Custom AI Chatbot Development That Answers From Your Own Data
RAG chatbots for your website, WhatsApp and help desk, grounded in your documents, wired into your tools, and built by a senior team that will tell you when an off-the-shelf bot is enough.
A good fit if
- Customers ask the same twenty questions and your team answers each one by hand
- You tried a chatbot builder and it confidently made things up
- Your customers message you on WhatsApp and expect a reply at night
- You need a bot that looks things up in your systems, not one that recites an FAQ
A custom AI chatbot is only as good as what it reads. We build chatbots that search your own documents, policies and product data before they answer, admit it when they do not know, and pass the conversation to a person when it matters.
Services
Custom AI chatbot development services
One team designs the conversation, builds the retrieval, connects your systems and watches quality after launch.
Custom AI chatbot for your website
A chat widget in your brand that answers from your site, help centre and documents, captures leads into your CRM and hands over to a person inside the same window.
WhatsApp AI chatbot for business
Built on the official WhatsApp Business Platform, so customers get answers, order updates and booking links in the app they already use, with approved templates for the messages you send first.
RAG development services
Ingestion, chunking, embeddings and retrieval over PDFs, help articles, spreadsheets and databases, kept in sync as your content changes, with the source attached to every answer.
Internal knowledge assistants
A private chatbot for staff that answers from policies, SOPs and past tickets, respects who may see what, and lives in Slack, Teams or your intranet.
Help desk chatbots
First-line answers on your support desk, ticket triage, and a clean handover to an agent with the full conversation and a short summary attached.
Fixing a chatbot you already have
Wrong answers, slow replies, a model bill that keeps climbing. We test the current bot against real questions, find where retrieval fails and fix the parts that matter.
RAG chatbots
What is a RAG chatbot, and why does it matter?
A plain language model answers from memory. A RAG chatbot answers from your documents. That difference decides whether you can put it in front of customers.
It looks before it answers
Retrieval augmented generation means the bot first searches your content for passages that fit the question, then writes its reply from those passages.
Answers you can trace
Each reply can link to the page or document it came from, so customers can check it and your team can see exactly why the bot said what it said.
Updates without retraining
Change the price list or the returns policy and the next answer reflects it. No model training, no developer ticket.
It knows when to stop
When nothing relevant comes back, the bot says it does not know and offers a person, instead of inventing an answer that sounds right.
Permissions carry through
Staff assistants only retrieve from documents that user is allowed to open, so a new hire never gets answers drawn from board papers.
Chatbot vs AI agent
Chatbot vs AI agent: which does your business need?
Most businesses need a chatbot first. An agent comes next, once the chatbot has earned trust.
A chatbot answers
It explains, recommends and collects details. Opening hours, product questions, policy questions and lead capture are chatbot work, and that is where most of the volume sits.
An AI agent acts
It books the slot, updates the order, raises the refund or files the ticket, using tools and permissions you grant it one at a time.
Human approval for risky actions
Anything that moves money, changes a record customers rely on or cannot be undone waits for a person. The bot prepares it, a human confirms it.
Your data stays where you decide
Hosted in your own cloud account, with model providers that do not train on your data, or on open models you run yourself. We settle this before we build.
Off the shelf is sometimes right
If a builder tool covers your questions and your only source is a public website, we will tell you to use it. Custom work earns its place when the bot needs your systems, your rules or private data.
How it runs
Our custom AI chatbot development process
- 01
Collect the real questions
Chat logs, inbox threads and support tickets, sorted into what the bot answers, what it hands over and what it refuses.
- 02
Build retrieval on your content
Your documents cleaned, indexed and tested against those real questions before any chat window exists.
- 03
Write the rules
Tone, handover triggers, approval steps and what the bot must never say, written down and agreed with you.
- 04
Launch, measure, tune
A staged rollout, a test set we rerun on every change, and a regular review of unanswered questions so gaps get closed.
Cost
How much does a custom AI chatbot cost?
Chatbot cost has two parts: the build, and the running cost each month for model usage, hosting and messaging. We quote the build as a fixed price after a scoping call and set out the monthly running costs before you commit.
