Retrieval-augmented generation over your own contracts, policies, price lists, tickets and product docs. Grounded, cited, permission-aware answers instead of a model recalling something plausible from training data.
Ask a language model about your refund policy and it will tell you about a refund policy. Confident, fluent, and not yours. The fix is not a better prompt — it is putting the right passage of your actual document in front of the model at the moment the question is asked, and requiring it to answer from that.
That is retrieval-augmented generation, and it is the difference between an AI that sounds knowledgeable and one that is useful. It also unlocks the material that has been sitting uselessly in shared drives for years: the SOPs nobody reads, the six-year archive of support tickets, the pricing rules that live in three spreadsheets and one person's memory.
The interesting problems here are not glamorous. How a contract gets split into chunks decides whether a clause survives intact or gets cut in half. Whether keyword search runs alongside vector search decides whether a search for a part number works at all. Whether results are re-ranked before reaching the prompt often matters more than which embedding model you picked. We treat these as measurable engineering choices, because they are.
Different documents want different treatment. A contract chunks along clauses. A knowledge-base article chunks along headings. A support ticket is a conversation and should stay one. A scanned PDF needs its tables extracted properly or it becomes noise. We build the ingestion pipeline around your document types rather than pushing everything through one fixed splitter.
Each chunk carries metadata — source, section, effective date, owning team, permissions — because that metadata is what makes filtering, freshness and citation possible later.
Vector search is excellent at meaning and mediocre at exact strings. Ask for model number XR-4400B and pure semantic search will happily return the paragraph about XR-4400A. Keyword search has the opposite problem. Running both and fusing the results covers far more real queries than either alone.
Then a re-ranking pass scores the shortlist against the actual question, so the strongest passages land at the top of the prompt where the model attends to them most. Metadata filters run alongside — region, product line, effective date — narrowing before ranking rather than hoping the model notices.
A grounded system answers from retrieved passages, links back to them, and says it does not know when nothing relevant came back. That last behavior is the one most implementations skip, and it is the one that builds trust. We tune the abstention threshold deliberately: a system that says "I couldn't find that, here's who can help" is worth far more than one that fills the gap.
Retrieval quality gets measured on its own, separately from answer quality, using a labeled question set with known correct sources. When something goes wrong you then know whether the search missed the passage or the model mishandled it.
A knowledge base that was accurate at launch and stale six months later is worse than none, because people stop trusting it and keep asking anyway. Connectors watch the source systems and re-embed only what changed, with deletions propagated so a retired document stops appearing in answers.
Permissions are enforced at the retrieval boundary, never in the prompt. Chunks inherit their source document's access rules, and the query filters on the requesting user's entitlements before results are assembled. If someone cannot open the file, the system cannot quote it to them.
Nearly every agent we build depends on this layer. A voice agent quoting a price, a support agent citing a policy, a lead-response agent confirming service coverage — all of it is retrieval underneath a conversation. Orchestration decides when to look something up; retrieval decides whether the answer is real.
And when the source material simply is not in a usable state — locked in a legacy system, scattered across drives, or trapped in a tool with no export — that becomes a build problem. An agent is only as good as the systems it plugs into. When your stack cannot support the automation, we build the stack.
One runs on your documents on a schedule. The other runs on a question, in the moment someone asks it.
We gather the questions people actually ask, with their correct answers and sources. That set becomes both the specification and the evaluation, before anything is indexed.
30-min call · roadmap deliveredPipeline, hybrid search and re-ranking get built and scored against that question set. Chunking and retrieval settings are tuned by measurement rather than by intuition.
Typically 2–5 weeksSync runs continuously, retrieval scores stay on a dashboard, and the question set grows with every gap real users find. Stale content is a monitored condition, not a surprise.
Ongoing partnershipBecause your documents change and fine-tuning does not. Retrieval updates the moment the source updates, shows which passage an answer came from, and can enforce who is allowed to see what. Fine-tuning teaches style and format well, but it is the wrong tool for facts that have to be current and auditable.
If your data already lives in Postgres, pgvector usually wins — one database, one backup story, and filters that run alongside the vector search. Pinecone and Weaviate earn their place at larger scale or where a managed service removes real operational burden. We size the decision to your corpus and query volume rather than defaulting to whichever is fashionable.
Access control is enforced at retrieval, before anything reaches the model. Every chunk carries the permissions of its source document, and the query filters on the requesting user's entitlements. Asking a model politely not to reveal a document it has already been handed is not access control, and we do not treat it as such.
Through sync rather than reload. Connectors watch the source — a drive, a CMS, a database, a ticket system — and re-embed only what changed, with deletions propagated so a retired policy stops being quoted. Every chunk carries a source timestamp so both the model and your team can see how fresh an answer is.
We measure it separately from the generation. A labeled question set with known correct sources gives you retrieval accuracy on its own, so when an answer is wrong you can tell whether the search missed the passage or the model mishandled it. Those two failures have completely different fixes, and conflating them wastes weeks.
Yes, and we treat that as mandatory rather than optional. Answers link back to the passage and document they came from, so a person can check in seconds. Citations also change behavior: a system that must show its evidence is far less likely to produce a confident answer with nothing behind it.
Bring us the questions your team answers over and over. We'll show you what it takes to make them retrievable, cited and safe to automate.
Roadmap delivered · whether or not you build with us