back_office_ops · workflow
How to build a scalable RAG-based enterprise knowledge assistant
Productizing and scaling LLM-based knowledge assistants for tens of thousands of enterprise users involves challenges across server capacity, algorithm tuning, robustness, reliability, privacy, and security.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User query received via API
The Application Layer activates upon receiving a user query through an API.
Tools used
RAGKedroLangChaingpt-4React
Outcome
(not stated)
Results
Volumeover 90%
Grounding & classification
Source type: technical build writeup
18 fields verified against source quotes.
conversational aienterprise searchknowledge searchragknowledge basepolicy documenttools describedworkflow describedaccuracy improvementtechnical build writeupback office opsit supportrag answering