Enterprise RAG with workspace isolation

What enterprise RAG requires

Isolation, authorization, freshness, and auditability—on top of embedding quality.

A brilliant retriever that leaks Customer A’s files to Customer B is a failed system. Enterprise RAG therefore starts with tenancy: which workspace owns the assets, which agents may query them, and which keys can trigger retrieval.

Kimss data assets are workspace-scoped; exact storage services for your deployment are described in /docs/architecture and /docs/data_assets.

Governance around retrieval

RAG traffic should inherit the same gateway controls as completions—auth, credits, and attribution.

If retrieval bypasses Kimss, you reintroduce Shadow AI for documents. Keep ingestion and query paths inside approved APIs. Combine with /multi-tenant-ai-security and /ai-agent-security.

Operating practices

Version corpora, separate staging assets, and test cross-workspace denial just as you test key isolation.

• Ingest per workspace; never share production corpora across tenants.
• Promote asset changes through environments deliberately.
• Monitor retrieval errors and empty-result rates as product SLIs.
• Align credit budgets with teams that run heavy re-index jobs.

Where to implement

Start with public data asset docs, then validate in a non-production workspace before customer data.

Engineers: /docs/data_assets and /docs/creating_agents. Architects: /docs/architecture. Evaluators comparing platforms: /compare.

Frequently asked questions