Knowledge Assistant with RAG
Documents flow in from the systems that own them; questions are checked for scope, answered from retrieved passages with citations, and tested before every release.
Two flows and a loop. Ingestion keeps the index current: the system that owns a document publishes its metadata, workers fetch, detect the language, chunk and embed it, and the index stores it for keyword and vector search. A question is first checked for scope by a relevance agent; in scope, hybrid search finds passages and the model answers with its sources. Every step is traced, wrong answers become test questions, and a version ships only when it passes them.
Point at a component to light its links; select it for what it does here and the practice that makes it right on AWS.
When to use it
- Employees spend time searching internal documents for answers
- An existing chatbot answers confidently but wrongly, or cannot say where an answer came from
- Documents change often and must reach the assistant without manual uploads
How NGT delivers it
Built from code, reviewed at a gate by an NGT architect, and handed over with runbooks your team can run.
AI & Agents
