AI for Recruitment Agencies: Uses by Task, Human Roles and AI Systems

What AI is changing most in recruitment right now is tasks such as searching by criteria, copying information over, and drafting. The role of CAs and RAs is shifting toward work that only people can do.

Using AI in recruitment means reorganizing work into a flow where AI drafts content and record updates, and people check and approve them. The aim is to give career advisors (CA) and recruitment advisors (RA) more time with candidates and client companies.

The quality of AI’s answers depends on the context you give it. Context is the background information that AI uses for judgments and drafts. It means having records in place that show who a person is, when things happened, how they got there, and which job openings and companies they are connected to. That is why NowVillage does not stop at introducing AI tools. We start by organizing your candidate database.

This article was written by NowVillage, which has supported more than 150 recruitment companies (as of October 2026).

Who this article is for

• Executives worried that AI will make recruitment agencies obsolete
• Teams unsure which tasks to start using AI for
• Teams that introduced AI tools that are not being used in daily work

This article focuses on where and how to use AI in recruitment work. For how to choose a system, see our comparison of recruitment agency systems and CRMs.

Will AI make recruitment agencies obsolete? What is changing now is mainly tasks

What AI is changing most right now is tasks such as searching by criteria, copying information over, and drafting. The human role is shifting toward work that only people can do.

AI can now handle the task of linking job openings and candidates on criteria alone. As a result, companies that spend much of their time on paperwork and data entry are more likely to fall behind companies that use AI.

On the other hand, changing jobs is a big decision for the person, and candidates often cannot put what they want clearly into words. When it comes to listening, drawing out what candidates really want, and making proposals that also fit a company’s hiring situation, the role of CAs and RAs grows even larger. Supporting people who hesitate after an offer, and adjusting salary and start dates, are also jobs for someone who knows each side’s situation.

No one can say for certain how the industry will change. NowVillage’s view is that the difference will come from what you do with the time AI frees up. Specifically, it depends on whether you can put that time into work only people can do, such as holding more consultations, deepening relationships with client companies, and re-engaging past registrants.

Work to hand to AI, and work only CAs and RAs can do

Hand AI the “drafting, organizing and detecting.” People take on “listening, deciding and persuading,” and approve what AI produces.

Splitting each task into what to hand to AI and what people decide looks like this.

Task	What to hand to AI	What people decide and approve
Acquisition	Drafts of blog posts and columns, social media posts and job ad copy, landing page drafts	The message, fact-checking, approval to publish
Follow-up after registration	Reply drafts, answers to common questions, assigning an owner	The first phone call, spotting people who are in a hurry
Consultations	Transcription, summaries, suggested updates to record fields	Hearing real feelings and doubts, career advice, approving suggested updates
Documents	Drafts of recommendation letters and job descriptions, organizing fields from received documents	Checking the wording, verifying against the facts
Matching	Listing candidates who match the criteria, showing trends in why placements fell through or were declined	Whether to recommend, confirming the candidate’s wishes, actions on the causes
Business development	Researching and updating company information	Which companies to approach and what to say, aligning on hiring requirements
Management metrics	Aggregation, report drafts	Where to take action, allocation of people and budget

In every task, the flow is “AI drafts, people approve.” AI can write facts that are not in the records, so the person in charge checks and approves AI drafts and suggested updates before sending them or saving them to records. Recommendation letters and messages to candidates go directly to the other party, so the person in charge always reads them before sending.

By task: 7 places to use AI in recruitment

There are seven places to use AI, from acquisition to management metrics. In each one, decide in advance who approves what AI produces.

1. Acquisition: draft blog posts, social media posts and job ads with AI

Creating acquisition content is something AI does well. You can hand it blog and column drafts, social media posts, job ad copy, and even landing page drafts. Some AI-powered CRMs and marketing tools let AI write blog and email copy right in the editor. Having AI draft social media posts based on published articles is another way to save effort.

Whatever the method, deciding the message, checking the facts and approving publication are the human role. For job ads in particular, check that conditions such as salary and work location match the job opening received from the client company.

For how to compare acquisition channels, including efforts to be chosen in AI answers (AEO) and ChatGPT ads, see how recruitment agencies attract candidates.

2. Follow-up after registration: book consultations faster

From registration to booking a consultation, results depend on how quickly you get in touch. So before using AI, set up CA assignment and pre-consultation reminders as fixed steps that run automatically.

