AI chatbot development grounded in your own knowledge
I build website and support chatbots that answer from your documents, qualify the visitor, book the next step and pass the conversation to a person when it needs one. Answers are grounded with retrieval-augmented generation (RAG), so the bot draws on your knowledge base instead of guessing.
- Sharp Edge lead-qualifying assistant
- DocuMind: fully on-device RAG
- Handover to a human with full context
Chatbots that finish a job
A useful chatbot is measured by what happens after the conversation: a qualified lead in your CRM, a booked appointment, a support question resolved without a ticket, or a clean handover to your team. I start from that outcome and work backwards to the knowledge, tools and integrations the bot needs, then build only what serves it.
- Website lead assistants that qualify and book
- Support bots that answer from help docs and policies
- Internal assistants over SOPs and wikis
- Document Q&A where files must stay private
Case: a lead assistant for Sharp Edge Property Group
For Sharp Edge Property Group in California, I built a website assistant on n8n and OpenAI. It qualifies inbound leads, answers property questions from a knowledge base and books appointments. When a person should take over, it hands the conversation to a human agent with the full context, so the visitor does not have to start again.
RAG knowledge bases, including local-first
Retrieval is where most chatbots succeed or fail. I prepare your content so the right passage is found for each question, keep answers tied to that source material, and have the bot say so when the knowledge base does not cover something. For sensitive material everything can stay on your hardware: DocuMind, my local-first RAG document assistant, runs retrieval entirely on-device so documents never leave the machine. LegalEase AI, a semantic legal research assistant built with MindsDB, Gemini and Ollama for MindsDB Quest 019, applied the same ideas to legal text.
Handover, guardrails and integrations
A chatbot should know its limits. I set clear rules for what it answers, what it refuses and when it escalates, and the handover carries the whole transcript so your team picks up mid-conversation. Bots connect to the tools you already run, such as GoHighLevel, HubSpot, Calendly, Slack or Google Sheets, through n8n or custom code, and chat activity can be tied back to the ad click that started it.
Rates from $20/hour
Hourly for open-ended work, fixed price for defined projects. The first call is free and there's no obligation.
Frequently asked questions
Will an AI chatbot make up answers?
It can if it is built carelessly. I ground answers in your own content with RAG, instruct the bot to admit when the knowledge base has no answer, validate structured replies, and route uncertain cases to a person. That makes mistakes less likely and much easier to catch.
Can the chatbot book appointments and update my CRM?
Yes. The Sharp Edge assistant books appointments directly, and the same pattern works with GoHighLevel calendars, Calendly or HubSpot. New leads can be created, tagged and routed in your CRM, and your team can be alerted in Slack or by SMS when a conversation needs them.
Which AI model will my chatbot use?
Whichever fits the job: OpenAI, Anthropic Claude or Google Gemini through their APIs, or an open model running locally through Ollama when data cannot leave your servers. I am a Claude Certified Developer – Foundations, but the choice comes down to answer quality, speed, cost and privacy for your use case.
What does AI chatbot development cost?
Rates start from $20/hour. A well-defined chatbot, such as a website lead assistant with booking and handover, can be quoted as a fixed price after a free intro call. Model usage is a separate running cost that depends on how many conversations you handle.