AI for recruitment agencies in Singapore

In short: AI can summarise CVs against a job order, surface database candidates, draft job ads and client updates, and propose interview times, but a consultant makes every candidate decision. Tools are designed around merit-based, job-related criteria under the Tripartite Guidelines.

AI for recruitment agencies works best on the reading, sorting and writing around each placement: summarising CVs against a job order, surfacing candidates a consultant may have missed, drafting job ads, arranging interviews and preparing updates for clients. In Singapore, the design matters as much as the model. Selection should rest on merit, the agency holds an employment agency licence from the Ministry of Manpower (MOM), and a consultant should make every decision about a candidate. FYAdvisers designs and builds AI tools for agencies on those terms, connected to the applicant tracking system or CRM you already use.

Where consultants lose time

A consultant’s day is split between people and paperwork. The paperwork tends to look like this:

• CVs in every format. Applications arrive as PDFs, Word files, portal profiles and email attachments, each laid out differently.
• Matching from memory. Good candidates already in the database are overlooked because nobody remembers them when a new job order arrives.
• Job ads rewritten for each role. Every requisition needs a fresh ad, and each one must be checked for fair wording.
• Interview scheduling by message. Times go back and forth between candidate, consultant and client until one sticks.
• Client updates assembled by hand. Progress notes are pulled from emails and calls into a weekly summary.

What we build for recruitment agencies

CV screening support

A tool that reads each CV, extracts structured details such as roles, dates, skills and qualifications, and summarises how they compare with the job order’s stated requirements. It shows the evidence for each point so the consultant can check it. It does not rank people on personal characteristics, and it does not reject anyone. This builds on our AI document processing work.

Candidate matching

When a new job order comes in, the tool searches your existing database for candidates whose experience fits the requirements and explains why each one was suggested. The consultant decides who to contact. We used a similar matching approach, on a much simpler problem, when we built an enquiry form with AI course matching for Jeff Learning Center, a tuition centre.

Job ad drafting

A drafting step that turns a job order into a first draft ad, focused on the duties, skills, qualifications and experience the role needs. We can add a check that flags wording TAFEP’s guidance warns against, such as age ranges or language requirements without a stated reason. A consultant reviews and approves every ad.

Interview scheduling

Calendar tools that propose slots based on the consultant’s and client’s availability, send the agreed time for confirmation and keep changes in one place. These are standard workflow automation steps with AI used only where it helps, such as reading a proposed time from an email.

Client updates

A draft weekly update for each client, built from the notes, stages and interview outcomes already recorded in your system. The consultant edits and sends it. Where your CRM or ATS offers an API, we connect to it so details are not typed twice.

The rules that shape the design

These points come from MOM and the Tripartite Alliance for Fair and Progressive Employment Practices (TAFEP), checked in October 2026. Your agency’s own compliance advice takes priority.

Fair employment practices

The Tripartite Guidelines on Fair Employment Practices start with recruiting based on merit: skills, experience and ability to do the job, not personal traits such as age, race or gender. TAFEP states that since 1 October 2020, employment agencies have had licence conditions requiring them to comply with the fair recruitment requirements in the guidelines when recruiting for clients. TAFEP’s guide to writing job advertisements sets out what to avoid, characteristic by characteristic.

Workplace Fairness Act status

Parliament passed the Workplace Fairness Bill on 8 January 2025 and the Workplace Fairness (Dispute Resolution) Bill on 4 November 2025. In its November 2025 press release, MOM said it aims for the Act to take effect in end-2027. MOM has also said the Tripartite Guidelines will continue to cover discrimination based on characteristics outside the Act. We design screening and matching tools around job-related criteria, which both expect.

Employment agency licence and candidate data

MOM licenses employment agencies and offers several licence types, and EA personnel must be registered with MOM. MOM also states that where licence conditions require an agency to collect identification such as NRIC or FIN for certain placements, the agency must not share that data with any other party, including its clients. We design tools to keep identification numbers out of AI processing and out of client updates.

FYAdvisers is not an employment agency and does not recruit or place candidates. We build and test with sample or anonymised CVs, and design the live tool to run in your own accounts, so candidate data stays under your agency’s control. For personal data in AI systems more broadly, PDPC’s Advisory Guidelines on the use of personal data in AI recommendation and decision systems are a useful reference.

Build or buy

Recruitment software built for agencies, such as Bullhorn, JobAdder and Manatal, covers candidate records, pipelines and job posting, and some vendors now include AI features. Check what your current system already offers before paying for anything custom.

A custom build makes sense when you need AI that follows your own screening criteria, works across systems that do not connect, or produces client updates in your house format. If an existing feature is enough, we will tell you.

What it costs

We outline costs after a conversation. The main drivers are:

• the number of CVs and job orders processed each month, which sets AI usage;
• whether your ATS or CRM offers an API or only exports;
• how many steps need a review screen for consultants;
• hosting, data retention rules and ongoing support.

How a project runs

1. Discovery. We map how a job order moves from intake to placement, and where consultants spend their time.
2. Prototype with your real data. We test on anonymised CVs and real job orders, so the results reflect your work.
3. Build. We start with one step, often CV summaries or job ad drafts, and connect it to your system.
4. Human review. Consultants use it on live roles, approve every output and tell us where it misreads a CV.
5. Monitoring. We check suggestions for patterns that look unfair, watch for errors and keep prompts and connections up to date.

Who this suits

• Agencies with a growing candidate database that is hard to search.
• Teams handling many similar roles where ads and updates repeat.
• Agency owners who want AI with clear review steps and records of why a candidate was suggested.

What to prepare before we talk

• The ATS or CRM you use and whether it has an API.
• Two or three anonymised CVs and a matching job order.
• A sample job ad and a sample client update.
• Your internal policy on candidate data and fair hiring.

Get in touch and we will suggest a practical first step.