RAG & Vector Search Solutions

Enterprise RAG (Retrieval-Augmented Generation) & Semantic Search

Unlock the hidden intelligence inside your unstructured corporate data. We develop production-ready RAG architectures that connect your proprietary databases, PDFs, SharePoint, Confluence, and internal wikis to LLMs with 100% cited, verifiable responses.

Why Enterprise RAG Over Generic LLMs?

• Zero Hallucinations with Source Citations: Every answer is grounded directly in your uploaded source documents with exact page/line references.
• Real-Time Knowledge Updates: No need to retrain or fine-tune models when company data changes; vector embeddings update instantly.
• Role-Based Access Control (RBAC): Ensure sensitive documents (HR, financial, executive) are only retrieved by authorized personnel.

Our RAG Architecture Stack

• Vector Databases: Pinecone, Qdrant, Weaviate, Milvus, ChromaDB, PGVector.
• Embedding Models: OpenAI text-embedding-3, Cohere Embed, BGE-Large, Voyage AI.
• Advanced RAG Techniques: Hybrid Search (BM25 + Dense Vectors), Semantic Chunking, Re-Ranking (Cohere Rerank), and Self-Querying Retrieval.