01 · Problem
What wasn't working
Sourcing teams spent most of their week on repetitive screening, missing strong candidates buried in backlogs.
02 · Solution
How we approached it
An AI-assisted sourcing layer that scores, clusters, and surfaces candidates — with guardrails against bias and cost.
03 · System Design
Architecture &
key features.
Architecture
- LLM scoring with eval harness
- Retrieval + candidate memory
- Orchestration queue
- Cost + latency SLOs
Features
- Resume + skill scoring
- Interview orchestration
- Bias-aware ranking
- Cost-bounded inference
04 · Results
Numbers measured in production, not on a pitch deck.
+64%
Growth in shortlist quality
40%
Recruiter hours returned
99.9%
Pipeline uptime
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