Compliance-first delivery
What an AI chatbot development company in Coimbatore should deliver
An AI chatbot development company in Coimbatore should deliver a chatbot that answers from your own catalogue, prices and policies rather than the open internet, handles Tamil, English and Tamil typed in English letters, captures enquiries with the details your sales team needs, and hands over to a person with a summary when it is unsure. It should be tested on your real customer messages before launch and run on the channels your customers already use.
Most chatbots fail on the second question, not the first
Search for an AI chatbot development company in Coimbatore and page one mixes national agencies, directories and a page written for another city. None of them says much about the questions Coimbatore customers actually ask. A dealer wants to know whether a 1 HP monoblock will lift water to the third floor. A parent asks about hostel fees and bus routes in a mix of Tamil and English. A shopper sends a photo of a saree and asks if it comes in another colour. The first answer is easy. The follow-up is where scripted bots give up.
An AI chatbot handles the follow-up because it reads your actual content: the product catalogue, spec sheets, fee structure, return policy, delivery areas. It answers in the customer’s own words and language, asks for missing details, and knows when to stop and call a person. That is the difference between a chatbot that collects leads and one that collects complaints.
Radiatus builds these remotely for Coimbatore businesses through our chatbot development practice, with on-site visits by agreement. If you need software that also acts in your back-office systems (drafting quotations, reading purchase orders), that is an AI agent, a different build. For wider custom software in the city, see our Coimbatore software page.
Where Coimbatore businesses use AI chatbots
Illustrative designs by sector, not client case studies.
| Business | What the chatbot handles | Where it hands over |
|---|---|---|
| Pump, motor and equipment makers | Model selection questions from dealers and farmers, spec sheets, nearest dealer, warranty terms, service request intake with a photo of the nameplate | Custom configurations, price negotiations and anything that needs an engineer’s judgement |
| Colleges and schools | Courses, eligibility, fee structure, hostel and transport, scholarship rules, admission dates, campus visit booking | Fee concessions, special cases and anything about an individual student’s application |
| Textile, saree and jewellery showrooms | Collections, availability by branch, timings, offers, photo-based “do you have this in…” questions, appointment booking | Custom orders, bulk pricing and complaints |
| Real estate and builders | Projects, unit types, location details, approvals the buyer should ask about, site-visit booking | Price negotiation, loan advice and booking amounts |
| Hospitals, clinics and labs | Timings, doctor schedules, test preparation, report collection and booking requests | Anything clinical: symptoms, results and advice always go to staff |
| Hotels, restaurants and travel | Room and package questions, menus, directions, booking requests, group enquiries | Payments, refunds and special requests |
AI chatbots we build for Coimbatore businesses
Four kinds of chatbot. All of them answer only from content you approve, log every conversation and hand over cleanly.
Product and catalogue assistant
The problem
Your catalogue runs to hundreds of models and variants, and dealers or customers phone the office to ask which one fits.
How we do it
We load your catalogue, spec sheets and price list into a retrieval index the chatbot answers from, with links back to the right PDF. For selection questions it asks for the details that matter (application, head, flow, power supply, budget) before suggesting options, and it never invents a model number.
What you get
- Answers grounded in your catalogue
- Guided selection questions
- Links to the right spec sheet
- Enquiry captured with all details
Admissions and enquiry assistant
The problem
During admission season the phones never stop, and the same twenty questions are answered hundreds of times a day.
How we do it
A chatbot trained on your prospectus, fee structure, eligibility rules and calendar answers parents and students on your website and messaging channels, books campus visits and passes serious enquiries to the admissions office with a summary. It is built to handle a seasonal surge.
What you get
- Answers on courses, fees and dates
- Campus-visit booking
- Lead summary for the admissions team
- Peak-season load tested
Lead qualification on your website
The problem
Website visitors leave without contacting you, or send one-line enquiries your sales team cannot act on.
How we do it
The chatbot answers the visitor’s questions, then asks the few qualifying questions sales actually needs (quantity, location, timeline, budget) and creates a lead in your CRM or inbox. Every lead arrives with the conversation attached.
What you get
- Qualifying questions you choose
- Leads into your CRM or inbox
- Conversation attached to each lead
- Weekly report of top questions
After-sales and service intake
The problem
Complaints arrive by phone and message without serial numbers, photos or addresses, and technicians go out unprepared.
How we do it
The chatbot collects the details a technician needs (product, serial number, photo, location, symptom), checks simple troubleshooting steps with the customer, and opens a ticket. Anything that needs a site visit or a warranty decision goes to your service team.
