Gig · from $90
Custom AI chatbot development: trained on your data (RAG)
A retrieval-augmented (RAG) chatbot for customer service and internal Q&A that answers from your own documents, with sources your customers or team can check, instead of guessing.
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- custom ai chatbot
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- chatgpt chatbot
- langchain
Basic
$90
Docs Q&A Prototype
RAG chatbot over up to 50 pages (PDF/DOCX/web), source citations, simple web demo.
- Delivery
- 3 days
- Revisions
- 1
Standard
$200
Business Chatbot
Up to 500 pages, tuned chunking and retrieval, citations, Next.js chat UI, embeddable widget.
- Delivery
- 7 days
- Revisions
- 2
Premium
$700
Production RAG System
Multiple sources, auto re-indexing, auth, feedback logging, eval set, deploy and docs.
- Delivery
- 14 days
- Revisions
- 3
Before you order: Send me a sample of your documents and your use case before ordering.
What you get
- Ingestion of PDFs, DOCX, web pages, Notion/Docs exports or database content
- Smart chunking, embeddings and similarity search (pgvector, Pinecone or similar)
- Answers with citations and an "I don't know" fallback to reduce hallucinations
- Clean chat UI in Next.js/React, or an API you can plug into your product
- Optional auth, usage logs and feedback buttons
- Deployment and a clear README
Who this is for
- Support teams and online shops that want a customer-service chatbot grounded in their own help docs, policies and product pages
- Businesses whose customers keep asking questions the documentation already answers
- Teams that want an internal assistant over PDFs, DOCX files, web pages or Notion/Docs exports
- Products that want a grounded Q&A feature on their own site or app
- Anyone who tried a generic chatbot and found it makes things up
- AI engines
- GPT · Claude
- Languages
- Python · TypeScript
- Vector search
- pgvector · Pinecone
How it works
01
Share your documents and use case
Send a sample of your documents and what the chatbot should help with, so I can confirm the right package.
02
Build and tune retrieval
Ingestion, chunking, embeddings and similarity search, tuned for retrieval quality rather than just a pretty chat box.
03
Deliver and deploy
A chat UI or API with citations and an "I don't know" fallback, deployed, with a clear README.
Common questions
Will it make things up?+
RAG greatly reduces this. Answers are grounded in retrieved text, sources are shown, and the chatbot is configured to say it does not know when the answer isn't in your data.
Is my data private?+
Your documents stay in your own accounts (vector database, cloud, API key). Working copies can be deleted after delivery.
Which vector database?+
pgvector (Postgres), Pinecone, or others you already use. I'll recommend the cheapest option that fits.
Can I add it to my website?+
Yes. Standard and Premium include an embeddable chat widget or API.
Can it work as a customer service chatbot?+
Yes. Trained on your help docs, policies and product pages, it answers common customer questions with sources and says it does not know instead of guessing. Anything beyond answering questions, such as hand-off to a human agent or a helpdesk integration, is scoped separately.
How much does an AI chatbot for a website cost?+
Packages are fixed-price and shown above, from a docs Q&A prototype to a production system with authentication, re-indexing and an evaluation set. The price depends on how many documents and sources you have and how it must be deployed.
Can it work on WordPress or my e-commerce site?+
Yes, as long as you can add a script or call an API. The chatbot ships as an embeddable widget or API, so it works on WordPress, online stores and custom sites. Deep platform plugins are scoped separately.
Part of AI & LLM Integrations. Need something bigger than a package? Custom projects are scoped individually.
Related guide
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