Enterprise RAG at Scale: 10M Documents, 100ms Latency

The Challenge

A Fortune 500 financial services company needed to make their institutional knowledge accessible through natural language. The requirements:

• 10+ million documents across legal, compliance, research, and operations
• Sub-100ms response time for interactive use
• Enterprise security: Role-based access, audit logging, data residency
• High accuracy: Responses must be grounded in source documents

Traditional keyword search wasn't sufficient—users needed contextual understanding and synthesised answers.

Solution Architecture

Document Processing Pipeline

1. Ingestion: Documents flow from SharePoint, Confluence, and internal systems
2. Chunking: Intelligent splitting respecting document structure
3. Embedding: Dense vector generation with domain-tuned models
4. Indexing: Distributed vector store with metadata filtering

Query Pipeline

1. Query understanding: Intent classification and query expansion
2. Hybrid retrieval: Vector similarity + keyword matching + metadata filters
3. Re-ranking: Cross-encoder scoring for precision
4. Generation: Grounded response with citation extraction

Technical Implementation

Vector Store Architecture

With 10M documents averaging 5 chunks each, we index 50M+ vectors. Design choices:

• Distributed indexing: Sharded across multiple nodes for parallel search
• Hierarchical clustering: Coarse-to-fine search reduces comparisons
• Quantised vectors: 4-bit quantisation for memory efficiency
• Hot/cold tiering: Frequently accessed documents in high-speed storage

Retrieval Strategy

Simple vector similarity isn't enough for enterprise use. Our hybrid approach:

Final Score = α × Vector_Score + β × BM25_Score + γ × Recency_Score + δ × Access_Frequency

Weights are tuned per document type and query pattern.

Access Control

Every query respects user permissions:

• Pre-filtering: Only search documents the user can access
• Post-filtering: Verify permissions before returning results
• Audit logging: Full query and response tracking

Performance Results

Latency Breakdown

Accuracy Metrics

• Retrieval precision@10: 87%
• Answer accuracy (human evaluation): 92%
• Citation accuracy: 96%

Scale

• Daily queries: 2.4 million
• Peak QPS: 850
• Document updates: 50,000/day with under 5 minute indexing lag

Key Learnings

1. Chunking Strategy Matters

Initial naive chunking produced poor results. Improvements:

• Respect document structure (headings, sections)
• Overlap chunks to preserve context
• Adjust chunk size by document type

2. Domain-Specific Embeddings

General-purpose embedding models struggled with financial terminology. Fine-tuning on domain documents improved retrieval precision by 23%.

3. Hybrid Retrieval is Essential

Vector search alone missed exact matches for regulatory codes and product names. Adding keyword search captured these cases.

4. Latency Budget Allocation

We allocated latency budget based on user impact:

• Retrieval: 40% (determines answer quality)
• Generation: 50% (user-visible output)
• Overhead: 10% (networking, logging)

Security and Compliance

Enterprise deployment required:

• Data residency: All processing within approved regions
• Encryption: At-rest and in-transit
• Access logging: Immutable audit trail
• PII handling: Automatic detection and masking
• Model isolation: Dedicated inference instances

ROI Analysis

After 6 months of deployment:

• Time savings: 45 minutes/employee/week on document search
• Support ticket reduction: 30% fewer internal queries
• Onboarding acceleration: New employees productive 2 weeks faster
• Compliance efficiency: Regulatory response time reduced 60%

Conclusion

Enterprise RAG at scale requires more than connecting a vector database to an LLM. Success depends on thoughtful architecture across ingestion, retrieval, and generation—while respecting enterprise security requirements.

For enterprise RAG solutions, contact info@scx.ai.