Guide

The Applicant Tracking System - a blessing and a curse for pretty much every recruiter out there.

Initially designed to serve as the “single source of truth” in the recruitment process, the ATS is supposed to be your go-to software for managing candidate information. But that’s not necessarily how things work in reality.

Proper upkeep of the ATS requires so much effort and attention that many recruiters struggle with keeping it updated amidst busy schedules.

As a result, the ATS - more often than not -ends up being a disorganized and incomplete filing cabinet, instead of the “goldmine of candidate information” that it's meant to be.

Sure, the essentials might be there -experience, location, salary expectations - but what about the more nuanced information that can actually inform better hiring decisions? The soft skills, cultural fit insights, or a candidate's willingness to relocate? This potentially make-or-break information often gets lost in the shuffle.

But that’s about to change.

In today’s day an age, we have generative AI - technology that’s changing the way the ATS is used and the value it brings.

Imagine having a smart assistant that can handle the heavy lifting of data entry, pulling information from various sources like interviews, social profiles, or assessments—so that you never have to spend another afternoon cleaning up your ATS data again.

Sounds good? Great! Then this guide is for you.

We'll explore three scenarios for using AI with your ATS to reduce admin and improve data quality. Each scenario offers a series of benefits as well as limitations, and the approach you choose can boost or hinder your effectiveness down the road.

So - exciting information to unpack here, but let’s take it one step at a time. Let’s first look into why the ATS often falls short of its potential. After that we’ll explore how artificial intelligence can help bridge the gap.

Enjoy the read!

Why ATS data hygiene gets neglected

Despite their potential to streamline hiring, ATS platforms are often under-utilised, and both recruiters and hiring managers have mixed feelings about them.

The main reason? Poor data hygiene.

And when data quality isn't maintained, the ATS actually becomes a bottleneck where candidates go to be forgotten, rather than a tool that drives efficiency.

This not only alters the recruiter experience, but also undermines a company’s ability to build and take advantage of talent pools. When candidate information in the ATS is incomplete or outdated, the sourcing and recruitment process for every new role needs to start from scratch, and the effort put into interviewing candidates and adding them to the talent pool is wasted.

But why is data hygiene so difficult to maintain? For a couple of reasons.

1. Human nature

Like it or not, recruitment is fast-paced, and many recruiters simply don’t have the bandwidth - or discipline - to update the ATS consistently.

Between sourcing new candidates, conducting interviews, and liaising with hiring managers or clients, data entry feels like a low priority task that always gets pushed to "later." As a result, key candidate information never makes it into the system, or if it does, it’s incomplete or inaccurate.

2. Disorganized processes

Without a clear, standardized process for how and when to update the ATS, data entry becomes inconsistent. Some recruiters might add detailed notes to the ATS after every call, while others might wait until the end of the week or skip certain fields altogether.

This creates a patchwork of information that’s difficult to navigate and ultimately diminishes the ATS’s value.

3. Incomplete feedback

Hiring managers are also contributing to this. Their feedback is crucial for the interview process, but the notes they add to the ATS are often incomplete or rushed.

When managers only provide surface-level impressions, like "good fit" or “not right for the role," the ATS can’t capture the full picture of a candidate’s qualifications or fit for future vacancies. As a result, recruiters can’t fully use the information when hiring for a new role, and may need to start the screening and interviewing process from scratch.

4. Messy notes

Adding to these, interview notes are often rushed and disjointed, making them hard to interpret a week later if you didn’t update the ATS immediately after the call.

With interviews scheduled in batches, it’s often not possible to do the data entry right after each interview. So, as with everything, if you put “garbage in”, you get “garbage out”. The same is true for your ATS.

5. Data formats‍

Finally, ATS platforms are built to handle structured data, like names, contact information, and job titles. However, interviews and candidate interactions produce unstructured data—conversational insights, nuanced feedback, or offhand comments.

This type of data doesn’t fit neatly into predefined ATS fields. As a result, valuable context can get lost, and recruiters might struggle to make sense of the stored input later on.

All these challenges compound over time, turning the ATS into a messy information dump rather than a tool that supports strategic recruitment decisions.

But by addressing these issues head-on, teams can unlock the true potential of their ATS — and that's where AI can make all the difference. Let’s see how.

How AI can address these challenges

Now that we’ve outlined the common struggles with applicant tracking systems, let’s explore how AI can ease the administrative burden and streamline your entire recruitment process — especially when it comes to maintaining data hygiene.  