Get a fixed quote→| What moves the price | Why |
|---|---|
| Channels | A website widget is one build. WhatsApp, Instagram, Slack or your help desk each add a connector, channel rules and their own testing. |
| State of your content | Clean help articles index quickly. Scanned PDFs, messy spreadsheets and knowledge spread over five tools need cleaning first. |
| Answers only, or actions too | A bot that answers is a smaller build than one that books, refunds or edits records, because every action needs permissions, approval steps and testing. |
| Integrations | Each CRM, booking system, order database or help desk the bot reads from or writes to adds build and test time. |
| Hosting and data rules | Running inside your own cloud account or on self-hosted open models takes more setup than a managed API, and can cost less per month at high volume. |
| Monthly running cost | Model usage grows with conversations and answer length, and WhatsApp messages carry Meta's own fees. We size these with you, so the chatbot cost per month is known before launch. |
Tools we use for this
- OpenAI
- Anthropic Claude
- LangChain
- LangGraph
- LlamaIndex
- Pinecone
- pgvector
- Python
- FastAPI
- Next.js
- WhatsApp Business Platform
- AWS Bedrock
FAQ
AI chatbot development questions
A RAG chatbot uses retrieval augmented generation. When a question comes in, it first searches a store of your own content, such as help articles, policies, product sheets or past tickets, and pulls out the passages that match. The language model then writes the answer from those passages, not from whatever it absorbed in training. The result is a bot that talks about your business accurately, can show where each answer came from, and stays current when you edit the underlying documents.
A chatbot holds a conversation: it answers questions, explains options and collects details. An AI agent goes further and takes actions in other systems, such as booking an appointment, updating an order or creating a ticket. The line matters because actions carry risk. We usually start with a chatbot grounded in your content, then give it tools one at a time, with a person approving anything that spends money, changes customer records or cannot be reversed.
It depends on the channels, how much content the bot must read and how clean it is, whether it only answers or also takes actions, and how many systems it connects to. A website chatbot over a tidy help centre is a much smaller project than a WhatsApp agent that books appointments and checks orders. We give a fixed quote for the build after a scoping call, plus an estimate of monthly running costs, so you see the full picture before agreeing to anything.
After launch you pay for what the bot uses: model calls, which grow with the number and length of conversations, hosting for the retrieval store and the app, and channel fees, such as Meta's charges on WhatsApp Business Platform messages. If you want us to review conversations and keep improving answers, that is an optional monthly retainer. We estimate each line from your expected volume before you commit, and the accounts sit in your name, so you see the real bills.
A focused website or WhatsApp chatbot over content that already exists is a matter of weeks, not months. The time goes into gathering real questions, cleaning documents and testing answers, more than into the chat window itself. Bots that take actions in your CRM or booking system take longer, because every action needs permissions and testing. We break bigger builds into stages, so a useful version goes live early and grows from there.
Yes. We build on the official WhatsApp Business Platform through Meta or an approved provider, not on unofficial tools that risk your number being banned. The bot answers from your content, can share booking links or order updates, and hands the chat to a person in a shared inbox when needed. Messages you start, like reminders, use templates approved by Meta, and the platform has its own fees, which we explain before you sign up.
If your bot only needs to answer from public pages on your site, a builder tool may be enough, and we will say so. A custom AI chatbot for business makes sense when it must read private documents, respect user permissions, connect to your CRM or booking system, follow rules a template cannot express, or keep data inside infrastructure you control. Custom also means you own the code and are not locked into one vendor's pricing.
A chatbot answering from a model's general memory will, sooner or later. That is why we ground every answer in retrieval over your own content and instruct the bot to say it does not know when nothing relevant is found. We test it against a set of real questions before launch and after every change. No AI system is perfect, so for anything costly to get wrong, a person reviews or approves before the answer or action goes out.
Where you decide, and we decide it with you before building. Options include keeping the retrieval store and app inside your own cloud account, using model providers whose business terms exclude training on your data, or running open models on infrastructure you control. Conversation logs are kept for as long as you set. You own the accounts, the code and the data, so nothing is held hostage if you change suppliers later.
Ask to see how they stop the bot inventing answers, and how they test quality before and after launch. Ask who writes the code, whether you will own it and the accounts, where your data will be stored, and what monthly running costs look like. A good AI chatbot development company will also tell you when a cheaper off-the-shelf tool fits better. Kavion is a small senior team in India working remotely with clients worldwide, replying within one business day.

