Retrieval Readiness
Your documents and tables prepared so an AI can actually find the right piece — cut into sensible chunks, labeled, permission-checked, and tested against real questions.
A pile of boxes in a garage and a library can hold the same books — but only one lets you find anything. Retrieval readiness is turning the pile into the library: every piece labeled and indexed, borrowing rules attached — so when the AI goes looking, it comes back with the right page, not a random one.
The problem, in plain words
You connect an AI assistant to the company's documents and ask it a question you already know the answer to. It comes back confident — and wrong. Not because the model is bad, but because the answer lives in the middle of a 90-page PDF, the search pulled a paragraph from an outdated draft of it, and nothing told the AI which version was current, who wrote it, or whether the person asking was even allowed to read it. The data was all technically there. It just wasn't findable, fresh, or safe to retrieve.
What we set up
We prepare the content for retrieval (the AI's document search). Documents are cut into pieces (chunks) sized for their type — long reports differently from tables, transcripts, or code. Each piece gets a label: source, owner, date, and sensitivity classification. Each piece also gets a machine-readable fingerprint of its meaning (an embedding), refreshed on a documented schedule so the search doesn't drift stale. Searches then run two ways at once — by meaning and by exact keywords (hybrid retrieval) — and a second pass (reranking) sorts the candidates so the best match comes first. Finally, we build a test set from real questions people actually ask, so retrieval quality is measured, not guessed.
How it works, step by step
- Cut content to fit
Long documents, tables, transcripts, and code each get their own chunking strategy — pieces big enough to make sense, small enough to be found precisely.
- Label every piece
Source, owner, date, and classification travel with each chunk — so answers can be cited, filtered, and permission-checked.
- Fingerprint the meaning, on schedule
Embeddings are refreshed on a documented cadence, with versioning, so the search reflects today's documents — not last quarter's.
- Search two ways, then sort
Meaning-based and keyword search run together, and a reranker orders the results — because the right answer sitting in third place is almost as bad as absent.
- Respect the permissions
Retrieval applies the same access rules as everything else. An agent finds only what its user is allowed to see.
- Test against real questions
A standing eval set of genuine queries measures whether retrieval is getting better or worse with every change.
What changes for you
Before: the AI answers from whatever the search happened to surface — sometimes right, sometimes an outdated draft, with no way to tell which. After: it cites the current version of the right document, with the source attached, and only from material the asker may see. Retrieval quality becomes a number you track, not a complaint you field. What it won't do: it won't conjure answers your documents don't contain — and if the documents are wrong or outdated, it will faithfully retrieve wrong, outdated content. Fixing the library's books is still an editorial job.