AI can take on the role of a digital data steward for your ATS, autonomously pulling and processing information from various sources like resumes, emails, assessments, pre-screening calls, and video interviews.

What makes AI particularly effective at maintaining data hygiene is that it can handle both structured and unstructured data - 24/7, error-free, and without getting tired.

Structured data, like candidate names, contact information, or education history, is easy to process since it fits neatly into predefined fields.

However, a lot of information in the hiring process comes from unstructured data—free-text responses, interview notes, email conversations, meeting transcripts, and so on. This data forms the context surrounding a candidate or a vacancy, and offers richer insights than the structured data alone.

Artificial intelligence is capable of analyzing the unstructured data, extracting meaningful details, and transforming the data points into structured information that the ATS can use efficiently. Without AI, this task falls on recruiters and takes up a lot of time.

For example, if a candidate provides details about their work history during a phone interview or in an email, AI can recognize key elements like job titles, skills, past experience and accomplishments, and automatically populate the relevant fields in your ATS.

By doing this across multiple candidate touch points, AI ensures that your ATS is constantly updated with complete and accurate information, no matter where the data comes from.

This ability to manage both structured and unstructured data allows AI to eliminate one of the biggest pain points for hiring teams: manual data entry.

In addition, it also helps prevent human errors, such as duplicating entries, or overlooking critical information that’s buried in free-text formats. By cleaning up and structuring this data, AI ensures that your ATS becomes the golden source of information it was designed to be, and enables more effective sourcing and screening processes as a result.

However, for AI to work its magic and maintain top-notch data hygiene, it needs to be properly integrated with your ATS and recruitment tech stack.

The deeper the integration, the better the data management becomes, because the AI gets access to the full context of your candidates, job openings, and clients. In other words, the AI is enabled to work with the same information that your human recruiters have at their disposal.

There are three main ways AI can be integrated with your ATS to enhance data hygiene and recruitment efficiency:

• The native assistant: In this first configuration, the AI lives inside the ATS, and is offered as an additional feature or functionality. It has access to everything within the ATS, but it can only work within this context; in other words, it can only work with the data already in the ATS. We’ll detail this in the next section.
• The external assistant: The second option is to use a specialised, recruitment-specific AI assistant that lives outside the ATS and interacts with it through a two-way API integration. This offers more flexibility and customisation options, as the AI is able to access virtually any source of information you hook it up to - not only what’s available in the ATS.
• The embedded assistant: Finally, a third solution is to embed an external AI assistant into the ATS via an iframe, for example, so that you use a single “cockpit”, instead of having to operate two separate systems. In this scenario, the AI works in the background, updating the ATS with information from various external sources - interviews, social profiles, assessments, and so on.

Each method has its strengths and limitations, and we’re about to explore how they can shape your recruitment process.

Using AI with an ATS: Three main scenarios

Time to get more technical. As said, there are three main approaches to consider for your AI/ATS integration:

• Native AI assistant
• External AI assistant
• Embedded AI assistant

Let’s break down each of these setups to understand which one is the best for you.

Scenario 1: The native AI assistant

In this setup, the AI assistant is a native component of the ATS, functioning as a core part of the platform. It can help with resume parsing, creating documents, and answering questions related to the information stored within the ATS. The AI has access to any data that the recruiter adds to the system - such as meeting notes, for example, and can use this data as input for generating job descriptions, candidate profiles, or interview questions.

All AI-related settings and configurations are managed within the ATS interface, providing a centralized control point, which is one of the main advantages of this setup. However, there are two limitations that strongly impact the effectiveness of this configuration.

Firstly: In this setup, the AI assistant doesn’t interact with any external, independent source of information; it only has access to the data already available in the ATS, which can come from:

• Manual data entry by recruiters, after a meeting or intake call, or
• Job application forms, social media platforms, and candidate databases, through API integrations.

In both cases, the data entered into the ATS is structured to fit the field requirements of the system.

For example, the “Location” field will only accept a data value like “Amsterdam, The Netherlands”, but will not accept a value like “Currently in Amsterdam, The Netherlands, but willing to relocate within the next 6 months”. An API integration might not pick up on this data value, and simply update the ATS with something like “not specified” or “Amsterdam”.

So information gets lost.  

