Solutions
Solve the search problems your agents hit.
Relevan helps engineering teams move from keyword retrieval to explainable, self-improving search built for AI agents.
Agentic RAG Systems
Autonomous agents that retry blindly on a failed query burn tokens and add latency, and a wrong retrieval can send the whole reasoning chain off course.
- Explanation signals show which field or phrase drove the match, so retry logic can target the fix instead of re-running the same query blind.
- Wire the Explainable Search API directly into your orchestration layer’s retry logic.
- Cut round-trips: agents fix bad queries in one step instead of full-context re-prompts.
Developer Search Platforms
Teams that expose search to coding agents or internal tools need the index schema to stay visible, and they need to ship relevance fixes without a redeploy.
- Query the Schema Exploration API at runtime so agents plan against real fields, not stale docs.
- Trace a bad result back to the query and the schema state that produced it.
- Push a relevance fix through the Feedback and Tuning API instead of a full re-index.
Enterprise Knowledge Search
Large organizations index documents, products, and profiles from many systems, each with its own schema, and audit teams need to know why a result surfaced.
- Query across mismatched document schemas without a shared upfront model.
- Keep a traceable record of why each result ranked, for audit and compliance review.
- Roll continuous relevance updates into production catalogs without downtime.
- Run at enterprise scale without staffing a team to tune and scale the search cluster.