Our Services RAG Pipeline & Document Intelligence
RAG Pipeline & Document Intelligence
Techseria builds Retrieval-Augmented Generation (RAG) pipelines that make your business documents, knowledge bases, and operational data queryable in natural language. We ingest your content into Qdrant vector database, build retrieval chains with LangChain.js, and connect them to Azure OpenAI — so your team can ask questions and get accurate, sourced answers from your own information. The entire stack is JavaScript and Node.js. There is no Python environment to manage alongside your existing infrastructure. Deployed on Azure with enterprise security, role-based access, and full auditability. Every engagement starts with a 2-week Discovery Workshop to assess your document corpus, define the retrieval architecture, and produce a fixed-fee build plan.
Benefits and Technologies
Key Benefits
- Natural language search across all your internal documents — policies, contracts, manuals, case files — without manual keyword matching
- Accurate, sourced answers with citations back to the original document — not hallucinated responses
- Qdrant vector database stores and retrieves semantically relevant content, not just keyword matches
- Incremental ingestion — new documents added to the knowledge base automatically without rebuilding the entire index
- Role-based access control ensures staff only retrieve content appropriate to their permission level
- Full JavaScript/Node.js stack — integrates cleanly with your existing backend without a Python dependency
Technologies We Use
Have a large document library that your team struggles to search effectively? Our 2-week AI Discovery Workshop assesses your content, defines the retrieval architecture, and produces a fixed-fee build plan before any development begins.
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