AI helps with writing messages and answering inquiries. In some systems, AI can draft emails based on what is in the records. Even then, the person in charge checks the draft before sending. Some AI tools answer inquiries based on your own FAQs and similar content, and can handle the first response to common questions from candidates.

3. Consultation records and summaries: AI suggests record updates, and the CA approves

Consultation records are where the effect of AI is easiest to see, because less data entry lets the CA focus on the conversation. Some AI-powered CRMs and meeting tools can record online meetings and calls and create transcripts and summaries.

If AI also suggests updates to record fields from the recording, and the CA checks and approves them before they are applied, you have the “AI drafts, people approve” flow as is. Decide in advance which fields update automatically and which go into the record only after approval.

Before recording, tell the candidate that you will record and get their agreement. Summaries are also hard to search or aggregate as plain text, so move them into fields such as desired salary and job change timing before using them.

4. Documents: draft recommendation letters and job descriptions, and import resumes

For recommendation letters and job descriptions, AI drafts them from the information in the records, and the CA or RA checks and finishes them. Less time spent working out how to begin leaves more time for checking facts and wording.

AI can also reduce the work of reading career history and qualifications from received resumes and moving them into record fields. In some systems, AI reads imported documents and fills in field values. Values read by AI can still contain errors, so the person in charge checks them before use.

If you set an internal format for career sheets and recommendation letters, and create them by fitting record information into that format, you will not need to fix the layout every time.

5. Matching: list candidates, and learn from why placements fell through

Searching for job openings or candidates that match criteria is easy to hand to AI or business systems, as long as the criteria are written consistently. If you can filter on the same criteria fields both when finding jobs for a candidate and when finding candidates for a job, CAs and RAs can talk while looking at the same candidate list.

Distance and commute time to the work location are also worth checking when choosing candidates. Some systems show addresses and work locations on a map and let you filter by distance.

Analyzing why placements fell through or were declined is another task AI can help with. First, AI reads selection results and reasons for withdrawal or rejection from emails, call and consultation transcripts, and existing records, and enters them into fields. The person in charge checks and approves the values, and only then are they finalized as records. Once results and reasons are in place, you can have AI summarize and classify the records to show trends by job type and salary band. CAs and RAs decide how to read those trends and how to change the way they introduce candidates and choose job openings.

6. Business development: research companies and keep information up to date

Results in RA sales depend on timing, so it is important to find companies that are stepping up hiring early and to keep information current. Having AI research company news and hiring activity reduces the work of researching companies one by one. Some systems can fill in company fields automatically.

However, AI only provides candidate companies and conversation starters. The RA, who knows each company’s situation, decides which companies to approach, in what order, and what to say.

7. Management metrics: check consultation rate, cost per consultation and cost per placement every week

The key numbers for running a recruitment agency are the consultation rate, the cost per consultation, and the cost per placement. Lining these up by acquisition channel and by CA shows where to take action.

Aggregation itself can be automated with CRM dashboards, so use AI to help find the reasons the numbers moved. For example, if AI classifies reasons for withdrawal, you can track in numbers at which stage people drop out.

The thinking behind consultation rate and cost per consultation is covered in detail in The Complete Guide to Recruitment Marketing and how recruitment agencies attract candidates.

What AI makes newly possible

AI does more than help with existing tasks. It makes possible analysis of records that people could never keep up with, and responses that were given up because of limits on time and headcount.

Until now, there was not enough time to reread every consultation record or to write messages tailored to each person. With AI handling drafting and organizing, efforts like the following come within reach.

1. Find combinations that tend to lead to placements across all consultation records

Until now, consultation records were rarely reread by anyone other than the CA in charge. If AI summarizes and classifies the records, you can look for trends across the whole company’s records. Examples are which combinations of candidates and job openings tend to lead to placements, and where withdrawals tend to happen.

However, what AI shows are possible trends. Working out why they happen and changing how candidates are introduced is the role of CAs and RAs.

2. Find companies that are stepping up hiring early

It was not realistic for people to follow company news and changes in how companies post jobs every day. If you have AI research company information regularly and flag signs of stepped-up hiring, RAs are less likely to miss the right time to reach out.

