Turn scattered company knowledge into a searchable, trustworthy system.
Company knowledge lives scattered across docs, wikis, and PDFs. Employees waste hours searching, or worse, get answers from an AI that hallucinates because there's no retrieval grounding it in real sources.
An enterprise RAG pipeline built for accuracy, not just speed — re-ranking to surface the right documents, faithfulness scoring on every response, and full traceability back to source pages.
Talk to PRIYA — a live example of this exact system
Try It Live →User Query -> Bi-Encoder Retrieval -> Cross-Encoder Re-Ranking -> LLM Generation -> Faithfulness Scoring -> Response
For simple business AI implementations — chatbot, FAQ assistant, basic knowledge base.
Workflow automation, AI agent, API/Telegram/email integrations.
RAG, NL2SQL, business analytics, document intelligence, advanced agents.
For larger or more complex requirements — scoped on a call.
Directional, not a fixed quote — real scope depends on your data and integrations.
Re-ranking to surface the right documents (not just the first match), faithfulness scoring on every response, and full traceability back to the exact source — the same architecture used in the live Advanced RAG Chatbot case study.
Scales with the vector database chosen for the project — ChromaDB, FAISS, Pinecone, or pgvector depending on your data volume and hosting constraints. This gets scoped during the initial conversation, not guessed at upfront.
Yes — document ingestion, chunking, and embedding are part of the build, not left as a separate problem for you to solve.
Tell me what you're trying to solve — I'll tell you honestly whether this is the right fit.