Compliance-first delivery
What an AI agent development company in Coimbatore should build
An AI agent development company in Coimbatore should build agents for specific back-office tasks: turning enquiries into draft quotations, reading purchase orders into draft sales orders, logging dealer complaints with warranty checks, chasing payments from Tally data, or preparing export documents. Each agent gets a short list of tools, read-only access by default, and a person who approves anything it writes. Directories list many firms; ask each one for traces and test results from an agent it actually runs.
An agent is worth building when the next step depends on what it just found
Search for an AI agent development company in Coimbatore and you will mostly find directory listings, with very little about what an agent would actually do inside a Coimbatore business. So here is the plain version. An AI agent is software that reads something messy (an emailed enquiry, a PDF purchase order, a dealer’s voice note), decides which of your systems to look at, gathers what it needs and prepares the next action. A person then approves it. It earns its keep where the path through a task changes from case to case.
Most automation in a factory office does not need an agent. If the steps never change, a fixed workflow is cheaper, faster and easier to trust, and our AI automation team builds those. If customers simply need answers, an AI chatbot is the better tool. This page is for the cases in between: work your staff do with judgement today, across Tally, Excel, email and WhatsApp, that an agent can prepare for them.
Radiatus builds agents remotely for Coimbatore teams, through our AI agent development practice, with site visits by agreement. We also run tool-using AI in our own products, so the rules on this page (approval before writes, traces, spend caps) are ones we live with. For quotation tools, portals and dashboards built as ordinary software, see our custom software page for Coimbatore.
Six AI agents we build for Coimbatore businesses
Each spec lists the tools the agent gets, what it may do alone, what needs approval, and how we measure it. These are design patterns, not client case studies.
Quotation agent for pumps, motors and valves
The problem
Enquiries arrive as emails, WhatsApp messages and visiting cards, each with partial specifications. Your application engineers spend hours turning them into quotations, and slow replies lose orders.
How we do it
The agent reads the enquiry, extracts what it can (duty point, application, voltage, material, quantity), asks the customer for anything missing, shortlists models from your product and price data, and prepares a quotation in your template. Tools: read the product and price database, read the customer master, create a draft quotation. Nothing is sent until an engineer approves it.
What you get
- Draft quotation in your template
- Shortlist with the reason for each model
- Missing-information questions drafted for the customer
- Measured: enquiry-to-quote time and edit rate
Purchase-order intake agent
The problem
Customer POs arrive as PDFs and scans in a dozen layouts. Someone retypes each one, and mismatches against the quotation are caught late, sometimes at dispatch.
How we do it
The agent reads each PO, maps customer item codes to yours, compares price, quantity, delivery date and terms with the accepted quotation, and creates a draft sales order. Mismatches are listed at the top for a person to resolve. Tools: read quotations and item master; write draft orders only.
What you get
- Draft sales order per PO
- Mismatch report against the quotation
- Item-code mapping that improves as you correct it
- Measured: lines extracted correctly, mismatches caught
Dealer service and warranty agent
The problem
Dealers and technicians report faults by phone, WhatsApp text, photos and Tamil voice notes. Warranty checks mean digging out the dispatch date by serial number, and tickets go missing.
How we do it
The agent turns each report into a ticket, transcribes voice notes, checks warranty status from the serial number and dispatch records, suggests a likely fault category and spare parts from past tickets, and drafts the reply. A service coordinator confirms before any replacement is promised.
What you get
- Ticket per complaint with photos attached
- Warranty status with the evidence shown
- Suggested fault category and spares
- Measured: time to first response, repeat visits
Collections follow-up agent
The problem
Outstanding payments sit in Tally while the accounts team chases the loudest customers, and polite reminders go out late or not at all.
How we do it
The agent reads outstanding bills and ledger extracts from Tally (read-only), drafts reminders that match each customer’s history and your tone rules, schedules follow-ups, and flags disputed invoices to a person. It never edits the ledger and never threatens; frequency caps and escalation rules are enforced in code.