What you get
- Structured service tickets
- Photos and serial numbers captured
- Basic troubleshooting first
- Handover to the service desk
Rule-based bot, AI chatbot or AI agent: which do you need?
| Rule-based bot | AI chatbot | AI agent | |
|---|---|---|---|
| How it works | Menus and keyword buttons you script in advance | A language model answering from your content | A language model that also uses tools to act in your systems |
| Handles | A short, fixed set of questions | Open questions, follow-ups, mixed languages | Tasks such as drafting a quotation or checking a warranty record |
| Fails when | The customer types something you did not script | Your content is missing or out of date | Permissions and approvals are not designed carefully |
| Running cost | Platform fee only | Model usage per conversation, usually small | Higher: several model calls per task |
| Choose it for | Order status by number, simple menus | Product, admissions and service questions | Back-office work; see our AI agents page |
Many good chatbots combine the first two: buttons for the five most common journeys, and AI for everything typed in free text.
How AI chatbots handle Tamil, English and Tanglish
Coimbatore customers rarely stick to one script. In a single chat you may see English, Tamil script, Tamil typed in English letters (“rate evlo?”) and a voice note. Current language models understand this far better than older bots did, but quality varies, so we work in four steps:
- Collect real messages. A few hundred past enquiries from your inbox, chat logs and phone notes, anonymised, become the test set.
- Decide the reply language. Usually the customer’s own language and script, with English for technical terms and model numbers that customers recognise.
- Test before launch. We score answers for correctness and tone in each language and fix the weak spots, often by adding Tamil phrasing to your content rather than changing the model.
- Handle voice carefully. Voice notes are transcribed and answered in text, with a person taking over when the transcript is unclear. For full phone conversations, see AI voice agents.
Which channels should a Coimbatore chatbot run on?
| Channel | Good for | Things to know |
|---|---|---|
| Website chat | Visitors comparing products, parents reading the prospectus | No per-message fee; you control the design and data |
| Instagram and Facebook messages | Showrooms, restaurants and colleges whose customers find them on social media | Runs through Meta’s messaging platform; replies must follow its policies |
| Dealers, existing customers and anyone who prefers to message | Business-specific AI assistants are allowed; general-purpose AI assistants are not, under Meta’s terms since January 2026. Opt-in and per-message charges apply; see our guide to the rules and costs | |
| Inside your app | Existing customers who are already logged in | The chatbot can personalise answers safely because it knows who the user is |
Start with the channel where most enquiries already arrive, prove it there, then add the next one. One knowledge base and one conversation log can serve all of them, so staff see every channel in one inbox.
How to stop a chatbot giving wrong answers
- Answer only from approved content. The chatbot retrieves from your catalogue, policies and FAQs; if the answer is not there, it says so and offers a person.
- Keep prices and stock current. Connect the price list or stock file directly, or put a date on it, rather than pasting numbers into a prompt that nobody updates.
- Never promise what staff must decide. Discounts, delivery dates, refunds and admissions decisions are handed over, by rule.
- Test every change. Each content or model update is checked against the test set before it goes live.
- Read the transcripts. A weekly review of conversations that ended badly finds the gaps in your content faster than any dashboard.
Our guide to taking retrieval-based AI to production goes deeper on grounding and evaluation.
Consent and personal data in a chatbot
A chatbot that collects names, phone numbers and addresses is processing personal data. Under India’s DPDP framework, with most obligations applying from 13 May 2027, show a short notice before collecting details, collect only what the enquiry needs, keep conversation logs only as long as you have a reason to, and let people ask for their data to be deleted. On WhatsApp and similar channels, follow the opt-in rules as well. Our DPDP checklist covers the details.
What does an AI chatbot cost to run?
The model bill for a chatbot is usually small. An illustrative conversation of four exchanges, each sending about 3,000 tokens of instructions, retrieved content and history and getting about 200 tokens back, at Anthropic’s list prices read on 1 November 2026:
| Model | Price per million tokens (input / output) | Per conversation | 5,000 conversations a month |
|---|---|---|---|
| Claude Haiku 5.5 | $0.10 / $0.50 | About $0.0016 | About $8 |
| Claude Sonnet 5.5 | $2.00 / $10.00 | About $0.032 | About $160 |
Before caching, hosting and tax. Prices from Anthropic’s pricing page; other providers price similar tiers. Messaging channels add their own per-message charges.
Most chatbots run well on the smaller model, with the larger one kept for hard questions. The bigger costs are building the knowledge base, testing in your languages and the staff time spent on handovers. Our AI cost guide explains the build side.