Secondly, because the data entry in this setup is done manually by the human recruiter, it is prone to errors, and it’s often incomplete, as we already showed in the beginning of this guide. The recruiter might remember that the candidate is willing to relocate if he updates the ATS within the first hours or day after the interview, but will likely forget this detail if the post-interview admin is delayed.

Thus, in this scenario, the recruiter is solely responsible for the data hygiene and their input becomes the weak point of the AI’s effectiveness.  

If the data added to the ATS is incomplete, rushed, inaccurate, or outdated, the AI assistant - no matter how intelligent - will generate flawed output. This can lead to additional work for recruiters, who may need to double-check and rewrite every answer or document generated by the AI.

In short: In this setup, the effectiveness of the AI assistant will only be as good as the data added by the recruiter.

That’s not to say everything is negative; a native AI assistant does offer some benefits.

Having all data centralised in one system - the ATS - is ideal, as it minimises context-switching for recruiters, allowing them to work in a more focused manner. At the same time, the fact that some ATSs offer the possibility of building automated workflows for speeding up the hiring process, and that AI could be added to these workflows, is beneficial.

However, if the AI assistant has little or no data points to work with, and the automation workflows are powered by the same incomplete, flawed data, these advantages aren’t really making an impact in the bigger picture of the recruitment process.

Thus, this type of setup, were the AI assistant is native to the ATS, might not be the ideal choice for teams looking to significantly reduce their admin workload.

So let’s explore the second option now.

Scenario 2: The AI assistant lives outside the ATS

‍Unlike built-in assistants, external ones operate as separate tools that integrate with  an ATS through API connections.

In general, these AI assistants offer more functionalities, customisation options, and flexibility than native ones, and can be configured to fit virtually any recruitment workflow. All the setup and data mapping is done within the AI tool itself, not in the ATS, and it’s a one-time thing during the integration or implementation process.

Because they’re not embedded in the ATS itself, these AI tools are accessed through their own interface. This means that recruiters have two options:

• To work in the AI tool, generate their documents there, and push them to the ATS through the API integration, or
• To work in the ATS only, and let the AI assistant do the work in the background - generate documents, populate the ATS fields, and so on.

Regardless of the chosen way of working, this setup provides a series of benefits that might not be found in the built-in solution we discussed before.

For example, external recruiting assistants can join recruiters during meetings, making sure the AI has access to the source material, as opposed to only the ATS data entries added by the recruiter. As a result, the output of an external assistant is much better than that of a native one.

Furthermore, they can integrate with a variety of data sources, from meeting tools and calendars to assessment tools and social media platforms. The data from all these sources can flow into the AI tool, where it will be either memorised or processed for different use cases.

These external assistants can help with predefined tasks, while at the same time offering recruiters the possibility to prompt and interact with the AI in a human-like manner. The tasks performed by an external AI assistant can be customised, with the goal of achieving the partial or full automation of recruitment workflows.

This integration option, with an external AI agent populating the ATS automatically,  enables recruiters to leverage AI for a wide variety of previously deemed impossible tasks, like admin automation, candidate sourcing and matching, or talent pool engagement - without having to replace their existing ATS.

Overall, this setup is better for recruitment teams than the native AI assistant, as the external agents can take over the bulk of admin work, freeing up recruiters for more impactful tasks. Less admin work also means a better recruiter experience and translates into a better candidate experience, as recruiters are able to focus and dedicate their full attention to candidates.

On the other hand, this approach does pose some small inconveniences: It requires separate system configuration and management, an extra budget, and in some cases a custom implementation if the tool doesn’t already integrate with a specific ATS.

Also, recruitment teams might need to rethink their processes to see which tasks could be delegated to AI and which should remain under human supervision. But all in all, the benefits far outweigh the drawbacks.

Before we move on to Scenario 3, there’s one important mention to be made here.

Throughout this section, we’ve referred to external AI assistants, and your mind might have immediately gone to tools like ChatGPT or Gemini. While these are powerful, they’re generic AI assistants, meaning that they’re not designed specifically for recruitment. As a result, they generate generic output.

Recruitment-specific AI solutions are tailored to the unique challenges and workflows of hiring teams, offering features like document generation, automated interview scheduling, and deep integrations with your ATS.

If you’re wondering about the key differences and why specialized tools matter, this article might be helpful: Generic Generative AI vs. Specialised AI - Main Differences.

