Retrieval-Augmented Generation
Hire a RAG developer
I build retrieval-augmented generation (RAG) systems — chatbots and assistants grounded in your own documents and data, with streaming answers, citations, and guardrails. I'm Bhuwanesh Sisodia, a full-stack and AI engineer in India working remotely worldwide, shipping RAG pipelines on pgvector, Pinecone, and Qdrant that stay accurate and cost-efficient at scale.
What I build
- Ingestion + chunking + embeddings pipelines over your docs, data, and APIs
- Vector search on pgvector, Pinecone, or Qdrant — with hybrid search + reranking
- Streaming chat UI with inline citations, grounded answers, and fallbacks
- Evals and observability (Langfuse / DeepEval) so quality is measured, not guessed
- Guardrails, prompt caching, and caching layers that keep token cost low
Stack
- TypeScript
- Python
- Vercel AI SDK
- OpenAI / Claude
- pgvector / Pinecone / Qdrant
- LangGraph / LlamaIndex
- Langfuse
- Next.js
Questions, answered
- How much does it cost to build a RAG chatbot?
- A focused RAG assistant grounded in your docs is typically a project-based engagement scoped to your data volume, accuracy bar, and where it gets embedded. I write a clear scope before quoting so there are no surprises.
- How do you keep RAG answers accurate and stop hallucinations?
- Retrieval quality first (good chunking, hybrid search, reranking), grounded prompts that cite sources, guardrails for out-of-scope questions, and continuous evals so regressions are caught before users see them.
- Can you embed the assistant in my existing web or mobile app?
- Yes — it ships as an embeddable widget or API that drops into your Next.js, React, or React Native app, using your auth and your branding.
Also hire me for