We run a live search with you, end to end. Nothing is sent to candidates.
Book a demo (opens in a new tab)The closest like-for-like comparison we have. It turns on where the candidates come from, how a must-have is judged, and where the process ends.
Naukri AI REX sources, calls and screens candidates from Naukri's jobseeker database and ends at a shortlist with interviews scheduled. EmployLabs searches professional-profile data beyond any job board, judges each must-have on evidence, and ends at a structured voice interview with a report. Choose AI REX when the right people are active on Naukri; choose EmployLabs when they are not.
Naukri’s AI REX is the product closest to what we do. Naukri describes it as an agentic AI talent-sourcing platform, introduced in April 2026, that takes a requirement from job opening to shortlist. The recruiter enters the designation, location, skills, experience and salary; AI REX shows sample matches, learns from Good fit, Maybe and Not a fit feedback, writes the job description and an editable screening questionnaire, then calls, messages and screens candidates from the Naukri database.
We do much of the same work: a sample before anything runs at volume, a phone call, screening questions, a hiring-manager review. So the honest distinction is not that one of us is agentic and the other is not. It sits in three places: where the candidates come from, how a must-have is judged, and where the process ends.
AI REX works the people already on Naukri and ends at a shortlist. EmployLabs searches beyond any job board and ends at an interview report.
If every role you hire for sits in the first group, AI REX is built for you and you can stop reading here.
| Outcome | Naukri AI REX | EmployLabs |
|---|---|---|
| What lands on your desk | A shortlist of Naukri candidates who were matched, called to check interest, screened on your questions and scheduled for interview, shared with the hiring manager for review. | Candidates found across professional-profile data and our own jobs board, contacted, screened and interviewed by voice, each arriving with a report that quotes what they said. |
| What you still do yourself | The interviews, and the judgement of who is genuinely strong once the shortlist arrives. | Arm the role with its scope and caps, answer the interventions that need judgement, then the hiring-manager conversation, the client rounds and the decision. |
| Time to the first useful thing | Sample matches as soon as the requirement is entered, from a database that is already there. | A sample of real profiles before anything is sourced at volume; interviewed candidates follow once people have been found and reached. |
| What you keep afterwards | A shortlist and the calibration you gave the matching for that requirement. | The pool, the scoring with its evidence, the interview reports and a decision record for every automated step. |
| Where it fails you | When the person you need is not active on Naukri. Matching can only rank the database it searches. | When the brief is wrong. Aimed at a badly specified role it produces confident activity and no hires, which is why the sample comes first. |
Outcomes are what you end up with. This is the work itself, job by job. Where Naukri’s published product material does not describe a capability, the row says so rather than reading an absence into it.
| Finding and deciding | Naukri AI REX | EmployLabs |
|---|---|---|
| Turn the brief into a requirement | The recruiter enters designation, location, skills, experience and salary; AI REX generates the job description and an editable screening questionnaire from it. | Meera, the recruiter copilot, turns the job description into one role definition that drives both the search and the scoring. Each must-have is recorded once in the JD's own words and marked as checkable from the profile or only from the candidate. |
| Calibrate before anything runs at volume | Sample matches are shown up front, and Good fit, Maybe and Not a fit feedback refines the matching in the product. | Meera proposes several sourcing strategies and the recruiter previews a sample of real profiles for each. Automation will not source from a strategy nobody sampled. |
| Find the candidates | Matched from the Naukri database, including high-potential candidates surfaced before they apply. Sourcing outside Naukri's own database is not described in published product material. | People search across major professional-profile data providers, reaching people who are on no job board, in a pool that pages forward and never buys the same person twice. Applicants from our own jobs board go through the same scoring. |
| Judge a must-have | AI-screened applications based on the recruiter's questions. | Each must-have is judged three ways: met, contradicted or not stated, with the evidence line attached. Only positive evidence of a mismatch disqualifies. Silence becomes a question put to the candidate, never a reject. |
| Rank the people who pass | Matching refined by the recruiter's feedback on sample candidates. | Seven anchored, role-specific dimensions, from skill match and ownership to domain depth and company background. Change the brief and everyone it can affect is re-scored automatically. |
| Reaching, assessing, handing over | Naukri AI REX | EmployLabs |
|---|---|---|
| Reach the candidate | Automated calling that pitches the role and checks interest, with a pitch the recruiter edits and can test-call; email, WhatsApp, the Naukri inbox, app notifications and job posts. | A four-email sequence over a week: a personalised first email, a same-thread follow-up, a new-angle email and a closing note. Once the email has provably reached the candidate, and their number is Indian, one AI phone call within TRAI calling hours. Recruiters can add LinkedIn invites through a browser extension they operate. |
| Hold the conversation | The calling agent pitches the role from a pitch the recruiter edits and checks interest; messages continue on WhatsApp, the Naukri inbox and app notifications. | Zia, one conversational engine across email, WhatsApp, phone and web. It knows each candidate's strengths and gaps, answers questions about the role, uses India salary benchmarks by role, city and seniority, and asks the must-haves the profile never settled. |
| Interview the candidate | Interviews are scheduled with shortlisted candidates. A structured AI interview producing an evidence report is not described in published product material. | Naira runs a browser voice interview, multi-module with phases enforced. Technical candidates write code in a live editor and explore a real repository with Naira, who reads and discusses it; non-technical roles get document-based exercises. |
| Give the hiring manager something to act on | The shortlist is shared for hiring-manager review, with automated reminders. | A five-level alignment rank with written rationale, spoken-English level on the CEFR scale, skill depth with the candidate's own quotes, and risks with a severity, on a share link only the people you name can open. |
| Flow into your ATS | Naukri's own RMS sits alongside it. AI REX integration with external ATSs is not described in published product material. | One live connector, Zwayam, submitting finalists with a generated CV into the client's portal after a dry run. Other ATS connectors are not built. |
For each finalist, the hiring manager opens the interview report on the share link: an alignment rank on a five-level scale with the written rationale behind it, the candidate’s spoken-English level on the CEFR scale with its criteria, skill depth per skill with the candidate’s own quotes, and the risks, each with a severity. Topics the interview never probed are marked “Not assessed” rather than guessed.
Start on one role, with nothing to integrate. Nothing runs until somebody arms it, choosing the scope and the caps on sourcing, finalists and spend, and you can stop at any time with the pool, the scoring and the reports still yours.
For an Indian team hiring across levels this is a sensible configuration, and it is operational rather than diplomatic. Put AI REX on the requirements where active jobseeker volume is the answer: the mid and junior roles, the regional hiring, the positions where the right candidate has a current Naukri profile and wants the call. AI REX is sold per requirement, one position with an allocation of CVs, which makes that split natural.
Put EmployLabs on the roles where the right person is not on a job board, and on the ones where the hiring manager needs interview evidence before spending an hour. Nothing runs until a person arms the role and sets its scope and caps, so splitting requirements between the two is a decision per role, not a migration. If your client runs on Zwayam, finalists from our side reach its portal with a generated CV.
If the people you need are active on Naukri, AI REX is the nearest thing to an AI recruiter working inside the database where Indian jobseekers already are, and it is the right tool. If the person you need is not on a job board, or the hiring manager needs to see how candidates think before meeting them, EmployLabs searches beyond the database, judges every must-have on evidence, and ends at an interview report rather than a shortlist. Our sourcing is global, with India-specific compensation intelligence in addition.
Bring a live role. We will show you the search, the outreach and an interviewed finalist's report before you commit to anything.
