automation/ iPaaS list is SnapLogic which comes with a low-code interface, enabling organizations to quickly design and implement application integrations. 

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Benefits of SnapLogic

• Simple UI and low-code functionality ensures that users from technical and non-technical backgrounds can leverage it
• Comes with a robust catalog of pre-built connectors to integrate fast and effectively
• Offers on-premise, cloud based on hybrid models of integration

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Limitations of SnapLogic

• May be a bit too expensive for small size organizations with budget constraints
• Scalability and optimal performance might become an issue with high data volume

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Ideal for organizations looking for automation workflow tools that can be used by all team members and supports functionalities, both online and offline. 

Customer facing integrations: Embedded iPaaS & Unified API

While the above mentioned SaaS integration platforms are ideal for building and maintaining integrations for internal use, organizations looking to develop customer facing integrations need to look further. Companies can choose between two competing approaches to build customer facing SaaS integrations, including embedded iPaaS and unified API. We have outlined below the key features of both the approaches, along with the leading SaaS integration platforms for each. 

Embedded iPaaS

An embedded iPaaS can be considered as an iPaaS solution which is embedded within a product, enabling companies to build customer-facing integrations between their product and other applications. This enables end customers to seamlessly exchange data and automate workflows between your application and any third party application they use. Both the companies and the end customers can leverage embedded iPaaS to build integration and automate workflows. Here are the top embedded iPaaS that companies use as SaaS integrations platforms. 

Workato Embedded

In addition to offering an iPaaS solution for internal integrations, Workato embedded offers embedded iPaaS for customer-facing integrations. It is a low-code solution and also offers API management solutions.

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Benefits of Workato Embedded

• Highly extensive connector library with 1200+ pre-built connectors and built-in workflow actions
• Enterprise grade embedded iPaaS with sophisticated security and compliance standards

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Limitations of Workato Embedded

• Requires customers to build each customer facing integration separately, making it resource and time intensive
• Lacks a standard data model, making data transformation and normalization complicated
• Cost ineffective for smaller companies and offers limited offline connectivity

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Ideal for large companies that wish to offer a highly robust integration library to their customers to facilitate integration at scale. 

Paragon

Built exclusively for the embedded iPaaS use case, Paragon enables users to ship and scale native integrations.

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Benefits of Paragon

• Offers effective monitoring features, including event and failure alerts and logs, and enables users to access the full underlying API (developer friendly)
• Facilitates on-premise deployment, especially, for users with highly sensitive data and privacy needs
• Ensures fully managed authentication and user management with the Paragon SDK

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Limitations of Paragon

• Fewer connectors are readily available, as compared to market average
• Pushes customers to create their own integrations from scratch in certain cases

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Ideal for companies looking for greater monitoring capabilities along with on-premise deployment options in the embedded iPaaS. 

Pandium

Pandium is an embedded iPaaS which also allows users to embed an integration marketplace within their product. 

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Benefits of Pandium

• The embedded integration marketplace (which can be white-labeled) allows customers and prospects to find all integrations at one place
• Helps companies outsource the development and management of integrations
• Provides key integration analytics

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Limitations of Pandium

• Limited catalog of connectors as compared to other competitors
• Requires technical expertise to use, blocking engineering bandwidth
• Forces users to build one integration at a time, making the scalability limited

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Ideal for companies that require an integration marketplace which is highly customizable and have limited bandwidth to build and manage integrations in-house. 

Tray Embedded

As an embedded iPaaS solution, Tray Embedded allows companies to embed its iPaaS solution into their product to provide customer-facing integrations. 

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Benefits of Tray Embedded

• Provides a large number of connectors and also enables customers to request and get a new connector built on extra charges
• Offers an API management solution to to design and manage API endpoints
• Provides Merlin AI, an autonomous agent, powering simple automations via a chat interface

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Limitations of Tray Embedded

• Limited ability to automatically detect issues and provide remedial solutions, pushing engineering teams to conduct troubleshooting
• Limited monitoring features and implementation processes require a workaround

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Ideal for companies with custom integration requirements and those that want to achieve automation through text. 

Cyclr

Another solution solely limited to the embedded iPaaS space, Cyclr facilitates low-code integration workflows for customer-facing integrations. 

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Benefits of Cyclr

• Enables companies to use seamlessly design a new workflow with templates, without heavy coding
• Provides connectors for 500+ applications and is growing
• Offers an out of the box embedded marketplace or launch functionality that allows end users to deploy integrations

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Limitations of Cyclr

• Comes with a steep learning curve 
• Limited built-in workflow actions for each connector, where complex integrations might require additional endpoints, the feasibility for which is limited
• Lack of visibility into the system sending API requests, making monitoring and debugging issues a challenge

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Ideal for companies looking for centralized integration management within a standardized integration ecosystem. 

