RAG Chatbots & Chat With Your Docs
I build RAG chatbots that answer from your own docs with source citations, using pgvector, Pinecone and LangGraph. Fixed-price builds from 2 weeks.
RAG chatbot development services let your team ask questions in plain language and get answers cited from your own documents, not a model's training data. I build retrieval-augmented chatbots over your private files using OpenAI or Claude, pgvector or Pinecone, and LangGraph. Every answer links back to its source, so staff trust it and you stay compliant.
What you get
How I work
- 01
Scope call and data review
We talk through your docs, your questions, and what 'a good answer' means. I check data sensitivity and pick the model and vector store that fit. You get a fixed quote and timeline.
- 02
Ingestion and retrieval build
I build the pipeline that loads, chunks, and embeds your content into pgvector or Pinecone. I tune retrieval with hybrid search and test it against real questions from your team.
- 03
Chatbot, citations, and guardrails
I wire the chat layer with OpenAI or Claude, add source citations, and set guardrails so it declines when the docs don't answer. I build a Next.js UI or embed it in your app.
- 04
Evaluation and launch
I run your question set through an eval to measure accuracy and catch weak spots. I deploy, hand over the code and docs, and stay available for tuning after go-live.
Ways to work together
RAG chatbot in 2 weeks, fixed price
I ship a working chatbot over one document set. Ingestion, pgvector or Pinecone retrieval, citations, and a Next.js chat UI. Fixed scope, fixed price, ready to demo.
Chat-with-your-docs MVP, 1 to 3 weeks
I connect your PDFs, Notion, or Confluence to a grounded chatbot. You upload docs, ask questions, and get cited answers. Good for validating the idea before a full build.
RAG accuracy and retrieval audit, 1 week
I review your existing RAG app, test retrieval quality, and find why answers are wrong or vague. You get a written report with chunking, embedding, and prompt fixes you can act on.
Embedded RAG widget for your product, fixed quote
I add a source-cited chat assistant inside your existing web app. React widget, your auth, your data, streaming answers. Scoped after a short call.
Tech I use for this
Common questions
Q.How much does it cost to build a RAG chatbot?
A production RAG chatbot usually starts around $4k to $12k fixed, depending on data sources, accuracy needs, and UI. I also work at $150 to $250 per hour for ongoing or open-ended work. After a short call about your docs and goals, I give you a fixed quote and timeline so there are no surprises.
Q.What is RAG and why not just use ChatGPT?
RAG, or retrieval-augmented generation, looks up your real documents first, then answers using only that text. Plain ChatGPT answers from training data and can invent facts about your business. RAG keeps answers grounded in your private data, adds source citations, and updates the moment you add new docs, with no model retraining.
Q.Can it cite sources and avoid making things up?
Yes. Every answer links back to the exact document and section it came from, so your team can verify it. I add guardrails so the bot says it doesn't know when your docs don't cover a question, instead of guessing. This matters most for policies, compliance, and regulated knowledge where wrong answers carry risk.
Q.What documents and data sources can you connect?
I connect PDFs, Word files, Notion, Confluence, websites, Google Drive, and SQL databases. I build ingestion pipelines that chunk and embed your content into pgvector or Pinecone. If your source has an API or export, I can usually load it. We confirm the exact sources on our scope call.
Q.Will my company data stay private and secure?
Yes. Your documents stay in infrastructure you control, like your own Postgres with pgvector or your Pinecone account. I can use Azure OpenAI or Claude with no-training data terms so your content is never used to train a model. For sensitive setups, we design data residency and access rules up front.
Q.Do you only build the bot, or can you maintain it too?
Both. I deliver the full chatbot with code and documentation you own. After launch I can stay on for tuning, new data sources, and accuracy improvements at $150 to $250 per hour, or on a small monthly retainer. You are never locked in, and the codebase is yours to take elsewhere.
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Start your project
Tell me what you are building and I will reply within one business day with next steps. You talk to me, the person writing the code, the whole way through.