A multi-tenant RAG platform for document-heavy enterprise workflows

Four microservices powering retrieval, generation, configuration and memory as a knowledge agent inside a larger agentic system.

  • 4 microservices in the platform
  • 4 reranking strategies supported
  • SSE token-level response streaming

We do not publish client names

The problem

Teams across several departments were losing time searching an internal wiki and downloading files to answer questions their own documentation already covered. The platform had to serve multiple tenants with isolated indices and slot into a wider ecosystem of notification, email and reporting agents.

What we built

Four services: ingestion, generation, config and memory. Ingestion runs event-driven Celery tasks that extract and chunk documents, embed them and index into per-tenant OpenSearch indices. Generation plans queries against conversation history and a glossary, expands each into up to five keyword and semantic variants, retrieves with a choice of BM25-only, semantic-only or hybrid strategies, and reranks with reciprocal rank fusion or one of three cross-encoder models. A critique node scores and refines answers over configurable iterations. Redis provides semantic caching, and results stream to clients over Server-Sent Events.

The result

A working multi-tenant knowledge agent. The memory service retains question, answer and node-level execution data to support multi-turn reasoning, and semantic caching removes redundant model calls.

More work

Tell us what you are trying to build

Describe the system and the constraint you have hit. You will get a technical reply, not a sales sequence.