3. Reply right away to registrations at night and on weekends and holidays

People who registered at night or on a weekend or holiday sometimes could not be contacted until the next business day. AI can draft replies, and fixed content such as guidance on scheduling a consultation can be sent as is, according to rules. Replies to individual questions, on the other hand, are sent only after the person in charge approves them. Set rules that separate where people approve.

4. Send past registrants messages tailored to each person

When contacting people who registered in the past, the best you could do was send the same message to everyone at once. With AI, you can draft a different message for each person, matched to their desired job type and what was discussed in their last consultation, for a large number of people. Before sending, the person in charge checks the recipient criteria and whether each person has agreed to receive messages, and approves the message.

5. Capture how consultations are run in a form new CAs can learn from

How high-performing CAs ask questions and make proposals used to live in their own experience. It was passed on to new CAs mainly by sitting in on consultations or by word of mouth. If AI organizes consultation records and transcripts, you can write up common questions and ways to speak to people who are hesitating, in a form others can read. Experienced CAs check that the write-up matches real consultations before it is shared internally.

All of these efforts assume that the context given to AI is in place. If consultation content and selection results are recorded differently by each person, or candidate and job opening records are not connected, AI cannot show trends correctly. So first standardize the format of all your data and connect your records.

AI matching and AI agents: what they can do, their limits, and human checks

AI matching and AI agents are good at listing candidates and carrying out fixed steps. People decide whether to recommend and how to communicate.

What AI matching can do

AI matching is a mechanism that lists job openings or candidates likely to fit, based on criteria such as job type, work location, salary and experience. Because it filters mechanically by criteria, it is faster than people comparing lists and misses less. Some mechanisms can also show a reason for each candidate.

Also, if AI reads selection results and reasons for withdrawal from records, and the person in charge approves and saves them, you can use AI to find trends in combinations that did not lead to placements. CAs and RAs decide how to read the trends and what to change.

Limits of AI matching

• It cannot see information that is not in the records: The candidate’s hesitations, the atmosphere of the workplace, and fit with a manager cannot be used unless they are recorded.
• Inconsistent wording causes misses: If “Sales,” “Corporate sales” and “Sales rep” are entered separately, matching candidates will not appear.
• It is pulled toward past results: Candidates tend to lean toward past placement trends, which makes new combinations less likely to appear.

What AI agents can do

An AI agent is a mechanism where, given a goal and steps, AI carries out work within the rules you set. Some AI-powered CRMs and business systems let you hand routine tasks to agents. Some also let you build your own agent by deciding what data it uses and how it answers. Which agents or features to combine depends on each company’s work, contract and settings.

Some agents let you choose whether a person checks each generated message before it is sent, or whether messages are sent automatically. Messages to candidates and client companies are seen directly by the other party, so it is safer to start with a setting where AI drafts and a person approves before sending.

3 points people check before recommending

1. Facts: Do the recommendation letter and summaries written by AI match the consultation records and the candidate’s documents?
2. Wishes: Even if the job matches the criteria, does the candidate actually want it?
3. Fairness: Are candidates being filtered by criteria unrelated to selection, such as gender or age?

Recording matching results, selection results and reasons for withdrawal improves the accuracy of the next candidate selection. How to structure records is explained in how to run recruitment operations on a CRM.

Risks and safeguards when using AI in recruitment

Recruitment agencies handle candidate information, so before using AI, cover five points: personal information, data handling, accuracy, fairness and access rights.

Each of these extends rules that recruitment agencies have always followed. But as AI is given information more often, it is important to decide in advance where things are checked and by whom.

1. Personal information: use it within the purpose of use, and decide what goes into AI

Candidates’ personal information is handled under the Act on the Protection of Personal Information, as well as the rules of the Employment Security Act and the guidelines of the Ministry of Health, Labour and Welfare. The guidelines state that candidates’ personal information is to be collected within the scope necessary to achieve the purpose of the business, and that its storage and use are also to be limited to the purpose for which it was collected.

The alert issued by the Personal Information Protection Commission calls for caution when entering prompts that include personal information into generative AI. Specifically, it asks businesses to fully confirm that doing so is within the scope necessary to achieve the purpose of use. It also points out that if personal data entered without the person’s consent is handled for purposes other than producing the response, this may violate the Act on the Protection of Personal Information.

So decide internally which fields may be given to AI, and confirm that this matches the purpose of use you have shown candidates. Information that the guidelines say must not be collected in principle, such as race, registered domicile, and thought and creed, should not be recorded in the first place.

