AI Chatbot Development Company in India

Description

Service AI chatbot development for your website and app

WavX builds custom AI chatbots for websites and apps that answer from your own documents, look things up in your systems and hand over to a person when they should. This page covers how one is built, a planning range for the build, what it costs to run each month, where chatbots fail, and when a subscription tool is enough.

Talk to a technology expert Last updated 2 October 2026

What a custom AI chatbot does

A custom AI chatbot answers questions in plain language on your website or inside your app, using a large language model from OpenAI or Anthropic and the information you give it. WavX builds it around three things a ready-made widget usually does poorly:

Answer from your own material. Policies, price lists, product pages and help articles, kept current, instead of whatever the model happens to remember.

Use your systems. Look up an order, check a booking slot, create a lead in Zoho or HubSpot, or open a ticket in Zendesk or Freshdesk.

Hand over to a person. When the question is outside its brief, the customer is unhappy, or the answer carries risk, the conversation moves to your team with the history attached.

The same chatbot can also run on WhatsApp. That channel has its own rules and charges, covered on the WhatsApp AI chatbot page.

If you are still deciding whether to build at all, the guide on how to add an AI chatbot to your website covers the choice from the owner's side.

How it works

Every message takes the same path. Drawn as a diagram it is seven boxes in a row:

Chat widget. The customer types a question on the site or in the app.

Your server. The message goes to a backend that WavX builds. The API key for the model stays here. It is never placed in the browser, where anyone could copy it.

Checks on the way in. The backend screens the message. Is it within scope? Is it an attempt to make the bot ignore its instructions? Has the customer asked for a person?

Context. The backend gathers what the model needs: the instructions, the recent conversation, and the relevant passages from your documents. Fetching those passages is retrieval-augmented generation .

Model call. Instructions, context and question go to the model, which writes a reply or asks to use a tool such as an order lookup.

Checks on the way out. The reply is checked against rules you set, for example no prices that are not in the source and no advice in restricted areas.

Reply, log or handoff. The customer sees the answer, the exchange is logged for review, or the conversation is passed to a person.

Steps 3, 6 and 7 are what separate a chatbot you can leave running from a demo. How to design them is covered in the guide to chatbot guardrails and human handoff.

What it costs to build

The figures below come from the cost model behind WavX's AI cost calculator . They are planning ranges, not quotes. The model adds the base price to the selected features, multiplies by a data factor, and gives a range from 15% below that subtotal to 25% above it.

Scope

How the subtotal is reached

Planning range

Timeline band

FAQ chatbot on a website, no custom data

Base ₹1,50,000

₹1,27,500 to ₹1,87,500

3–6 weeks

Answers from your documents, with human handoff and CRM

(₹1,50,000 + ₹70,000) × 1.25 for documents = ₹2,75,000

₹2,33,750 to ₹3,43,750

The same, plus WhatsApp and Hindi

(₹1,50,000 + ₹70,000 + ₹80,000 + ₹50,000) × 1.25 = ₹4,37,500

₹3,71,875 to ₹5,46,875

6–10 weeks

Assumptions: one brand, a knowledge base that already exists in writing, and systems that offer a usable API. Change any selection in the calculator to see the range move. A firm price is quoted after scoping. The AI chatbot development cost guide prints wider tiers, up to enterprise builds, and compares them with subscription platforms.

What it costs to run

The build is paid once. Four things are paid every month, and the first scales with use.

Cost

Paid to

What drives it

How it is kept down

Model usage

OpenAI or Anthropic

Tokens in and tokens out. Every reply resends the instructions, the retrieved passages and the conversation so far

A smaller model for routine questions, shorter prompts, caching of the repeated part

Hosting

Your cloud account

The backend, the database and the document index

Sized to real traffic

Logs and monitoring

Your cloud account or a logging tool

How many conversations are kept, and for how long

A retention period you choose

Maintenance

Your development team

Model versions being retired, documents changing, new kinds of question

Budgeted yearly

For a sense of scale on the first line: as of October 2026 Anthropic lists Claude Haiku 4.5 at $1 per million input tokens and $5 per million output tokens, and the same pricing page works an example of 10,000 support conversations of about 3,700 tokens each, which comes to roughly $37 on that model. Larger models cost several times more per token, and prices change, so read the provider's page before budgeting. OpenAI's pricing page has the same structure, with separate prices per million tokens for input, cached input and output.

Maintenance is the line owners forget. WavX's published guide puts software maintenance at 15–25% of the build cost per year; see app maintenance cost per year .

Where chatbots go wrong

It answers from memory instead of from your documents. The reply sounds right and is wrong. This is the reason step 4 exists.

The source documents disagree. Two versions of a refund policy produce two answers on two days.

