Ingestion pipeline
Connectors for PDFs, websites, Notion, Drive, SharePoint, Confluence. Smart chunking, deduplication, metadata extraction.
RAG Chatbot Development — From Document Ingestion to Production
A RAG chatbot is easy to demo and hard to ship. I build production RAG systems where retrieval is tuned, evaluation is automated, costs are predictable, and answers are grounded with citations.
Pricing from ₹80,000 • Remote • NDA-friendly • Reply within 24h
Who this is for
- SaaS companies adding an AI assistant to their docs, support, or in-app help.
- Teams replacing manual support tickets with a smart, source-grounded chatbot.
- Internal tools — knowledge bots over Notion, Confluence, Google Drive, or SharePoint.
What I deliver
Retrieval that actually works
Hybrid search (BM25 + dense), reranking (Cohere/BGE), query rewriting, and citation tracking that links back to source paragraphs.
Streaming chat UI
Next.js + Tailwind chat interface with streaming responses, citations, message history, and feedback capture for continuous improvement.
Evaluation & observability
Ragas-based eval pipeline, golden test set, hallucination detection, and dashboards for retrieval recall + answer quality.
Tech stack
LangChainLangGraphOpenAI / ClaudeOllama (local LLMs)pgvectorPineconeQdrantCohere RerankNext.jsFastAPIRagasDocker
How we'll work together
01
Discovery
Document corpus size, query types, latency / cost / accuracy targets.
02
Prototype
Week 1: working RAG over a sample of your docs with eval baseline.
03
Tune
Iterate on chunking, retrieval, prompts, reranking until eval scores hit target.
04
Deploy
Production deploy with streaming, citations, monitoring, and runbook.
FAQ
How much does a RAG chatbot cost?
Standard RAG chatbot (one data source, English, hosted on cloud LLM): ₹80,000–₹2,50,000. Multi-source, multi-tenant, or self-hosted LLM: ₹3,00,000+.
How long until it's live?
Working prototype in 7 days. Production-ready (eval-tuned, deployed, monitored) in 3–4 weeks.
Can it run on our infrastructure?
Yes. Self-hosted Ollama or vLLM, on-prem vector store (pgvector), no data leaves your network. Required for healthcare, fintech, or regulated industries.
What's your accuracy guarantee?
I commit to a measurable eval target (e.g., faithfulness > 0.85, answer relevance > 0.80 on your golden set) before declaring the system production-ready.
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Ready to start?
Tell me about your project. I'll send a scoped proposal within 24 hours.
Get in touch →