Microsoft Solution Partner
|Open-source · No vendor lock-inUSA · UK · India+44-7782500082
RAGPipeline&DocumentIntelligence
Techseria builds RAG pipelines using LangChain.js + Qdrant, making your docs queryable via Azure OpenAI. Fixed-fee. Node.js. Discovery Workshop.
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
Stack
Technologies Used
LangChain.js
Qdrant Vector Database
Azure OpenAI Service
LangChain.js
Qdrant Vector Database
Azure OpenAI Service
Node.js / TypeScript
Azure Blob Storage
Node.js / TypeScript
Azure Blob Storage
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.
FAQs