All right. That was Scenario 2. But there’s one more option to explore: The embedded AI assistant.

Scenario 3: The AI assistant is embedded into the ATS

This last setup offers a bit of both of the previous set ups: The AI assistant functions as an integral part of the ATS, but is a standalone tool that can be accessed through its own interface, and that connects with external sources of information - social media platforms, candidate databases, meeting and scheduling tools, and so on.

Within the ATS, the assistant is available as an iframe or a separate platform section, and the recruiter can access most functionalities without having to leave the ATS.

Just like in the previous scenario, the AI assistant works in the background, 24/7, joining meetings, taking notes, converting them into ATS-ready data, and generating output documents specific to the meeting type: Job descriptions and intake briefs after an intake call, candidate profiles and write-ups after an interview.

The AI assistant enriches the candidate profiles, ensuring recruiters always have the most up-to-date and complete information at their fingertips. This reduces the admin workload and context switching, as recruiters no longer need to cross-reference data with other tools. As a result, they experience less fatigue and can be more productive during the day.

Additionally, this setup ensures the ATS truly functions as the central hub for recruitment, driving smarter, faster hiring decisions.

As you can see, the use cases are quite similar to the standalone external assistant, and so are the benefits and limitations. However, there are some distinctions worth noting:

• The standalone external AI assistant might offer more flexibility and customisation options when accessed through its own interface, while the embedded version might have some limitations given by the ATS itself. For example, some functionalities might be disabled in the embedded assistant, or some integrations might go directly through the ATS and become redundant - the connection with social media platforms, messaging, or scheduling tools.
• Also, some documents might be duplicated between the ATS and the AI tool - candidate resumes for example, so you might need to disable the AI tool’s ability to overwrite these docs and keep it with the ATS itself, for proper data hygiene.

Now that you’re familiar with the three potential setups and their pros and cons, let’s look at some overlapping use cases.

Common use cases of AI recruitment assistants

Although the use cases are virtually endless, we’ll list the most common ones, to help you understand what you can use an AI assistant for, once you integrate it with your ATS.

1. Take notes during interviews and intake calls to restore the recruiter’s focus on the candidate.
2. ‍Automatically fill in the ATS with data from intake calls and interviews , candidate data bases, and so on, to minimise the risk of error associated with manual data entry.‍
3. Reduce the admin workload of recruiters by automating routine tasks. Tasks like: scheduling interviews, creating job descriptions and job requirements after an intake call, or creating candidate write-ups after an interview. AI assistants can also help prepare interview questions, follow-up emails, or pre-screening questions.
4. Analyse conversations and emails to gauge the candidate sentiment and engagement level. This data can be automatically fed back into the ATS to help recruiters understand the emotional tone of interactions and candidate satisfaction.‍
5. Pre-screen candidates through written or voice conversations - instant messaging, prescreening calls, or online assessments. This can significantly reduce the time spent evaluating candidates' skills, competencies, and fit for the role.‍
6. Dynamically match candidates to jobs, whenever a new opening is posted or a new candidate applies to an open role. ‍
7. Build your talent pool, activate passive candidates, and keep candidates engaged throughout the application process through personalized messages.
8. Finally, although most ATSs come with built-in analytics modules, an AI assistant can analyse data and offer insights into the effectiveness of the recruitment process that would be hard to gauge through out-of-the-box dashboards.

To sum it up: each AI integration scenario—built-in, external, and embedded—brings unique benefits and trade-offs.

The next step is to evaluate which of these approaches fits best with your recruitment goals, operational needs, and future vision.

Which scenario is best for you?

Ultimately, choosing between these AI integration approaches depends on your specific needs.

