AI Customer Support Chatbot
RAG-powered support bot trained on the client's own docs — accurate answers, honest hand-offs.
Overview
RAG-powered support bot trained on the client's own docs — accurate answers, honest hand-offs. I built a retrieval-augmented (RAG) chatbot that embeds the client's documentation and help articles into a Pinecone vector store, so OpenAI answers only from real, cited content. A clean React widget sits on the site; when the bot isn't confident, it hands off to a human instead of hallucinating. Built with OpenAI, React, Pinecone, Node.js, this ai chatbot project was delivered for a client in Worldwide and designed for reliability, a clean user experience and long-term maintainability — so it keeps delivering value well after launch.
The challenge
A SaaS product's support inbox was overwhelmed with questions already answered in the docs, and generic chatbots gave wrong, made-up answers that eroded trust.
What I built
I built a retrieval-augmented (RAG) chatbot that embeds the client's documentation and help articles into a Pinecone vector store, so OpenAI answers only from real, cited content. A clean React widget sits on the site; when the bot isn't confident, it hands off to a human instead of hallucinating.
How I built it
- Implemented a retrieval-augmented (RAG) architecture over a Pinecone vector store.
- Constrained OpenAI to answer only from real, cited documentation.
- Embedded a clean, accessible React chat widget on the site.
- Added a confidence threshold with human handoff instead of hallucinating.
Key features
- Natural-language understanding
- Grounded, accurate answers from your content
- Lead capture and qualification
- Smooth human handoff for complex cases
- Multi-channel deployment
- Conversation logging and analytics
The results
- Grounded, accurate answers pulled from real docs
- Support ticket volume noticeably reduced
- Confident hand-off to humans instead of made-up replies
This is an example of my ai automation work. Need something similar? Start a project →
My delivery process
Discovery
We start by understanding the goal, the users and the constraints — no jargon, just a clear picture of what success looks like.
Plan & design
A clear scope, architecture and milestone plan, so you know exactly what's being built and when.
Build & iterate
Development in reviewable increments with regular updates, so you see working software early and often.
Launch & support
Testing, deployment and ongoing support, so it keeps running smoothly long after go-live.
AI Customer Support Chatbot — FAQ
Can you build a ai customer support chatbot for my business?
Yes. I build custom ai chatbot solutions like this ai customer support chatbot, using OpenAI, React, Pinecone, Node.js and tailored to your exact workflow, timeline and budget. Send me your requirements and I'll reply with a clear plan and quote.
How much does a project like this cost?
It depends on scope. After a short discovery call I provide a clear, fixed quote and milestone plan before any work begins — no surprises. Smaller builds start low; larger platforms are quoted per milestone.
How long does it take to build?
A focused MVP can take a few weeks, while a larger ai chatbot build runs in milestones over a couple of months. You'll see working software early and often, not just at the end.
What technology do you use, and will I own it?
This project uses OpenAI, React, Pinecone, Node.js. I pick the right stack for each project, and you own 100% of the code and infrastructure — delivered in your own repositories and accounts.
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Want something like this built?
Tell me about your project and I'll get back to you within 24 hours. Prefer to chat? Message me on WhatsApp.