AI consultant staffing matcher
An end-to-end retrieval pipeline for matching consultants to client engagements based on skills, experience, and project fit.
Problem
Staffing the right consultant on a project relies on tacit knowledge that is hard to surface across a large pool of CVs and past engagements.
Approach
Built an end-to-end pipeline: CV ingestion and parsing, structured extraction with LLMs, semantic indexing in ChromaDB, hybrid retrieval using reciprocal rank fusion, and an explainability layer that surfaces why each candidate was matched.
Validation
Iterated on retrieval quality using realistic staffing queries and inspected ranking explanations to confirm matches reflected meaningful overlap rather than surface-level keyword similarity.
Outcome
A working prototype delivered as a live panel demo, showing how retrieval and explainability can be combined to support staffing decisions.