Enterprise RAG Architecture
Answer employee or customer questions from internal knowledge, with citations and no hallucinated advice.
Architecture gallery
The conversation has moved on from “can AI do this” to “can we trust it in production.” The hard problems now are grounding answers in real sources, deciding how much autonomy an agent should have, evaluating a system that is non-deterministic by design, and doing all of it without a runaway cloud bill. These are the patterns I keep coming back to for exactly those problems.
Each one covers the request flow, why each component is there, the alternatives, and when not to use it. Start with Enterprise RAG for the interactive walkthrough.
Showing 7 items.
Answer employee or customer questions from internal knowledge, with citations and no hallucinated advice.
Multi-step tasks where the path isn't known in advance: research, triage, or orchestration across systems.
High-stakes drafting where a confident wrong answer is a liability: security questionnaires, compliance responses, contract review.
Any workflow where a wrong automated action is expensive: customer communications, financial actions, clinical or legal support.
Ingesting and enriching documents (contracts, invoices, uploads) at unpredictable volume without standing infrastructure.
Operational dashboards, anomaly detection, and alerting where minutes-old data is already too old.
Enterprise AI on governed data that shouldn't leave the platform: internal assistants, NL-to-SQL, in-database scoring.