Unified API

The next approach to powering customer-facing integrations is leveraging a unified API. As an aggregated API, unified API platforms help companies easily integrate with several applications within a category (CRM, ATS, HRIS) using a single connector. Leveraging unified API, companies can seamlessly integrate both vertically and horizontally at scale. 

Merge

As a unified API, Merge enables users to add hundreds of integrations via a single connector, simplifying customer-facing integrations. 

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Benefits of Merge

• High coverage within the integrations categories; 7+ integration categories currently available
• Integration observability features with fully searchable logs, dashboard and automated issue detection 
• Access to custom objects and fields like field mapping, authenticated passthrough requests

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Limitations of Merge

• Limited flexibility for frontend auth component and limited customization capabilities
• Requires maintaining a polling infrastructure for managing data syncs
• Webhooks based data sync doesn’t guarantee scale and data delivery

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Ideal to build multiple integrations together with out-of-the-box features for managing integrations.

Finch

A leader in the unified API space for employment systems, Finch helps build 1:many integrations with HRIS and payroll applications. 

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Benefits of Finch

• One of the highest number of integrations available in the HRIS and Payroll integration categories
• Facilitates standardized data for 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

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Limitations of Finch

• Limited number of integration categories available
• Offers  “assisted” integrations, requiring a Finch team member or associate to manually sync data on your behalf
• Low data fields support limited data fields available in the source system

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Ideal for companies looking to build integrations with employment systems and high levels of data standardization. 

Apideck

Another option in the unified API category is Apideck, which offers integrations in more categories than the above two mentioned SaaS integration platforms in this space. 

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Benefits of Apideck

• Higher number of categories (inc. Accounting, CRM, File Storage, HRIS, ATS, Ecommerce, Issue Tracking, POS, SMS) than many other alternatives and is quick to add new integrations
• Popular for its integration marketplace, known as Apideck ecosystem
• Offers best in class onboarding experience and responsive customer support

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Limitations of Apideck

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• Limited number of live integrations within each category
• Limited data sync capabilities; inability to access data beyond its own data fields

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Ideal for companies looking for a wider range of integration categories with an openness to add new integrations to its suite. 

Knit

A unified API, Knit facilitates integrations with multiple categories with a single connector for each category; an exponentially growing category base, richer than other alternatives.

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Benefits of Knit

• Seamless data normalization and transformation at 10x speed with custom data fields for non-standard data models
• The only SaaS integration platform which doesn’t store a copy of the end customer’s data, ensuring superior privacy and security (as all requests are pass through in nature)
• 100% events-driven webhook architecture, which ensures data sync in real time, without the need to pull data periodically (no polling architecture needed)
• Guaranteed scalability and delivery, irrespective of the data load, offering a 99.99% SLA
• Custom data models, sync frequency and auth component for greater flexibility
• Offers RCA and resolution to identify and fix integration issues before a customer can report it
• 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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Ideal for companies looking for SaaS integration platforms with wide horizontal and vertical coverage, complete data privacy and don’t wish to maintain a polling infrastructure, while ensuring sync scalability and delivery. 

Best SaaS integration platforms: A comparative analysis

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TL:DR

Clearly SaaS integrations are the building blocks to connect and ensure seamless flow of data between applications. However, the route that organizations decide to take large depends on their use cases. While workflow automation or iPaaS makes sense for internal use integrations, an embedded iPaaS or a unified API approach will serve the purpose of building customer facing integrations. Within each approach, there are several alternatives available to choose from. While making a choice, organizations must consider:

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• The breadth (horizontal coverage/ categories) and depth (integrations within each category) that are available
• Security, authentication and authorization mechanisms
• Integration maintenance and management support
• Visibility into the integration activity along with intuitive issue detection and resolution
• The way data syncs work (events based or do they require an additional polling infrastructure)

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Depending on what you consider to be more valuable for your organization, you can go in for the right approach and the right option from within the 14 best SaaS integration platforms shared above. 

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.

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.

‍

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)

‍

‍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

‍

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.

‍

Humi HR software is a robust all-in-one human resources information system (HRIS) tailored for Canadian businesses, particularly small to medium-sized enterprises. It offers a comprehensive suite of tools designed to streamline HR processes, making it an invaluable asset for companies looking to enhance their human resources management. By consolidating various HR functions into a single platform, Humi HR software helps businesses manage payroll, HR, benefits, and insurance with ease. Its user-friendly interface and extensive features, such as employee data management, payroll management, and benefits administration, empower organizations to improve efficiency, reduce errors, and save valuable time.

