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A chatbot that knows your business, not the whole internet
A general AI model knows a lot about the world and nothing reliable about your prices, policies or products. An AI chatbot built on your content closes that gap. When a customer asks a question, the system first searches your own knowledge base for the relevant passages, then asks the model to answer using only those passages. This approach is usually called retrieval-augmented generation (RAG).
The result is a bot that can say “our return window is seven days for unworn items” because that sentence is in your policy, not because the model guessed. When the answer isn’t in your material, a well-built bot says so and offers a person instead of inventing one.
Who gets the most out of it
- Clinics and hospitals answering questions about timings, departments, preparation instructions and appointment steps.
- Coaching institutes handling fee structures, batch schedules, eligibility and admission documents during admission season.
- D2C and e-commerce brands fielding sizing, shipping, returns and product comparison questions.
- SaaS companies that want help-centre answers inside the product without a support agent on every chat.
- Real estate developers sharing project details, amenities and site-visit booking with enquirers from ads.
The common thread is a steady stream of questions whose answers already exist somewhere in your documents — just not where the customer can find them quickly.
What the build includes
- Knowledge base setup — collecting, cleaning and splitting your documents, FAQs and pages so the right passage can be found for each question.
- Retrieval and answer logic — search tuned to your content, with instructions that keep answers grounded in it.
- Tone and format — replies that sound like your brand, in the languages you need, at a sensible length.
- Fallback behaviour — what the bot says when it doesn’t know, and how it avoids topics you don’t want it to discuss.
- Human handover — passing the conversation, with history, to a person on your team.
- Conversation logs and review — a record of real questions, unanswered ones highlighted.
- Deployment on your website, inside your app, or on channels such as WhatsApp or Telegram.
How we build it
We start by collecting the questions customers really ask — from your inbox, chat history or the people answering the phone. Those become the test set. Next we gather the content that answers them and find the gaps; often the first discovery is that some answers aren’t written down anywhere.
Then we build the retrieval pipeline and the answer prompt, and run every test question through it. We review the answers with you, adjust the content and instructions, and repeat until the results are consistently right. Only then does the bot go live, usually on one channel first, with logs reviewed closely in the early weeks.
The technology underneath
Answers are generated with models from OpenAI or Anthropic, called through their official APIs. Your content is converted into embeddings and stored in a vector index — often PostgreSQL with pgvector, or a dedicated vector database where scale calls for it. The backend is typically Python or Node.js, and the chat widget is a lightweight component for your site.
For messaging channels we use the official platforms: the WhatsApp Business Platform for WhatsApp chatbots and the Telegram Bot API for Telegram support bots.
What shapes timeline and cost
- State of your content — well-organised documents are quick to use; scattered or outdated ones need cleaning first.
- Number of channels and languages the bot must work in.
- Live data needs — a bot that checks order status or availability needs integrations, not just documents.
- Conversation volume, which drives ongoing model usage costs.
- Review depth — regulated or sensitive topics need more testing and stricter limits.
Mistakes that make chatbots unpopular
- No way to reach a human. Customers forgive a bot that says “let me connect you”; they don’t forgive one that loops.
- Feeding it outdated content. The bot will confidently repeat last year’s prices if that’s what it was given.
- Letting it answer everything. Medical, legal or financial specifics often need firm limits and a referral to a person.
- Never reading the logs. The questions it couldn’t answer are the most useful output of the whole project.
Frequently asked questions
Will the chatbot make up answers?
Any AI model can produce a wrong answer, so we design against it: answers are based on retrieved passages from your content, the bot is instructed to say when it doesn’t know, and test questions are checked before launch. Logs let you spot and correct problems quickly.
What content can the bot learn from?
Documents such as PDFs and Word files, website pages, FAQs, product data and past support replies you are comfortable using. We help decide what to include and what to leave out.
Can it answer in Hindi, Gujarati or other languages?
Current models handle many languages, including major Indian languages, reasonably well. We test the languages you need with real questions before committing to them, because quality varies by language and topic.
How do we keep it up to date?
When your content changes, the knowledge base is refreshed. Depending on the setup this can be automatic from a source such as your website or a shared folder, or a simple update step your team runs.
Can it go on WhatsApp as well as our website?
Yes. The same knowledge base can serve several channels. WhatsApp requires the official Business Platform and follows Meta’s messaging rules, which we explain before building.
Talk to us about ai chatbot development
Chatbots trained on your own content that answer customer questions in your brand's voice, around the clock.