2. How the AI provider handles data: confirm in the contract whether data is used for training

When choosing AI tools, check the terms of use and the contract to see whether entered data is used to train the AI, and where it is processed. The Personal Information Protection Commission’s alert also asks businesses to fully confirm points such as that the provider does not use entered personal data for machine learning.

Some AI-powered CRMs and business systems let you choose in the settings whether your data is used to improve the AI. Some also let you limit the types of data AI can access. When implementing a system, check whether these settings exist and what the defaults are.

3. Accuracy: send AI drafts only after a person approves them

Generative AI can output content that is not in the records, or inaccurate information. The Personal Information Protection Commission’s alert also mentions the risk of inaccurate personal information being output. So the person in charge checks AI drafts against the facts and approves them before sending. For recommendation letters, check against the consultation records and the candidate’s documents. For job descriptions and messages to candidates, check against the job opening received from the client company.

Working conditions in particular must be clearly disclosed to candidates under the Employment Security Act, and the same guidelines state that they must not be false or exaggerated. From April 2024, items such as the scope of possible changes to duties and place of work were added to the items that must be disclosed. When drafting job descriptions with AI, do not change conditions such as salary and work location from the original job opening.

4. Fairness: do not let AI filter by criteria unrelated to selection

As the basics of fair hiring selection, the Ministry of Health, Labour and Welfare asks employers to keep their doors widely open to applicants and to select them on criteria based on aptitude and ability. The Ministry’s FAQ states that this approach must be followed in selection by any method, including the use of AI.

Matters the applicant is not responsible for, such as registered domicile and family, and matters related to thought and creed, such as religion and political party support, are not to be used as grounds for hiring decisions. When having AI list candidates, set it up so that gender, age, family situation and similar factors are not used as filters, and have the CA make the final decision on whether to recommend.

5. Access rights and logs: decide who can see which candidates’ information

Under the guidelines, employment placement business operators are to establish rules for the proper management of personal information. These rules set out matters such as the range of people who may handle personal information. When using AI, decide who can see which candidates’ information in line with these rules.

When choosing a CRM or business system, check whether you can set the range of records each user can see. Also check whether viewing and export actions are logged. When a person in charge leaves or changes roles, transfer their records to another user and then disable their login.

Checklist before introducing AI

Before introducing AI into your work, confirm the following internally.

• Does the purpose of use of personal information shown to candidates include processing by AI?
• Have you decided which fields may and may not be entered into AI?
• Have you checked in the terms or contract whether the AI tool uses entered data for training, and where it processes data?
• Have you decided who approves recommendation letters, job descriptions, messages to candidates, and record updates suggested by AI?
• Is there a procedure for checking working conditions in job descriptions against the job opening received from the client company?
• Is the system set up so that candidates are not filtered by gender, age, family situation and similar factors?
• Have you set permissions for who can see which candidates’ information?
• Have you decided on a procedure for disabling users when people leave or change roles?

These risks are why NowVillage starts with the data foundation and the points where people approve, before AI. Once it is clear which information is held where, it becomes easier to decide which fields to give AI, who can see them, and who checks.

This section is a general explanation based on public information from government agencies (checked October 20, 2026). For decisions on individual cases, please consult experts such as your local Labour Bureau or a lawyer.

5 steps to start using AI in recruitment

The first thing to decide is not which AI tool to use, but which tasks to hand to AI and what data those tasks need.

1. List the tasks that take time: Lay out a week of CA and RA work and check how much time goes into data entry, copying information and preparing documents.
2. Decide which tasks to hand to AI and where people approve: For each task, decide who approves AI drafts and suggested updates, and when.
3. Standardize data formats and build context: Decide where each piece of information is held as the “source of truth,” and standardize fields that can be options, such as job type, work location and selection stage. Merge duplicates and connect candidate, job opening, company and application records so AI can grasp the background.
4. Bring everything into one database and set up AI: Bring candidates, client companies, job openings and applications into one place, such as an AI-powered CRM. Then build AI into the flow of your work.
5. Measure the effect in numbers: Compare the consultation rate, cost per consultation, cost per placement, and the time CAs could spend on consultations with the figures from before you started.

Before giving candidates’ personal information to AI, check what information AI will be allowed to use and how the tool handles data. Turn what you decide into internal rules, and use the checklist in the previous section for what to check.

