Enterprise RAG: High-Load 65k+ Chunk Knowledge Engine & AI Gateway

Enterprise RAG platform built on PostgreSQL (pgvector HNSW) with on-device embeddings, 88% Recall@5, and a resilient multi-provider AI Gateway.

Client

Global Consulting Enterprise

Services

Enterprise RAG & Hybrid Retrieval, pgvector HNSW + Reranking, Multi-Provider AI Gateway, Token Metering

Service

AI & Business Automation

Recall@5 88% / Precision@5 85%

Retrieval Accuracy

3ms avg / 4ms p95

Search Latency

1.9s with streaming

End-to-End Latency

Task

An international enterprise possessed over 2,300 proprietary compliance standards, legal briefs, and audit reports fragmented across legacy file shares. Staff spent up to 40% of their workday searching for policies. Off-the-shelf consumer LLMs produced hallucinations and leaked data, while single-provider APIs frequently throttled requests. The objective was to construct an autonomous enterprise RAG engine with local vector search and a resilient multi-provider AI Gateway providing verified answers with exact citations.

Solution

Built as a unified Turborepo/Bun monorepo. Documents are parsed and indexed (2,393 documents / 65,798 chunks) with local CPU embeddings via ONNX Runtime to ensure data privacy. A two-stage retrieval pipeline (pgvector HNSW graph search plus a Cross-Encoder Reranker) delivers 3ms retrieval latency (p95 of 4ms) and Recall@5 of 88%. The AI Gateway orchestrates Anthropic, OpenAI, Google, and DeepSeek with circuit-breaker failover: if rate limits or 5xx errors occur, requests route seamlessly to backup providers.

Resilient Enterprise RAG Dataflow

┌─────────────────────────┐          Staff Inquiry               ┌──────────────────────────┐
│    Enterprise Portal    │ ───────────────────────────────────► │  ONNX Local Embeddings   │
└────────────▲────────────┘                                      └────────────┬─────────────┘
             │                                                                │ 384-dim Vector
             │ Streamed Response + Strict Citations                           ▼
┌────────────┴────────────┐     Top-20 Chunks    ┌──────────────────────────────────────────┐
│   AI Gateway Router     │ ◄─────────────────── │   pgvector HNSW (3ms) + Reranker (88%)   │
│  (Anthropic / OpenAI /  │                      │   (65,798 chunks / 2,393 SOP documents)  │
│   Google / DeepSeek)    │                      └──────────────────────────────────────────┘
└─────────────────────────┘
 (Failover on 429 / 5xx)

Retrieval Accuracy Benchmark (Recall@K / Precision@K)

Pipeline Variant	Recall@5	Precision@5	Latency p95
Baseline Flat Search (pgvector IVFFlat)	62.4%	58.1%	48.0 ms
HNSW Indexing (Zero Reranker)	76.8%	71.5%	3.8 ms
HNSW + Local Cross-Encoder Reranker	88.2%	85.0%	4.2 ms

Key Features

High-Precision RAG (65k+ Chunks): Two-stage hybrid retrieval (pgvector HNSW + Reranker) achieving 88% Recall@5 and 85% Precision@5.
Multi-Provider AI Gateway: Resilient circuit breaker orchestrating Anthropic, OpenAI, Google, and DeepSeek with zero-downtime failover.
Sub-Millisecond Vector Retrieval: Average vector search latency of 3ms (4ms p95), end-to-end streamed response in 1.9s.
PostgreSQL Lean Queue (SKIP LOCKED): Background document ingestion and chunking handled directly in PostgreSQL without Redis or message brokers.
Token Accounting & Strict Citations: Granular department-level cost tracking and verbatim paragraph-level source attribution.

Technologies

Architecture and Deployment

Deployed in an on-premises enterprise environment. The system handles thousands of queries daily, slashing knowledge discovery time from minutes to 2 seconds while completely safeguarding confidential corporate intelligence.