How to tell whether your chatbot is working
Agree these measures before launch and review them weekly for the first two months. They tell you more than a count of conversations.
| Measure | What it shows | What to do if it is poor |
|---|---|---|
| Answered without handover | Share of conversations the chatbot finished on its own, correctly | Read the handovers and add the missing content |
| Wrong answers found in review | Answers a reviewer marked as incorrect or misleading | Fix the content or the rule behind each one, then add it to the test set |
| Leads with complete details | Enquiries passed to sales with everything they need to call back | Change the qualifying questions or their order |
| Time to a human reply after handover | How long a customer waits once the bot hands over | Staff the inbox at peak hours or set honest expectations in the handover message |
| Top unanswered questions | What customers ask that your content does not cover | Often the most useful product and website feedback you will get |
Who keeps the chatbot’s knowledge up to date?
A chatbot is only as current as the content behind it. Name an owner on your side for each source (catalogue, price list, fee structure, offers, policies) and agree how changes flow: a shared folder the knowledge base re-reads every night, a direct connection to the price or stock file, or a simple admin screen. Expired offers and old fee structures cause more complaints than any model limitation, so every time-sensitive item should carry an end date the chatbot respects.
Questions to ask an AI chatbot development company
- Will the chatbot answer only from our content, and what does it say when it does not know?
- How will you test it in Tamil, English and mixed messages, using our real enquiries?
- How does it hand over to a person, and what does the person see?
- Which channels will it run on, and what does each cost per message or per month?
- How do we update prices, stock and policies without calling you?
- Where are conversations stored, for how long, and who can read them?
- What happens when the AI model behind it is retired or changes?
- Who owns the knowledge base, the conversation logs and the code?
From first call to a live chatbot
A fixed-scope project, usually four to six weeks for one channel, priced by milestone after a short discovery.
Questions and content
We collect your most common questions, your catalogue, prices and policies, and agree what the chatbot must never answer.
Test set in your languages
A few hundred real past enquiries, anonymised, with the right answers, including Tamil and mixed-script messages.
Build and test
Knowledge base, chatbot, handover and lead capture on the first channel, tested daily against the set until it reaches the agreed score.
Soft launch
Live for a share of visitors with staff watching transcripts. Then full launch, a weekly review rhythm and the next channel when you are ready.
Chat and AI inbox products we run
We operate live chat and AI conversations in our own products, so handover, inbox design and transcript review are things we use every day.
Insight OS
Analytics, live chat & leads in one script Live chat, WhatsApp and call options in one bubble on your website, with every enquiry landing in a single inbox. An AI chatbot can answer first and hand over inside the same inbox. Visit insight.hi4.in
Appointment Reminder App
WhatsApp & SMS reminders that grow into an AI inbox Its shared AI inbox answers replies to WhatsApp and SMS reminders on the Claude API, with staff taking over the conversation whenever the AI is unsure. Visit appointmentreminderapp.comGuides and related pages
Guides
Questions about AI chatbot development in Coimbatore
It builds chatbots that answer customers from your own catalogue, prices and policies, in the languages your customers use, on your website and messaging channels. A good one also captures enquiry details for your sales team, hands over to a person with a summary when the bot is unsure, and tests everything on your real customer messages before launch.
Yes. Current models understand and write Tamil, English and Tamil typed in English letters, including mixed messages. Quality varies with the subject and phrasing, so we test on a few hundred of your real enquiries before launch, choose the reply language rule with you, and hand over to staff when the chatbot is not confident.
A rule-based bot follows menus and keywords you script in advance and fails when a customer types something unexpected. An AI chatbot reads your content and answers free-text questions and follow-ups in the customer’s own words. Many businesses use both: buttons for common journeys, AI for everything else.
Yes. The same chatbot and knowledge base can run on your website, Instagram and Facebook messages, WhatsApp and inside your app, with every conversation in one inbox. Each messaging channel has its own policies, opt-in rules and charges; on WhatsApp, business-specific assistants are allowed while general-purpose AI assistants are not.
Building one is quoted after a short discovery; we do not publish rates. Running costs are usually modest: in our illustrative example, 5,000 four-exchange conversations a month cost about $8 in model usage on a small model and about $160 on a larger one, before messaging-channel charges, hosting and tax.
About four to six weeks for one channel in our plans: a week to gather questions and content, a week to build a test set in your languages, two weeks to build and test, and a soft launch with staff watching transcripts. Messy or outdated content is the most common cause of delay.
It is designed not to. It answers only from content you approve, says when it does not know, never promises discounts, delivery dates or decisions that staff must make, and is tested before every update. Weekly transcript reviews catch gaps early. No chatbot is perfect, which is why the handover to a person matters.
Yes. Admissions and enquiry assistants answer questions on courses, eligibility, fees, hostels, transport and dates, book campus visits and pass serious enquiries to the admissions office with a summary. They are load-tested for admission-season peaks. Decisions about individual applications always stay with staff.
Planning a chatbot for your Coimbatore business?
Tell us what customers ask most, which channels they use and which languages they write in. A senior engineer will reply with how we would build it and what we would test first.