AI-powered apps / case 01
OptiTalent
Evidence-first recruiting workflows
A multi-tenant recruitment product that turns candidate search, evidence review, shortlist decisions, controlled sharing, and recruiter feedback into one coherent workflow.
- Classification
- Independent Project
- Delivery status
- Independent product / active validation
- Full record
- Open canonical case
The problem
Recruiting teams need to move quickly without turning matching into an opaque score or losing the evidence behind a decision.
System arrangement
How the system works
Confirmed role constraints fan into exact and semantic recall lanes. Candidate-level RRF builds the pool, ColBERT refines it, and evidence policy controls what can reach a recruiter shortlist.
- 01SourceRole intent + constraints
Confirmed eligibility compiles first
- 02CapabilityPGlite exact + lexical
Canonical facts, filters, and FTS
- 03CapabilityQdrant dense + sparse
BGE-M3 versioned derived index
- 04CapabilityCandidate-level RRF
Fuses only executed recall lanes
- 05CapabilityColBERT MaxSim
Reranks the fused candidate pool
- 06ControlEvidence policy
Unknown stays unknown, not excluded
- 07OutcomeRecruiter shortlist
Evidence remains reviewable
- 08ControlTenant-local compute
Redacted subjects stay close to data
My role
I defined the product boundaries, tenant model, evidence contracts, recruiter workflow, review gates, and release acceptance. AI agents produced most implementation code under those constraints.
Verification evidence
- •
Public product surface at app.optitalent.cc.
- •
Implemented candidate search, candidate evidence, shortlist, and sharing contracts.
- •
Tenant-scoped retrieval and explicit review states are present in the product runtime.
- •
LightGBM LambdaRank was evaluated against deterministic baselines on a frozen retrieval protocol and was not selected as the production winner for that feature shape.
Safety and architecture decisions
- 01
Prefer evidence-first retrieval and explicit unknowns over a single opaque LLM judgment.
- 02
Place bounded embedding and retrieval work close to tenant data while preserving server-side policy and review authority.
- 03
Keep the policy and lifecycle authority behind stable contracts while interfaces render approved state.
- 04
Treat agent and human entry points as adapters over the same capabilities.
Capabilities
- 01
Hybrid lexical and dense candidate retrieval
- 02
Evidence-preserving reciprocal-rank fusion
- 03
Local embedding and Qdrant recall runtime
- 04
Candidate evidence and explainable match signals
- 05
Draft and approved shortlist states
- 06
Controlled sharing and feedback
- 07
CLI, MCP, desktop, and web access paths