NowVillage builds with HubSpot’s AI and its own extension apps

NowVillage brings the seven areas of recruitment work into one HubSpot, and combines HubSpot’s AI with its own extension apps to put AI to work.

NowVillage’s support covers HubSpot, the AI built into HubSpot, and our own extension apps that run on HubSpot.

NowVillage is a HubSpot Diamond Partner. We were ranked 1st in Japan and 4th in Asia-Pacific in the “AI Excellence” category of the HubSpot Solutions Partner Impact Awards (FY2025 performance), announced in February 2026.

What HubSpot’s AI can do for recruitment work

HubSpot’s AI is customized to fit recruitment work. We set up record properties, instructions to AI (prompts), approval rules, agent behavior and more to match the flow of your work. The main uses, and where people approve, are as follows.

What AI can do	Example use in recruitment	What people approve
Summaries: summarize records, notes and conversations	Grasp a candidate’s background before a consultation	Whether the summary matches the records
Drafts: write copy for emails, blog posts, pages and social media posts	Messages to candidates, job ads, blog drafts	The copy before it is sent or published
Property entry and suggested updates: produce property values and suggested updates from meeting recordings, email and call records, and files	Enter consultation content, resumes, and selection results and reasons into records	The values applied to records
Research: research company news and the technologies companies use	Research companies before business development	Which companies to approach and what to say
Duplicate detection: find likely duplicate records and formatting issues	Prevent the same candidate or company from being registered twice	Which record to merge into
Agents: carry out work within the rules you set	First response to common questions, routine tasks	The scope to hand over, and where people approve

For example, in consultations, AI creates a transcript and summary from the meeting recording and suggests updates to record properties. The CA checks the values, then approves or corrects them before they go into the record, so you reduce data entry while keeping records accurate.

For documents, a feature called smart properties lets AI read an uploaded resume PDF and fill in property values. The same feature can also read selection results and reasons for withdrawal from emails, call and meeting transcripts, and notes. NowVillage sets this up so the person in charge checks and approves the values AI entered before they are finalized as selection records.

For acquisition, HubSpot’s built-in AI drafts the body of blog posts, emails and pages, as well as social media posts based on published articles. The person in charge checks them before publishing.

Extension apps cover tasks specific to recruitment

Tasks specific to recruitment, such as linking job openings and candidates, filtering by commute and creating documents in a set format, are covered by NowVillage’s extension apps.

App	What it does
Matching Extension	Search candidates and job openings in both directions, and create application records in bulk
MAP Extension	Show addresses and work locations on a map, and filter by radius or travel time
PDF Extension	Create recommendation letters, job descriptions and career sheets in PDF, Word or Excel from record information

To filter by ease of commute, use MAP Extension to show the candidate’s address and the work location on a map and filter by distance. Career sheets and recommendation letters are created by merging record information into a set format with PDF Extension.

AI and the extension apps run on the same data, so they can be used together. For example, a recommendation letter drafted by AI is approved by the person in charge and then merged into a PDF Extension template. Candidates listed by Matching Extension are checked by the CA before being recommended.

AI’s answers depend on context

What we do not recommend is loading everything into HubSpot at once without standardizing data formats. If you load data without consistent formats, the same companies and candidates are duplicated, and job types and work locations are written differently by each person. Records are not connected either, so AI cannot grasp who is connected to which job opening and how, and its summaries and suggested candidates are off.

So before loading, decide which record holds each piece of information as the “source of truth.” At the same time, do not leave fields that can be options, such as job type, work location, selection stage and reason for withdrawal, as free text, and standardize the format of all your data. Then connect candidate, company, job opening and application records in HubSpot to build context that AI can use.

Which features and apps to combine depends on each company’s work, how it holds data, and its contract. That is why NowVillage first learns about your current workflow, then designs with you which tasks to hand to AI and where people approve.

An overview of the AI features is in this Marketing Spot article, and how to run the work is in how to run recruitment operations on a CRM.

Feature details are based on public information as of October 20, 2026. Available features depend on your contract and settings.

Case studies: building the data foundation for AI

NowVillage has brought recruitment agencies’ data into one CRM and built the foundation for using AI and extension apps.

Migi Naname Ue Inc.: Placements per CA up 120%

The company managed candidates, job openings, companies and selections in spreadsheets. NowVillage defined each record and how they connect, and built them in HubSpot. We also set up automatic task assignment, reminders, and dashboards for conversion rates and lead times.