Beyond its core functionalities, Humi HR software excels in providing seamless onboarding and offboarding workflows, performance management, compliance and reporting tools, and digital document management. These features ensure that businesses can maintain a high level of organization and compliance while focusing on their growth and development. A key aspect of Humi's appeal is its ability to integrate with other systems through the Humi API, allowing businesses to customize and extend the software's capabilities to meet their unique needs. This flexibility makes Humi HR a versatile solution for modern HR challenges.

Key highlights of Humi HR APIs

Humi offers a Partners API that enables developers to access and manage employee data within third-party applications, facilitating seamless integration with the Humi HR platform. This API adheres to the JSON:API specification, ensuring standardized data exchange. Source: GitHub

Key Features of the Humi Partners API:

• Authentication: Utilizes a token-based system. Tokens are included in the Authorization header as a Bearer token for each request.
• Data Format: Employs JSON for both requests and responses, aligning with the JSON:API spec
• Endpoints: Provides access to employee information and time-off data. Key endpoints include:some text
  • Employees:some text
    • List Employees: GET /v1/employees
    • Retrieve Employee Details: GET /v1/employees/{employeeId}
  • Time Off:some text
    • List Time Off Requests: GET /v1/timeoff
    • Retrieve Time Off Details: GET /v1/timeoff/{employeeId}
• Webhooks: Supports event-driven interactions with webhooks for events such as:some text
  • Employee Created
  • Employee Updated
  • Employee Terminated
  • Employee Deleted
  • Company Created
  • Company Updated

‍

Humi HR API Endpoints

Employee

• GET https://partners.humi.ca/v1/employees/:employeeId : Get Employee Details

Payroll

• GET https://partners.humi.ca/v1/payroll/additional_incomes : Get Additional Incomes for a Company
• POST https://partners.humi.ca/v1/payroll/employee_time_worked : Employee Time Worked

Time Off

• GET https://partners.humi.ca/v1/timeoff : Get Approved Time Off Requests
• GET https://partners.humi.ca/v1/timeoff/:employeeId : Get Approved Time Off Requests for Employee

Humi API FAQs

How can I access the Humi HR API?

• Answer: To access the Humi HR API, you need to obtain a Humi Partners API Token. This token allows you to access employee data within third-party applications. To request a token, contact Humi support at support@humi.ca with the subject "Requesting Humi Partners API Token."
  GitHub

What authentication method does the Humi HR API use?

• Answer: The Humi HR API uses token-based authentication. Once you have obtained your API token, include it in the Authorization header of your HTTP requests as follows: Authorization: Bearer your-token-here.

Are there rate limits for the Humi HR API?

• Answer: The official documentation does not specify explicit rate limits for the Humi HR API. However, it's recommended to implement error handling for potential rate limiting responses to ensure robust integration.

Can I retrieve employee data using the Humi HR API?

• Answer: Yes, the Humi HR API provides endpoints to retrieve employee data. For example, you can use the GET /v1/employees endpoint to fetch a list of employees, and GET /v1/employees/:employeeId to retrieve details of a specific employee.

Does the Humi HR API support webhooks for real-time data updates?

• Answer: Yes, the Humi HR API supports webhooks for real-time data updates. Events such as EmployeeCreated, EmployeeUpdated, EmployeeTerminated, and EmployeeDeleted can trigger webhooks, allowing your application to receive immediate notifications when these events occur.

Get Started with Humi API Integration

1. Obtain an API Token: Contact Humi at support@humi.ca to request a Partners API token. Note that the API is in a closed beta phase, and access is provided upon request.
2. Authenticate Requests: Include the obtained token in the Authorization header of your HTTP requests as follows:
   makefile
   Copy code
   Authorization: Bearer your-token-here
3. Make API Calls: Utilize the available endpoints to interact with employee and time-off data. For example, to list employees:
   bash
   Copy code
   GET https://partners.humi.ca/v1/employees
4. Handle Responses: Responses conform to the JSON:API specification, providing structured data for easy parsing and integration.

Additional Resources:

• Sample Node.js Client: A sample Node.js client is available to demonstrate API usage and can serve as a reference for implementation.
• Integration Use Cases: For practical examples of integration scenarios, refer to the Humi-HR API Integration Use Cases, which provide insights into common API interactions.  Knit Developers

About Knit

Knit API offers a convenient solution for quick and seamless integration with Humi HR API. Our AI-powered integration platform allows you to build any Humi API Integration use case. By integrating with Knit just once, you can integrate with multiple other CRM, Accounting, HRIS, ATS, and other systems in one go with a unified approach. Knit handles all the authentication, authorization, and ongoing integration maintenance. This approach saves time and ensures a smooth and reliable connection to Humi HR API.‍

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