What you get
- Daily list of reminders ready to send
- Ledger extract attached to each
- Disputes routed to accounts
- Measured: days sales outstanding, reminders sent on time
Export documentation agent
The problem
Exporters re-key the same order data into the invoice, packing list and certificate-of-origin request, and a single mismatch against the letter of credit can hold up payment.
How we do it
The agent prepares draft export documents from the order and shipment details, checks descriptions, quantities and HS codes against past shipments and the LC terms you upload, and lists every discrepancy for your documentation team. It drafts; your team signs and files.
What you get
- Draft invoice and packing list data
- Discrepancy list against LC terms
- Consistency check against past shipments
- Measured: discrepancies caught before submission
Rejection and 8D report agent
The problem
Foundries and machine shops record rejections on paper or spreadsheets, and writing an 8D report for an OEM customer takes a quality engineer most of a day.
How we do it
The agent gathers inspection records, rejection counts and past corrective actions for the part, drafts the 8D structure with the data filled in, and suggests containment steps that worked before. The quality engineer writes the root cause and signs the report.
What you get
- Draft 8D with data and history filled in
- Similar past cases and their actions
- Pareto of rejection reasons
- Measured: hours per report, repeat defects
What building AI agents in Coimbatore actually involves
The data is spread out before it is messy. In most Coimbatore engineering firms, product data sits in spreadsheets kept by the design team, prices in a sales manager’s file, customer and ledger data in Tally, and the real history in email threads and WhatsApp groups. An agent needs read access to the right parts of all of them. The first weeks of a project are usually spent agreeing which copy of a price list is the true one and giving the agent a clean, read-only way to reach it.
Tamil and English arrive mixed. Dealers, technicians and suppliers write in English, Tamil script and Tamil typed in English letters, often in the same message, and send voice notes in Tamil. Current models handle this far better than they did, but quality varies by task and accent. We test on a sample of your own messages and recordings before promising anything, and we keep a person in the loop wherever the agent is unsure.
Tally stays the book of record. Agents read from Tally and prepare entries; your accounts team posts them. That keeps the ledger trustworthy and keeps you inside Tally’s licensing, which treats remote and virtual access under its Tally Virtual User rules. If your Tally still runs on an office server, our cloud migration page for Coimbatore covers moving it safely first.
The city is not only factories. Guidance Tamil Nadu’s GCC page counts more than 80 global capability centres in Coimbatore, alongside its software parks and product companies. Those teams usually want an agent inside their own platform, with evals and traces their parent company’s risk team can read. The same discipline applies; only the paperwork grows.
Personal data rules are coming into force. Most of India’s DPDP obligations apply from 13 May 2027. An agent that reads customer names, phone numbers or ledgers should keep logs that redact personal data, keep processing inside India where possible and work under a processing contract. Our DPDP checklist lists the controls.
Agent, workflow or chatbot? A decision table with Coimbatore examples
| Task | Build | Why |
|---|---|---|
| Send a dispatch notification when an invoice is posted | Workflow | Same steps every time; no judgement needed |
| Answer “what is the price of model X?” for dealers | Chatbot with your price data | An answer, not an action; see our chatbot page |
| Turn an incomplete enquiry into a quotation | Agent | The next step depends on what is missing and which models fit |
| Read POs in twelve different layouts into draft orders | Agent, or a workflow with one AI step | Layouts vary; the checks against the quotation are fixed |
| Approve a credit note | Neither: a person | Financial and irreversible; the agent may prepare it at most |
| Prepare an 8D report for an OEM | Agent with a quality engineer signing off | Gathering data from several records is variable work; the conclusion is human |
The most common design we ship is a workflow with one agentic step in the middle. It costs less to run and is easier to test than a fully autonomous agent.
How much autonomy should an agent get in a Coimbatore office?
