AI Chatbot Development
AI chatbot development that answers from your content - not from guesswork.
Most chatbots fail for one of two reasons: they only know a script, or they confidently invent things. I build chatbots that read your actual content - your documentation, product data, policies and past tickets - and answer from it, with a link back to the source on every reply. If the answer is not in your material, it says so.
What it is
A grounded chatbot is a retrieval system with a conversation on top. Before it answers, it searches your own content, ranks what it found, and writes the reply from those passages - so the answer is traceable to a document rather than to the model’s memory.
That is the same architecture I build for enterprise knowledge systems, sized down to a support or sales assistant. It is what separates a chatbot your team trusts from a demo that embarrasses you in front of a customer.
It goes where your customers already are: on your website, or inside Slack, Microsoft Teams or WhatsApp, with the conversation handed to a human whenever it should be.
What you get
Built for production, not a demo
Grounded in your own content
Answers are written from your documents, product data and policies - not from whatever the model happens to remember.
A citation on every answer
Each reply links back to the source it came from, so anyone can check it in a click.
An honest "I don’t know"
When your content does not cover the question, it says so and offers a handover instead of inventing an answer.
Handover to a human
Clear escalation rules, so the conversation reaches a person when it needs to - with the context attached.
Wherever your customers are
Website widget, Slack, Microsoft Teams or WhatsApp - the same brain behind each one.
Logs you can learn from
Every conversation is stored and reviewable, so you can see what people actually ask and where you are missing content.
How it works
From first call to production
Point it at your content
We pick the sources that matter - docs, site pages, product data, past tickets - and connect them in place.
Build the retrieval
Structure-aware chunking, hybrid search and reranking, so the right passage is found before anything is written.
Shape the conversation
Tone, escalation rules, and what it should refuse to answer - tested against your real questions.
Ship and watch it
Deployed where your customers are, with logging and review so it keeps improving after launch.
Proof
Real systems, shipped
Live products I designed and built end to end - the clearest signal I can build yours.
NirixAI
A production chatbot over a 20K+ content corpus - hybrid search on Pinecone, streaming answers, citations on every reply.
This site
The assistant in the corner is mine: it answers from my own pages and posts, and tells you when it does not know.
FAQ
Questions clients ask
How much do AI chatbot development services cost?
It depends on how many content sources it reads, where it needs to live, and how much conversation design it needs. I scope it on a call and send a fixed quote before any build starts - there is no hourly meter running.
How do you stop the chatbot from making things up?
It answers only from content retrieved out of your own material, and every reply carries a citation back to the source. When retrieval finds nothing relevant, the chatbot says it does not know and offers a handover instead of guessing.
Can it use our existing documentation and help centre?
Yes, and it should. Standard connectors read SharePoint, Confluence, Google Drive, Notion, S3, website content and most databases in place - nothing needs migrating.
Where can the chatbot be deployed?
A widget on your website, or inside Slack, Microsoft Teams or WhatsApp. The retrieval layer is the same in each case, so you are not rebuilding it per channel.
Does our data get sent to OpenAI or Anthropic?
Only if you choose that deployment model. The options are self-hosted open-source models in your own cloud, enterprise API tiers with zero-data-retention agreements, or a hybrid. We pick it against your compliance requirements before anything is built.
What happens when it cannot answer?
It hands over to a human with the conversation context attached, and the gap is logged. Those logs are the fastest way to find out which content you are missing.
Related
Next Step
Ready to scope this?
A 60-minute call, free. No sales pitch - just honest, actionable direction on your project.
Let’s talk