Staffing and HR technology
Search and screening that reads the whole record, not just the keywords in it.
A staffing and HR technology client had candidate and company information spread across several systems and searched by keyword. Keyword search returns the records that happened to use the same words, not the candidates who fit the role. We replaced it with conversational, context-aware search and embedded an assistant in the product, grounded in the client's own data.
Underneath, the search infrastructure was rebuilt so retrieval stays fast as volume grows, and every model call is observed and evaluated so output quality is measured rather than assumed.
The situation
- Candidate and company information was spread across several systems and searched by keyword.
- Screening meant reading long lists of records to find the few that mattered.
- Recruiters spent their day filtering rather than talking to people.
What we built
- Conversational, context-aware search across candidate and company records.
- An assistant embedded in the product, grounded in the client's own data.
- A search infrastructure upgrade underneath, so retrieval stays fast as volume grows.
- Observability and evaluation on every model call, so output quality is measured rather than assumed.
What changed in daily operations
- Recruiters start from a ranked, explainable shortlist instead of an unfiltered list.
- Time released from filtering goes back into candidate conversations.
- Model behavior is monitored in production, so regressions are caught rather than reported by users.
Related work
Does this look like your process?
Book a free consultation and we will tell you whether this pattern fits, and what it would take to ship the first working version against your own data.