Type the brief. Get ranked, explained candidates.

Describe the role in plain English ('night-shift ICU nurse, 3+ years, within 20 km, immediate joiner') and the search copilot parses it into locked must-haves and nice-to-haves, runs lexical and semantic retrieval together, and returns candidates ranked by an explainable score, each with an honest 'why this fits' and the gaps named. Rediscovery flags people already in your database before you pay to source them again.

2

retrieval arms: lexical + semantic, merged

measured

15pt

hard cap on the semantic nudge over the explainable score

measured

Criteria you can see and lock

The parsed brief renders as editable chips: lock a criterion to make it a hard filter, relax it to a preference. Re-ranking happens server-side, honestly.

Explainable ranking, capped semantics

The backbone score is deterministic (role, seniority, skills, location, experience); semantic similarity adds at most a bounded nudge. No candidate is ranked by vibes.

Honest fit notes

Each result carries a grounded why-this-fits plus the gap ('no ICU experience listed'), generated from the actual profile. When the AI is down, results still rank; notes just wait.

Rediscovery first

Candidates already in your pipelines or database get badged, because your best next hire is often someone you already screened for a different role.

Versus Naukri Resdex boolean search

Resdex offers a hundred million resumes behind boolean syntax; volume is its moat and its noise. Neuradesk searches living, structured profiles with visible criteria, explainable scores, and named gaps, and it learns from your outcomes because your hires happen in the same graph. Smaller pool, radically higher signal per minute.

How it works

1Type the brief in plain language.
2Review the parsed chips; lock must-haves.
3Read ranked results with fit notes and gaps.
4Shortlist, message, or launch outreach without leaving the page.

Questions, answered

Which candidates can my search see?

Only candidates whose visibility settings permit it: public profiles, or candidates who included your org. Consent gates the data path itself, per India's DPDP Act; deepening never widens.

Why is the semantic score capped?

So rankings stay auditable. The deterministic backbone (skills, seniority, location, experience) explains itself; embeddings add a bounded nudge and can never quietly override the visible criteria.

What happens to my search when the AI provider is down?

Parsing falls back gracefully and ranked results still return; the natural-language fit notes wait. Deterministic backbone first is the whole design.

Talent Search Copilot

Rolling out; included with Growth and Scale AI features. Start free; upgrade when the volume does.

Start hiring free