It is talked out of its instructions. A user tells the bot to ignore its rules. OWASP lists prompt injection as LLM01 in its 2025 Top 10 for LLM applications.

It keeps going when it should stop. There is no route to a person, so an upset customer argues with software.

Nobody reads the logs. Wrong answers continue for weeks because no one is looking.

If you already have a bot doing these things, start with the troubleshooting guide on wrong answers listed under related pages below.

What the chatbot should not do without a human

Anthropic's own guidance on reducing wrong answers ends with a caution: the techniques reduce hallucinations, they do not remove them, and critical information should always be validated. Design the chatbot on that basis. A person should confirm before any of these take effect:

Refunds, credits, discounts and price exceptions.

Medical, legal, tax or financial advice specific to the customer.

Any change to an order, a booking or an account that cannot be undone.

Commitments about delivery dates, stock or contract terms that are not in the source documents.

Complaints, and any conversation in which the customer has asked for a person.

The chatbot can collect the details and prepare the case. The decision stays with your staff.

How it is tested before launch

WavX builds a test set with you before building the chatbot: real questions from your inbox, call notes and chat history, each with the answer your team would accept. The set includes questions the bot must refuse and questions it must hand over. The chatbot is run against the set, the failures are read one by one, and the set is run again after every change to the instructions, the documents or the model. The same set shows whether a cheaper model is good enough for your questions.

After launch, a sample of real conversations is reviewed on a schedule agreed with you, and each failure found is added to the set.

When a ready-made tool is enough

Do not commission a custom chatbot if a subscription tool covers the job. Three examples, as their own sites describe them in October 2026:

Tool

What it offers

Fits when

Zoho SalesIQ (Zobot and Answer Bot)

A no-code bot builder, and a bot that answers common questions from your knowledge base

You already run Zoho and need FAQ answers on the site

Tidio (Lyro AI Agent)

An AI agent that works only from the support content you give it

A small store wants chat and FAQ answers without a project

Intercom (Fin AI Agent)

An AI agent priced at $0.99 per outcome, with a minimum of 50 outcomes a month, that also works with other helpdesks

A support team prefers to pay per resolved conversation

A custom build makes sense when the bot must act inside your own systems, when per-conversation fees at your volume add up to more than owning the software, when the data cannot sit inside a third party's product, or when the chatbot is part of a product you sell. You own the source code and IP WavX ships; the running costs are the ones in the table above.

AI chatbots are one part of AI solutions and automation at WavX.

Frequently asked questions

How much does it cost to build an AI chatbot in India?

Our cost model gives a planning range of ₹1,27,500 to ₹1,87,500 for a website FAQ chatbot with no custom data, and ₹2,33,750 to ₹3,43,750 for one that answers from your documents and hands over to your team through a CRM. These are planning ranges from the AI cost calculator, not quotes. A firm price follows a scoping call.

Which model do you build on, GPT or Claude?

We build on OpenAI and Anthropic Claude models. The choice is made by running your test questions through the candidates and comparing accuracy, speed and cost, and the backend is written so the model can be changed later without rebuilding the chatbot.

Will the chatbot make things up?

It can. Grounding it in your documents, telling it to say when it does not know, and checking replies before they are shown all reduce wrong answers, but none of them removes the risk. That is why risky requests go to a person and why conversation logs are reviewed after launch.

Can it answer in Hindi or Hinglish?

Yes. Multilingual support in English, Hindi and Hinglish is part of the service. The test set has to include questions typed the way your customers type them, including Hindi written in English letters, because that is where chatbots tested only in English fail.

How long does a chatbot take to build?

The cost model's timeline bands are 3 to 6 weeks for a build under ₹3,00,000 and 6 to 10 weeks up to ₹6,00,000. The usual causes of delay are documents that are not yet written down and systems the chatbot must reach that have no usable API.

Who owns the chatbot and the conversation data?

You own the source code and IP we ship. The model provider and cloud accounts should be in your company's name, so the logs sit in your account. As of October 2026, OpenAI and Anthropic both state that data sent through their APIs is not used to train their models by default.

Sources

Anthropic: Claude API pricing (model prices and the support-ticket cost example) · read 2 October 2026

OpenAI: API pricing · read 2 October 2026

Anthropic: Reduce hallucinations · read 2 October 2026

OWASP Top 10 for LLM Applications 2025: LLM01 Prompt Injection · read 2 October 2026

Zoho SalesIQ: Zobot chatbot builder and Answer Bot · read 2 October 2026

Tidio: Lyro AI Agent · read 2 October 2026

Fin AI Agent pricing (Intercom) · read 2 October 2026

OpenAI: Data controls in the OpenAI platform · read 2 October 2026

Anthropic Privacy Center: Is my data used for model training? · read 2 October 2026

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How to build an AI chatbot for your business

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