RAG Solutions
Retrieval-Augmented Generation pipelines connecting AI to your proprietary data for accurate, contextual responses.
AI grounded in your data
Retrieval-Augmented Generation (RAG) is the gold standard for building AI systems that reference your proprietary data instead of hallucinating. We design end-to-end RAG pipelines that ingest documents (PDFs, databases, wikis, Notion pages), chunk and embed them into high-performance vector databases (Pinecone, ChromaDB, Weaviate), and serve contextually accurate answers via LLMs. Our pipelines include hybrid search (semantic + keyword), re-ranking layers, and citation tracking so users can verify every response.
Whether you need an internal knowledge assistant for employees, a customer-facing support bot grounded in your documentation, or a research tool that synthesizes findings from thousands of papers — we build it production-grade. We implement guardrails, access control, real-time indexing for live data, evaluation frameworks to measure accuracy, and monitoring dashboards for continuous improvement.
What's included
- Custom vector database setup
- Document processing pipeline
- Embedding model selection
- Query optimization
- Real-time retrieval
- Accuracy monitoring