Case 03
Enterprise AI Knowledge Platform
Multi-tenant retrieval with source-grounded answers
Overview
A multi-tenant AI knowledge platform that lets people upload and connect documents, repositories, and other sources, then ask questions in natural language and receive answers grounded in those sources.
Problem
Organizational knowledge is scattered across documents, repositories, and internal systems, which makes accurate retrieval slow and unreliable.
Architecture
Architecture · 03
- 01
Source connectors
- 02
Async ingestion
- 03
Chunk + embed
- 04
Hybrid retrieval
- 05
Rerank
- 06
Cited generation
An asynchronous ingestion pipeline handles processing, chunking, embeddings, and indexing. Queries use hybrid retrieval — semantic search plus keyword search — then reranking and LLM generation with citations.
Engineering
The product only works if retrieval is permission-aware and citations stay honest. Multi-tenancy, metadata filters, and evaluation sit at the center of the design.
Challenges
- Document ingestion at scale
- Chunking strategy and hybrid retrieval
- Reranking and metadata filtering
- Multi-tenancy and permission-aware retrieval
- Citation accuracy and RAG evaluation