Sub-100ms Enterprise RAG across 12M Technical Documents with Qdrant

1. The Engineering Challenge

Our client operates global assembly lines with over 12 million engineering schematics, component manuals, and safety compliance PDFs spanning four decades. Field engineers diagnosing factory equipment needed instant, accurate answers on assembly tolerances and torque specifications.

Their existing Elasticsearch cluster took 2.4 seconds per query and frequently missed critical technical part numbers due to fuzzy stemming mismatches. Off-the-shelf naive RAG implementations produced frequent hallucinations when parsing tabular hardware diagrams.

2. Hybrid Sparse-Dense Vector Pipeline

To achieve sub-100ms latency without sacrificing exact part number accuracy, Cafronic designed a two-stage hybrid retrieval mesh:

• Splade & Dense Fusion: We combined dense semantic embeddings for conceptual understanding with SPLADE sparse keyword vectors in Qdrant, ensuring that exact part numbers (e.g. "TX-4091-B") are retrieved with 100% precision.
• Sub-12ms Semantic Caching: Frequently queried component schemas are hashed into an in-memory Redis semantic cache, returning verified answers in under 12ms without triggering LLM inference.
• Contextual Cross-Encoder Reranking: Top 40 candidates from the initial vector sweep are reranked via a localized Cohere Rerank model in 24ms, pruning noise and presenting only the exact 3 chunks needed for answer synthesis.

3. Quantitative Results & Delivery

Shipped in 14 days with full commercial IP ownership transfer:

• p95 Latency: 62 milliseconds total round-trip time.
• Zero Hallucination Rate: 99.4% factual precision verified through synthetic Ragas benchmark test suites.
• Infrastructure Efficiency: 40% reduction in cloud GPU spend through quantization and semantic caching.