Hybrid RAG that matches candidates to roles in seconds.

A hybrid lexical + semantic retrieval system that ranks candidates against job requirements with cited evidence from the resume.

9×faster shortlist generation per requisition

At a glance

The challenge, approach & outcome.

Challenge
An IT staffing firm's recruiters were shortlisting candidates by hand, taking 4–6 hours per requisition. Generic ATS keyword matching missed strong candidates and surfaced weak ones. Recruiters didn't trust pure-LLM matching — too many opaque false positives.
Approach
Built a hybrid RAG (BM25 + dense embeddings) that ranks candidates against job requirements and surfaces cited evidence from the resume for each match factor. Recruiters review the evidence, not the model's opinion. Continuous feedback loop tunes the ranker against actual hire outcomes.
Outcome
Shortlist generation 9× faster, interview-to-offer conversion up 38%, and 100% of decisions backed by cited resume evidence. Recruiters reported trusting the system after week two.

Deliverables

Stack

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