• AI assistants native to the ATS are added on top of the existing structure and logic, so they inherit the limitations of the ATS itself, as shown in the previous section. Still, if your main goal is to start experimenting with AI capabilities while minimising disruption, a native AI assistant might be a good solution, as it offers a smooth transition to collaborating with AI and integrating it into recruitment workflows. However, with this setup, the recruitment team might still handle some data entry tasks manually - for example, taking notes during an interview and adding them to the ATS. Of course, recruiters could use an additional tool like a meeting note taker, along with tools like ChatGPT to turn those notes into structured ATS data. But this approach just adds complexity to workflows that are already heavy on admin tasks.
• Standalone assistants are AI-first, meaning that their interface and logic are built for an AI-enabled process. So, if your goal is to minimise admin work and automate your process as much as possible while capturing richer insights about candidates, the external AI assistant approach could be a better fit. Through integrations with meeting tools, this approach allows for more advanced features like meeting transcription, note-taking, and automated generation of recruitment content, while ensuring the ATS is automatically filled with up-to-date information. Just keep in mind that while this setup offers greater flexibility and capability, it may require extra setup, workflow adjustments, and monitoring to ensure seamless data flows and system synchronization.
• Finally, those seeking a middle ground between flexibility and simplicity can opt for an embedded AI assistant that offers the best of both worlds. It operates as an external tool but is tightly integrated within the ATS, providing advanced functionalities while remaining within the familiar ATS interface. With this approach, the recruitment team benefits from AI-powered workflows without having to switch between tools. Just like scenario number two, this setup minimizes disruption and enhances automation, but may involve a more complex integration process and potentially higher costs for customization and maintenance than the other two options.

Below you can see a summary of these potential setups, to help in your decision-making process.

In the end, the end goal of integrating AI into your ATS is to transform your applicant tracking system from a basic database into an always up to date - smart platform that removes time-consuming, repetitive tasks, and powers more informed hiring decisions.

So, before choosing a solution, make sure to consider the aspects below:

• Your integration goals
• Your current ATS capabilities versus your desired functionalities
• The API accessibility of your ATS, to assess the complexity of a potential integration
• The suite of integrations with external tools already offered by your ATS
• The user friendliness and reviews of external tools, if considering such options
• The flexibility and customisation options offered by external AI assistants
• Data security and privacy
• The scalability offered by external tools
• Costs of integration
• Training needed to drive the adoption of new tools, if opting for external AI solutions.

By weighing these factors, you can determine which scenario aligns best with your goals and recruitment strategy.

Example AI / ATS integration: Carv - Bullhorn

Now that we’ve discussed the different scenarios for AI-ATS integration, let’s look at a real-life example. We’ll explore the third setup, the one of the embedded AI assistant, by analysing the Carv - Bullhorn integration.

Here’s a quick overview of the two platforms.

Carv is AI for recruiters, designed to increase productivity and restore the focus on candidates by bringing recruitment admin down to zero.

Carv’s AI assistant joins recruiters in all their meetings - interviews, intake calls, and internal meetings, whether they’re face-to-face, virtual, or by phone - taking notes and recording everything that’s said.

This context is then turned into different output formats that can be used as they are in the Carv platform or pushed to an ATS.

For example, for a general meeting, Carv will provide recruiters with meeting notes and action points, while for an interview it will create output documents like a candidate profile and a presentation for clients or hiring managers.

Carv stores both meetings and candidate profiles, making it easy for recruiting teams to refer back to these sources of information and understand the full context of a candidate or vacancy. The AI assistant has infinite memory, so no information gets lost.

Bullhorn is one of the leading recruitment CRM and ATS platforms, widely used by agencies and in-house teams to manage candidate pipelines, job postings, and client relationships. It’s a robust system that centralizes recruitment tasks, but like many ATS tools, it is built around structured data, which means it comes with some limitations.

Although it’s a flexible tool that offers plenty of customization options, Bullhorn’s view of a candidate is still limited to a number of predefined fields. As a result, the rich information captured during screening calls and interviews gets lost unless the recruiter writes down everything and manually adds those notes to the ATS.

This is where Carv’s AI adds tremendous value.

Adding Carv’s AI assistant to Bullhorn supercharges the applicant tracking platform, enabling fully automated workflows and an AI-powered process where human recruiters and AI agents work alongside. Data entry tasks are handed over to AI, so recruiters can focus on building relationships with candidates, hiring managers, and clients.

Carv’s AI excels at processing unstructured data, turning it into structured data points that can be used by other systems.

For example, it can extract key details from a screening call or interview and transform them into structured data fields that are automatically pushed to Bullhorn. This eliminates the need for manual data entry, enhances the candidate profiles, and enables easier searchability and matching within the ATS.

As a result, instead of just seeing basic information—like job titles, years of experience, or contact details—recruiters access a rich candidate page that includes not only the candidate profile and write-up, but also the recordings of all the meetings held by the hiring team.

Documents like the resume, cover letter, an even assessment results are also available in the candidate page, giving a complete, 360-degree view of the applicant that helps improve both the quality and the speed of placements.

