Retrieval Readiness
Tables, documents, and indexes prepared for agent retrieval — chunked, embedded, metadata-tagged, permission-resolved, and tested.
Most enterprise data is not retrieval-ready. The shape that works for OLTP or BI is rarely the shape an agent should retrieve. Retrieval-readiness is the discipline of making it retrievable safely and accurately.
What it solves
Bridges the gap between 'we have the data' and 'an agent can find and cite the right piece of it'. Makes retrieval quality a function of the substrate, not the prompt.
How we build it
Chunking tuned per content type (long-form documents, structured tables, transcripts, code). Embeddings refreshed on a documented cadence. Metadata for every chunk (source, owner, date, classification). Hybrid retrieval (dense + BM25) with reranking. Eval set built from real queries against the corpus.
- Per-content-type chunking strategy
- Refresh cadence with versioning
- Metadata for every chunk
- Hybrid retrieval with rerank and eval
What changes when it is in place
Agents start citing the right document with the right freshness and the right permissions. Retrieval quality becomes a measurable trend, not a complaint.