Hybrid semantic retrieval where precision and recall both matter

Challenge

Pure vector search misses exact matches; pure keyword search misses meaning. For knowledge where both precision and recall matter, neither approach alone is good enough — and teams rarely have visibility into why a retrieval went wrong, so they can’t fix it.

Approach

We delivered hybrid retrieval that combines keyword precision with semantic recall, then made it observable. Every query exposes what was retrieved and how it scored, so retrieval quality becomes something the team can measure and tune deliberately — rather than guess at.

System design

• Hybrid retrieval blending keyword and vector search
• Re-ranking to surface the most relevant evidence
• Tunable parameters exposed for deliberate quality control
• Observability into retrieved candidates and their scores

What we delivered

• A hybrid search system balancing precision and recall
• Tooling to inspect and tune retrieval behavior
• A measurable foundation for grounded, accurate answers
• Retrieval quality that improves with evidence, not intuition

Why it mattered

Retrieval quality is the ceiling on answer quality. By making it observable and tunable, the system turns a black box into an engineering surface — one the team can keep improving as the corpus and use cases grow.