As a result, placements per CA were up 120% and the placement rate was up 140% (Migi Naname Ue case study).

Blackbelt Inc.: Moved from Salesforce, ending double management and manual work

Blackbelt, a recruitment agency specializing in healthcare, managed selections in Notion as well as in Salesforce. Along with the move to HubSpot, we set it up so that candidates and client company contacts are managed together as contacts. We also built search with Matching Extension and a weekly KPI dashboard.

Double management with Notion is no longer needed (Blackbelt case study).

Each of these is an individual case and does not guarantee the same results.

FAQ

Q. Will AI make recruitment agencies obsolete?

No one can say for certain where the industry as a whole is heading. What AI is replacing now is tasks such as searching by criteria, entering records and drafting documents. So the difference between companies is likely to come from whether they can put the freed-up time into consultations that draw out candidates’ real feelings and into building relationships with client companies.

Q. What can AI make possible that we could not do before?

Examples include finding combinations that tend to lead to placements across all consultation records, and replying right away to registrations at night and on weekends and holidays. Messages tailored to each past registrant, and capturing how consultations are run in a form new CAs can learn from, also come within reach. All of these were hard because people did not have enough time. Messages that reach the other party are sent only after the person in charge approves them.

Q. How can we improve the accuracy of AI matching?

Accuracy depends less on the mechanism itself than on whether the context given to AI is in place. Having “Sales” and “Corporate sales” entered separately is enough for matching candidates to be missed, so first standardize the options and connect candidate and job opening records. If AI also reads selection results and reasons for withdrawal from records, and the person in charge approves and saves them, you can apply trends in combinations that did not lead to placements to the next candidate selection.

Q. Is it safe to enter candidates’ personal information into AI?

Decide only after confirming that it is within the purpose of use shown to candidates, and that the tool does not use entered data for training. The Personal Information Protection Commission also asks businesses to fully confirm that entering personal information into generative AI is within the purpose of use. Then decide internally which fields may be entered into AI, and get the candidate’s agreement when recording consultations.

Q. Can we send recommendation letters and messages written by AI as they are?

Do not send them as they are. The person in charge checks and approves them before sending. AI can write content that is not in the records, so check recommendation letters against the consultation records and the candidate’s documents, and check working conditions against the job opening received from the client company.

Q. How much recruitment work can we hand to AI agents?

You can hand them tasks where steps and rules can be defined, such as the first response to common questions and routine messages. Judgments that depend on the other party’s situation, such as whether to recommend or how to negotiate conditions, stay with people. For messages that go directly to the other party, it is safer to start with a setting where a person approves before sending.

Q. What can HubSpot’s AI do for recruitment work?

It can summarize consultations, suggest record updates, draft recommendation letters and emails, research companies, and more. Properties, approval rules and other settings are customized to fit recruitment work, and NowVillage’s extension apps cover tasks such as searching job openings and candidates. For details, see the section on how NowVillage builds it.

Q. Where should we start to use AI in recruitment?

First, list the tasks that take up CA and RA time, and decide which tasks to hand to AI and where people approve. Next, standardize data formats such as job type, work location and selection stage, merge duplicates, and connect records. If you manage data in spreadsheets, bringing it into one database before adding AI makes summaries and suggested candidates less likely to be off.

Summary: build the “AI drafts, people approve” flow on a data foundation

What AI is changing most now is the tasks in recruitment. By reorganizing work into a flow where AI drafts and people approve, CAs and RAs can spend their time on “listening, deciding and persuading.”

There are seven places to use AI: acquisition content, follow-up after registration, consultation records, documents, matching, business development, and management metrics. In every one, AI creates drafts and suggested updates, and the person in charge checks and approves them before they are sent or saved to records.

AI also makes possible efforts you could not get to before. Examples include finding trends across all consultation records, and replying right away to registrations at night and on weekends and holidays.

However, the quality of AI’s answers depends on the background information AI uses for its judgments, in other words, the context. So standardize the format of all your data, such as job type, work location and selection stage, connect the records, and bring them into one database. Then combine the AI-powered CRM and systems that fit your work.

Drawing on its experience supporting more than 150 recruitment companies, NowVillage helps recruitment agencies build their operations with HubSpot and HubSpot’s AI. If you are considering using AI in HubSpot, feel free to contact us.