AI chatbot developer who builds RAG assistants grounded in your own content, with human hand-off and honest limits
I build AI chatbots and RAG assistants that answer from your own documents, pages and policies instead of guessing. They can qualify leads, book calls, answer support questions and hand a conversation to a person with the history attached. I work with Claude, ChatGPT, Gemini and open models, test for wrong answers, and promise no perfect accuracy. You own the system.
What you get
- Use-case brief
- the assistant's job, scope, exclusions and success measures.
- Knowledge base
- cleaned and structured content ready for retrieval, with update instructions.
- Assistant
- a working chatbot on your website, WhatsApp or an internal tool.
- Behaviour rules
- tone, limits, refusals and the conditions for human hand-off.
- Integrations
- CRM, calendar, e-mail or ticket tools connected with minimal permissions.
- Test set
- a scored list of questions and answers, used before and after each change.
- Conversation log view
- a way for your team to read chats and mark wrong answers.
- Privacy notes
- what is collected, where it is stored and retention settings.
- Handover
- source code, prompts, deployment notes and a recorded walkthrough.
What a useful business chatbot is, and is not
A good business chatbot is a narrow tool that does a defined job reliably: answering common questions from approved content, qualifying an enquiry, collecting details, booking a call or routing a request to the right person. A bad one is a general-purpose model pasted onto a website that confidently answers anything, including things your business never said. The difference is design, and it is where I spend most of my effort.
Language models can produce fluent wrong answers. I do not hide that. I design around it with grounding, limits on what the assistant may say, visible sources where suitable, a clear way to reach a human, and testing against the awkward questions your customers actually ask. I cannot promise an assistant never makes a mistake, and I would be wary of anyone who does.
RAG: answering from your content
Retrieval-augmented generation, or RAG, means the assistant first searches your own material, such as your website, price lists, brochures, policies, help articles and past answers, and then writes its reply using what it found. That keeps answers tied to your information, makes updates easy because you change the documents rather than retrain a model, and lets the assistant say it does not know when nothing relevant is found.
A RAG system is only as good as its content and its retrieval. I clean and chunk the documents, remove contradictory or outdated pages, add metadata such as service, location and date, choose a retrieval method and test it with real questions. In reviews I commonly find that the problem is not the model but an old PDF that contradicts the current price list, so I fix the knowledge base before blaming the technology.
Chatbots that qualify leads and feed your CRM
My background is lead generation: 12+ years in paid media with 500+ brands. So I build chatbots as part of the sales path rather than a novelty. A lead-qualification assistant can ask the questions your team would ask, such as the service needed, the location, the budget range and the timing, capture the answers as structured fields and send them to a CRM or a WhatsApp notification, with the source campaign attached.
The assistant should not act like a pushy salesperson or invent offers. I set the tone, the rules about what it may promise and when it must pass the conversation to a person, and I log conversations so you can read them and improve the content. Outcomes can be reported against ad campaigns, so you can see which sources produce conversations that turn into customers.
Channels, models and integrations
The assistant can live on your website, inside WhatsApp through an approved provider such as WATI, or behind an internal tool for your own team, for example a knowledge assistant over your policies and past proposals. I use Claude, ChatGPT and Gemini through their APIs, and open models such as DeepSeek through Ollama where data must stay on your own infrastructure. I recommend a model by task, accuracy, speed, language and data-handling needs, not by fashion.
I connect assistants to other systems through APIs and MCP or OpenAPI workflows: a calendar for booking, a CRM for lead capture, a help desk for tickets, an order system for status lookups. Each connection has the smallest permissions that work, and write actions such as booking or changing a record require clear confirmation.
- Website chat widget, WhatsApp assistant or internal staff assistant
- Knowledge from your site, documents and help articles, kept up to date
- Lead capture to CRM, e-mail or WhatsApp with the source campaign
- Booking, ticket creation and order lookups through scoped API access
- A hand-off to a human with the conversation history
Testing, safety and privacy
Before launch I build a test set of real and awkward questions: ones with clear answers, ones the content does not cover, ones that try to push the assistant off topic, and ones that ask for private data. I score the answers, fix the content or the instructions, and repeat. After launch, conversation logs show where it fails, and I update the knowledge base on a schedule.
Chat conversations can contain personal data. I design to collect only what is needed, tell users they are talking to an automated assistant, avoid personal data in application logs, and set retention limits. Rules such as GDPR, UK GDPR, India's DPDP Act 2023 or CCPA/CPRA may apply to your use, and in health, finance or legal settings the assistant should not give advice. I build the safeguards, but I am not a lawyer, so confirm obligations with your own adviser.
How I work and what you own
I work remotely from India with weekly calls, a staging link for the assistant and same-working-day replies. We start with a narrow first use, measure it on real conversations, then widen it. I use AI tools throughout my own development and review every change myself.
The code, prompts, documents, vector index and accounts are yours, documented so another developer can maintain them. Usage costs for model calls are paid directly by you to the provider, and I explain what drives them so there are no surprises.
How the work runs
- 01
Use case and content review
A call to choose one job for the assistant, the questions it must answer and the documents that hold the truth.
- 02
Knowledge base preparation
Content cleaned, de-duplicated, labelled and checked for contradictions or outdated facts.
- 03
Design of behaviour
Tone, limits, what it must refuse, when it hands off to a person and what it records.
- 04
Build
Retrieval, model prompts, the chat interface or WhatsApp connection and the CRM or calendar integrations.
- 05
Test set and tuning
Real and difficult questions scored, failures traced to content, retrieval or instructions, and fixed.
- 06
Soft launch
A limited release with logging, so the first real conversations can be read and corrected.
- 07
Maintain and improve
Regular review of logs, content updates and extra use cases once the first one works.
Tools and proof
- Tools
- Claude, OpenAI ChatGPT API, Google Gemini, DeepSeek and Ollama, RAG pipelines, MCP and OpenAPI workflows, WATI (WhatsApp), Node.js and Next.js
- AI builds
- RAG pipelines, AI chatbots, lead qualifiers and knowledge-base assistants.
- Multi-agent platform
- A platform with 20+ concurrent AI agents running as Node.js background services, built end to end.
- Lead generation background
- 12+ years in performance marketing across 500+ brands, so assistants are built around the lead path.
- Education
- M.Tech in Computer Science and Engineering.
Questions about AI chatbot and RAG assistant development
RAG means the assistant searches your own documents first and then writes an answer from what it found. That keeps replies tied to your information, makes updates a matter of changing documents and lets it say it does not know.
Yes, any language-model system can. I reduce the risk with grounding in your content, limits on what it may say, testing against difficult questions, human hand-off and log review, but I cannot promise it will never be wrong.
Yes, through an approved provider such as WATI. WhatsApp has template and opt-in rules, so I design conversation flows to follow them and to hand over to a person when the assistant cannot help.
Yes. It can ask your qualifying questions, store the answers as fields and push the lead to a CRM, e-mail or WhatsApp with the source campaign attached, so your team can reply quickly.
It depends on the task, accuracy, speed, language and data needs. I work with Claude, ChatGPT and Gemini through their APIs, and with open models through Ollama when data must stay on your own servers.
I minimise what is collected, tell users they are talking to an automated assistant, keep personal data out of logs and set retention limits. Legal obligations depend on your country and sector, so your adviser should confirm them.
You do, directly to the model provider, from your own account. I explain what drives usage, such as message length and the number of conversations, and set limits so costs stay predictable.
AI chatbot developer by country and city
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