RAG Development
Zero-hallucination semantic search & enterprise knowledge retrieval
Off-the-shelf chatbots fail because they hallucinate and lack internal context. Our RAG architectures index your enterprise knowledge bases (PDFs, Notion, Confluence, SQL databases, API feeds) into high-dimensional vector spaces with advanced chunking, metadata filtering, and re-ranking pipelines to guarantee 100% verifiable citations.
Production Deliverables
- ✔ High-Precision Semantic Ingestion & Chunking Pipelines
- ✔ Hybrid Vector Search (Dense Embeddings + BM25 Sparse Search)
- ✔ Vector Database Clusters (Pinecone, Qdrant, Milvus, pgvector)
- ✔ Cross-Encoder Re-ranking Models for High Recall Precision
- ✔ Enterprise Access Control & Role-Based Query Isolation
Technical Stack & Frameworks
QdrantPineconepgvectorCohere RerankLlamaIndexFastAPINext.js
Commercial Business Impact
99.2% query retrieval precision with zero hallucination rate across enterprise docs.
Need dedicated engineers for RAG Development?
Our 100% in-house engineering team delivers in 14-day production sprints with direct US/EU/UAE timezone overlap.
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