Enterprise RAG is a different beast from consumer RAG. A consumer RAG system answers questions from a single knowledge base. An enterprise RAG system must: answer questions from multiple data sources (Confluence, SharePoint, databases, email), enforce access controls (different users see different documents), meet compliance requirements (SOC 2, HIPAA, GDPR), scale to thousands of concurrent users, and provide audit trails for every response. Here is the enterprise RAG architecture.
Multi-Source Ingestion
Enterprise data lives in multiple systems: document management (SharePoint, Google Drive), wikis (Confluence, Notion), databases (PostgreSQL, MongoDB), email and chat (Outlook, Slack), and custom applications. Each source has different formats, different update frequencies, and different access patterns. The ingestion layer must: connect to each source via API, extract content in a uniform format, chunk and embed consistently, and handle incremental updates (not full re-indexing). The ingestion pipeline is the most complex part of enterprise RAG.
Access-Controlled Retrieval
Different users should see different documents. An HR agent should answer questions about HR policies but not reveal engineering salaries. A support agent should answer product questions but not expose internal architecture documents. Implement access-controlled retrieval: tag each document chunk with access permissions (role, department, classification level), check the user's permissions at query time, and only retrieve documents the user is authorised to see. This is not optional for enterprise — it is a compliance requirement.
Warning
Access-controlled retrieval is a compliance requirement, not a feature. If your RAG system does not enforce document-level access controls, it is a data leak waiting to happen.
Scale and Performance
Enterprise RAG must handle: thousands of concurrent queries, millions of document chunks, sub-second retrieval latency, and 99.9%+ availability. This requires: a distributed vector database (not a single-node instance), query caching (cache frequent queries), sharding (partition the document index by tenant or domain), and load balancing (distribute queries across multiple retrieval instances). The architecture must be horizontally scalable — adding capacity by adding nodes, not upgrading hardware.
Conclusion
Enterprise RAG requires additional architecture beyond basic RAG: multi-source ingestion, access-controlled retrieval, compliance controls, and horizontal scalability. These are not features — they are requirements for production in enterprise environments.
Key Takeaways
- Enterprise RAG: multi-source ingestion, access controls, compliance, and horizontal scalability
- Each data source (SharePoint, Confluence, databases) has different formats and update patterns
- Access-controlled retrieval is a compliance requirement — tag documents, check permissions at query time
- Scale requires: distributed vector database, query caching, sharding, load balancing
- Enterprise RAG is fundamentally more complex than consumer RAG — architect accordingly