Enterprise RAG
Agam Intelligence builds retrieval-augmented generation so answers come from your corpus - policies, contracts, product docs, and knowledge bases - not the open internet. We design ingestion, access control, retrieval, and evaluation so people can trust what the system cites.
Intelligence
Answers with a source your teams can check.
A chat window on top of a vector store is not an enterprise knowledge system. Permissions, stale documents, chunking, and evaluation decide whether RAG helps or misleads. We start with who is allowed to see what, then build ingestion, retrieval, and citations so generative AI is grounded in the documents you actually own.
We ingest PDFs, wikis, tickets, and file shares with the metadata that retrieval needs - owner, sensitivity, freshness. Chunking and parsing are designed for how your documents are actually written, not a generic splitter.
Enterprise RAG fails if a user can retrieve a document they could not open in SharePoint. We enforce identity, ACLs, and tenant boundaries at retrieval time - not only at the chat UI.
Answers include sources the user can open. We tune retrieval, reranking, and prompts so the model stays on the retrieved context - and refuses when the corpus does not support an answer.
We build question sets from real work, then measure recall, citation correctness, and answer usefulness. RAG quality is a product metric, not a vibe from a few happy-path questions.
Documents change. We design refresh, deprecation, and ownership so yesterday’s policy does not keep answering today’s questions. Someone has to own the corpus; we make that operationally possible.
RAG belongs where questions already happen - intranet, service desk, product UI, Slack or Teams. We integrate retrieval into those surfaces with logging and feedback, not a standalone chatbot nobody bookmarks.
A regional health network needed executive dashboards and predictive models grounded in governed clinical data.
A B2B SaaS vendor needed RAG-based product assistance grounded in customer docs - not a generic chatbot.
A payments processor needed anomaly detection that compliance could defend - not another black-box alert queue.
Measure time-to-answer, deflection, and citation correctness against the questions people already ask - not a demo corpus.
ACL-aware retrieval and refusal behaviour reduce the chance that a fluent answer leaks a document or invents a policy.
Roll RAG out to a team or a document set first, with evaluation and owners, before it becomes a company-wide search replacement.
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