How is this different from a generic AI chatbot?

A generic chatbot answers from whatever the language model happens to know, which is why it invents policies and prices. The chatbot I build answers from retrieval over content you supply, with citations, explicit refusal boundaries, and a defined handoff to a human. You can inspect and correct the behaviour instead of accepting it.

Can the chatbot use my real business data, such as order status?

Yes. Rather than only reading documents, the assistant is given a defined set of permitted tool calls — for example looking up an order, checking availability, or creating a lead. Each call is implemented against your real backend and scoped so the assistant cannot reach data it should not.

Will it work on WhatsApp and Telegram?

Yes. The assistant logic and knowledge base are shared, so the same capability can run on your website, on WhatsApp, and on Telegram without maintaining three separate systems. Channel-specific behaviour such as message formatting is handled per platform.

What happens when the assistant does not know something?

That case is designed for rather than left to chance. The assistant is instructed to say it does not know, and the conversation is routed to a human with the transcript and the retrieved context attached, so your team gets the question instead of a guess.

Can I update the answers myself after launch?

Usually, yes. Because the answers come from your content rather than from hard-coded prompts, most changes are a content edit rather than a code change. I document the exact process during handoff so your team can keep the assistant accurate.

Which AI provider do you build with?

I build with the OpenAI API for the model layer, combined with your own content for grounding. The provider choice is a straightforward swap if you later want to compare models or costs, because the retrieval and tool layers are kept separate from the model call.

All services

GPT assistants · Retrieval on your content · Web · WhatsApp · Telegram

Custom AI Chatbots That Answer From Your Own Knowledge

Most generic chatbots give confidently wrong answers because they know nothing about your business. I build assistants grounded on your real content — product information, policies, pricing, past tickets — so they answer from facts you control and hand off to a person at the point where a person is genuinely needed. The result is a chatbot that sounds like your business, runs on the channels your customers already use, and can be corrected without a rebuild.

Discuss your project See what I build

Problems this engagement is built to solve

The scope starts from the product problem—not a prepackaged list of technologies.

The bot invents answers that contradict your pricing, policy, or warranty terms
It answers from generic model knowledge instead of your actual content
There is no clean way to hand a difficult conversation to a human
It is trapped in a website widget instead of the messaging channels customers use
Nobody can tell which conversations it resolved and which it lost
Correcting one answer means a rebuild or a call to a vendor

What you should leave with

Answers grounded in content you supply, traceable back to a source
A defined boundary where the assistant stops and a person takes over
One assistant deployed on your website, WhatsApp, and Telegram
Conversation logs and detected intents you can review to close real gaps
Prompts, retrieval settings, and permitted tools that you own

What the work includes

A focused engagement covers the decisions, implementation, and handoff needed to solve the problem cleanly.

Knowledge grounding

Turn your documents, FAQs, policies, and product information into a retrieval pipeline the assistant can cite, rather than relying on model memory.

Assistant behaviour

Define the tone, the persona, and the refusal boundaries, plus the specific tool calls the assistant is allowed to make so it can read real data instead of guessing.

Channel deployment

Embed the chat experience in your Next.js site and connect the same assistant to WhatsApp or Telegram, sharing one knowledge base and one conversation model.

Handoff, logging, and tuning

Route unresolved questions to a human with the transcript attached, then review intents and unanswered questions to improve the assistant over time.

A clear path from diagnosis to delivery

You see what is being changed, why it matters, and how the result is verified.

01
Map the real questions
Start from the questions your customers and team actually ask, not a generic script. This becomes the ground truth for what a correct answer looks like.
02
Assemble the knowledge
Clean and structure the content the assistant is allowed to use, and decide explicitly what it must never answer.
03
Build and connect
Implement retrieval, prompts, and permitted tool calls, then wire up the interface and the messaging channels.
04
Test against real conversations
Run out-of-scope prompts, adversarial questions, and failure cases before customers ever see it.

What I can build

The engagements I take on for this service, described so you can tell whether any of them match what you need.

Website assistant grounded on your docs

An embedded assistant that answers from your published help content and cites where each answer came from.

WhatsApp and Telegram support bots

The same assistant in the messaging apps your customers already have installed, with the conversation state preserved across channels.

Product and pricing answers with live lookups

Tool calls that read current data from your backend, so the assistant reports real stock, pricing, or order state instead of a cached guess.

Lead qualification and structured handoff

The assistant captures the details a sales team needs, then passes a clean summary to your inbox or CRM rather than a raw transcript.

Internal knowledge assistant

A private assistant for your own team that answers from internal documentation, runbooks, and onboarding material.

Multilingual answers from one source of truth

Serve customers in the language they write in without maintaining a separate knowledge base per language.

Frequently asked questions

Practical answers before we define the project scope.

How is this different from a generic AI chatbot?: A generic chatbot answers from whatever the language model happens to know, which is why it invents policies and prices. The chatbot I build answers from retrieval over content you supply, with citations, explicit refusal boundaries, and a defined handoff to a human. You can inspect and correct the behaviour instead of accepting it.
Can the chatbot use my real business data, such as order status?: Yes. Rather than only reading documents, the assistant is given a defined set of permitted tool calls — for example looking up an order, checking availability, or creating a lead. Each call is implemented against your real backend and scoped so the assistant cannot reach data it should not.
Will it work on WhatsApp and Telegram?: Yes. The assistant logic and knowledge base are shared, so the same capability can run on your website, on WhatsApp, and on Telegram without maintaining three separate systems. Channel-specific behaviour such as message formatting is handled per platform.
What happens when the assistant does not know something?: That case is designed for rather than left to chance. The assistant is instructed to say it does not know, and the conversation is routed to a human with the transcript and the retrieved context attached, so your team gets the question instead of a guess.
Can I update the answers myself after launch?: Usually, yes. Because the answers come from your content rather than from hard-coded prompts, most changes are a content edit rather than a code change. I document the exact process during handoff so your team can keep the assistant accurate.
Which AI provider do you build with?: I build with the OpenAI API for the model layer, combined with your own content for grounding. The provider choice is a straightforward swap if you later want to compare models or costs, because the retrieval and tool layers are kept separate from the model call.

How this engagement is scoped

Pricing: Custom scope — quoted after a short discovery call
Typical timeline: A focused assistant is typically live within a few weeks
Ownership: No retainers required. You own the prompts, knowledge base, and code.

Bring me the problem—not a finished technical brief.

Share the product, the current constraint, and what a better outcome looks like. I'll help define the right scope.

Start a conversation