Agents Grounded in Your Knowledge.

Standard RAG is a single pass: embed the question, fetch the top matches, generate an answer. If the retrieval was poor, the answer is poor and nothing notices. A RAG agent treats retrieval as a tool it can use repeatedly — it evaluates what came back, reformulates the query, searches a different store, follows a reference from one document to another, and only answers once it has sufficient evidence. On multi-hop questions the difference is large.

Yes, and it must. We enforce entitlements at query time by filtering the retrieval set against the asking user's permissions from your identity provider or document system — so two users asking the same question can legitimately receive different answers. We never rely on instructing the model not to reveal something, because that is not a security control.

Pinecone, Weaviate, Qdrant, pgvector and Azure AI Search are all in regular use, and the right choice depends on your scale, latency needs and where your data is allowed to live. For many mid-size corpora pgvector alongside your existing Postgres is the correct, boring answer — no new infrastructure to run. We will not push you to a new database you do not need.

First by surfacing it rather than silently picking one: the agent flags the conflict, cites both sources with their dates and owners, and applies your precedence rules where you have them — newest version wins, or this jurisdiction overrides that one. The corpus audit usually finds these conflicts early, and resolving them is often the highest-value output of the whole project.

Yes. We deploy the full stack — ingestion, vector store, orchestration and open-weight models — inside your cloud account or on-premise. This is standard for our clients in healthcare, financial services and defence-adjacent work, where the data residency requirement is non-negotiable regardless of what a vendor DPA says.

With a labelled evaluation set built from your real questions and verified answers, scored on retrieval quality and answer groundedness separately. Separating the two matters: it tells you whether a failure came from not finding the document or from misreading it, which are entirely different fixes. The suite runs on every change so accuracy regressions are caught before users find them.