AI Chatbot Development
An AI chatbot is a conversational interface that answers questions or moves a sale forward, built on a large language model but grounded in your own documentation, product data and policies rather than the model's general knowledge alone. At Beacon Coders that grounding — usually through retrieval-augmented generation against your actual content — is the difference between a chatbot that gives your specific refund policy and one that confidently invents a plausible-sounding answer. Projects start at ₹1,50,000 / $1,800 and typically run 4 to 10 weeks.
Where this differs from an agent
A chatbot responds to messages inside a conversation; it does not, on its own, take multistep actions across your systems the way an AI agent does. Many projects combine both — a chatbot interface backed by an agent doing work behind it — but the chatbot itself is scoped here as the conversational layer: understanding what a visitor is asking, answering from your grounded content, and handing off to a person or a scoped agent action when the question goes beyond what it should answer alone. If your project's core need is a system that takes actions — processing a request, updating a record — rather than answering questions, start on the agent page instead.
What's included
A knowledge base built from your existing documentation, help articles, product data and policies, structured for retrieval rather than dumped in wholesale
A knowledge base built from your existing documentation, help articles, product data and policies, structured for retrieval rather than dumped in wholesale
Retrieval-augmented generation so the chatbot answers from your actual content and can cite where an answer came from, rather than generating from the model's general training alone
Retrieval-augmented generation so the chatbot answers from your actual content and can cite where an answer came from, rather than generating from the model's general training alone
Conversation design covering common paths, edge cases, and the specific points where the chatbot should hand off to a human rather than keep guessing
Conversation design covering common paths, edge cases, and the specific points where the chatbot should hand off to a human rather than keep guessing
Integration with your CRM or helpdesk
Salesforce, HubSpot, Zendesk or a custom system — so conversations create or update real records
A defined escalation path to a human agent, with the full conversation history passed along so a customer never has to repeat themselves
A defined escalation path to a human agent, with the full conversation history passed along so a customer never has to repeat themselves
Guardrails against the chatbot answering outside its scope
pricing it should not quote, commitments it should not make, topics it should redirect rather than address
Testing against real customer questions, including deliberately ambiguous and adversarial ones, not just a scripted demo flow
Testing against real customer questions, including deliberately ambiguous and adversarial ones, not just a scripted demo flow
Analytics on what visitors are actually asking, which is often as valuable as the chatbot itself for spotting gaps in your documentation
Analytics on what visitors are actually asking, which is often as valuable as the chatbot itself for spotting gaps in your documentation
What's not included
AI chatbot development does not include multistep task automation across systems — see AI agent development for that. It does not include adding a single AI feature inside an existing product interface without a conversational component — see AI integration. It does not include training a custom model from scratch, or guaranteeing zero incorrect answers, which no chatbot grounded in a language model can honestly promise; we design for graceful escalation instead.
Frequently asked questions
How we build a chatbot
Discovery maps the questions your team actually gets — from support tickets, sales calls, or FAQ pages — and identifies which ones the chatbot should own outright and which need a documented handoff to a person, as the first stage of the process behind every AI development project. Build happens against your real documentation from the first sprint, not placeholder content, so retrieval quality is tested honestly throughout. Testing specifically probes how the chatbot handles questions outside its knowledge base, since a confident wrong answer does more damage than an honest "I don't know, let me connect you with someone."