With Carv embedded directly into Bullhorn, recruiters can interact with the AI assistant without having to switch tools. The AI works in the background 24/7, automatically filling in the ATS after each meeting. This ensures that recruiters always have up-to-date and accurate information without needing to manually input, categorize, or organize data.

Moreover, the recruiter can ask questions and prompt the AI to provide specific insights or updates. For example, they can inquire about a candidate’s suitability for a role based on recent interview notes, request a summary of key strengths and weaknesses, or seek clarification on any unusual data points.

This level of interaction not only enhances the recruiter’s ability to make informed decisions but also streamlines the process by providing immediate, actionable information directly within the Bullhorn interface.

This allows recruiters to operate more efficiently by minimising the amount of admin tasks they have to execute after each intake call and interview. By no longer having to worry about taking notes or turning them into accurate data for the ATS, recruiters can be fully present in meetings.

As for the types of outputs generated by the Carv AI assistant, both their structure and tone of voice can be customised to fit the employer brand of the hiring organisation.

The same approach can be applied to any other ATS that supports this type of integration.

By embedding Carv’s AI into various ATS platforms, recruiters can benefit from a richer, more comprehensive candidate profile that captures and organizes both structured and unstructured data. This integration ensures that valuable insights from interviews, calls, and other interactions are seamlessly incorporated into the ATS, regardless of the system in use.

Measuring the success of the integration

We’re almost at the end. To ensure that the integration of AI into your ATS is delivering real value, it's important to establish clear success metrics and track performance over time.

We generally recommend looking at four different areas of impact:

• Recruiter experience
• Candidate experience
• Productivity & process effectiveness
• Overall business impact

Of course, there might be some differences between agency and in-house teams, but in general, these are the areas where AI can make the biggest difference. Here’s how to evaluate each of them.

Recruiter experience

AI assistant should operate as an extension of a human recruiter with infinite memory and execution power. It should be able to take over the time-consuming, repetitive tasks that don’t form the core of the recruitment process; Think of repetitive admin work like taking notes, ATS data entry, or creating job descriptions.

From this perspective, it should be fairly easy to evaluate whether the integration has the expected impact or not, by simply looking at the reduction in manual work for your recruitment team. Check if the AI addition is simplifying workflows, allowing recruiters to focus on more strategic activities, such as relationship-building with candidates and clients.

Surveys or feedback sessions can help gauge recruiter satisfaction and the AI adoption rate within your organisation.

Candidate experience

A smoother, faster, and more personalized candidate journey is often a core benefit of AI integration.

Metrics to watch include the candidate satisfaction scores, response times, conversion rates throughout the recruitment funnel, and the quality of job matching and placements. Look for improvements in communication, speed of feedback, and the overall ease with which candidates move through your hiring process.

Productivity & process effectiveness

Measure productivity by tracking time saved in administrative tasks, such as ATS data entry, document creation, candidate screening, or interview scheduling.

Look for reductions in manual work and increased recruiter bandwidth, which should indicate that the AI is effectively streamlining day-to-day operations.

Metric-wise, you can evaluate whether the AI is enhancing process efficiency by looking into your time-to-fill and time-to-hire numbers. With improved data quality and consistency, your recruiters should be able to move candidate through the process and make decisions faster.

Business impact

Finally, assess how an AI-enhanced recruitment process is influencing your overall business performance and growth. Eventually, AI should lead to higher candidate placement and retention rates, better-quality hires, faster time-to-placement, and improved satisfaction for both clients and hiring managers, all of which drive business success.

These factors demonstrate how AI is enhancing your organization’s ability to identify, evaluate, and place candidates effectively, ultimately supporting sustained growth and a competitive edge in the market.

By evaluating these four areas, you’ll be well-equipped to determine if your AI-ATS integration is delivering genuine value throughout the recruitment process.

Conclusion

AI tools have the potential to significantly enhance ATS workflows, addressing many of the common challenges recruiters face when managing candidate data.

Whether the AI assistant operates within the ATS or as an external tool that feeds into it, both approaches ultimately work toward the same goal: making the applicant tracking system a truly valuable resource for smarter, faster recruitment decisions.

If you’re ready to implement AI into your workflows and want to explore options for your ATS, book a demo here.

Carv’s AI assistant can be integrated with most ATS systems to reduce the admin work and increase the efficiency of the recruitment process.