Decide action by action, and write it down before building. Our default for a first agent:
| Action | Example | Rule |
|---|---|---|
| Read | Look up stock, a customer’s outstanding, a pump curve | Automatic, logged |
| Draft | Quotation, sales order, reminder, 8D outline | Automatic; appears in a queue for a person |
| Ask the customer | Request a missing duty point or a photo of the nameplate | Automatic within agreed templates; staff can see every message |
| Send or commit | Send a quotation, confirm a replacement, issue documents | Only after a person approves |
| Financial or ledger change | Post an entry, change a price, approve a credit note | Not available to the agent |
As evals and pilot data build trust, some drafts can move to “send automatically below a threshold”, for example routine reminders. That is a decision you take with numbers in front of you, not on day one.
Is your data ready for an AI agent?
Run this checklist before you ask anyone for a quote. Each “no” is work to do first, and it is usually cheaper than the agent itself.
- Is there one agreed product list with current model codes and specifications?
- Is the price list in a file or system the agent can read, with a date on it?
- Are customers and item codes consistent between Tally, Excel and your ERP?
- Can you export a few months of past enquiries, POs or complaints to build a test set?
- Do you know who will approve the agent’s drafts, and how quickly?
- Is the system the agent reads reachable remotely and securely, not only from the office LAN?
How to choose an AI agent development company in Coimbatore
Directory listings rank for this search, but a listing tells you nothing about whether a firm can run an agent in production. Ask every shortlisted company the same questions:
- Show us the trace of a real agent run: every tool call, input and output, with personal data redacted.
- How will you test it before launch? How many real cases, and what score must it reach?
- What will one task cost to run, at what volume, on which model? Show the arithmetic.
- Which actions can the agent take alone, and which need approval? Put it in writing.
- How does the agent reach Tally or our ERP, and can it change anything there?
- How does it handle Tamil, mixed-script messages and voice notes? Can we test with our own samples?
- Who owns the code, prompts, test set and logs if we part ways?
- What happens when the AI model it uses is retired or changes?
- Will you tell us if a simple workflow would do the job?
A firm that answers these clearly, with examples, is worth a pilot. A firm that answers with a feature list is not. Working with someone outside Coimbatore is fine if they meet you in your working hours and can visit when a shop-floor walk-through genuinely helps.
What does an AI agent cost for a Coimbatore business?
We do not publish rates. Build effort depends on how many systems the agent touches, whether it writes into them, and how big the test set must be; running cost depends on volume and model choice. Our AI agent development cost guide sets out engineer-weeks by tier and the token arithmetic with current prices. As a rough guide to where the six agents above sit:
| Agent | Systems touched | Likely tier in our cost guide |
|---|---|---|
| Collections follow-up | Tally (read), email or WhatsApp | A or B: read-mostly, drafts only |
| Rejection and 8D report | Inspection records, spreadsheets | A or B |
| Purchase-order intake | Mail inbox, quotations, ERP or Tally drafts | B |
| Quotation | Product and price data, customer master, quotation template | B |
| Dealer service and warranty | Tickets, dispatch records, messaging, voice notes | B |
| Export documentation | Orders, shipments, LC terms, document templates | B, or C if documents are filed electronically by the agent |
The cheapest route to a useful agent is a narrow first task with a clean data source and a quick approver. Start there, measure it, and widen only when the numbers say so.
What to measure during the pilot
Agree these numbers before the pilot starts, and measure the “before” column with your own staff in the same weeks. Without a baseline, nobody can say whether the agent helped.
| Measure | How it is counted | What it tells you |
|---|---|---|
| Time per task | From the enquiry or document arriving to the approved output, median and slowest tenth | Whether customers actually get faster replies |
| Edit rate | Share of drafts the approver changed, and how much | Whether the agent saves work or just moves it |
| Error caught | Mismatches, missing details or wrong models the agent flagged that staff would have missed | Value beyond speed, especially on POs and export documents |
| Wrong drafts | Drafts that would have caused harm if sent unreviewed | How much autonomy is safe later |
| Approver minutes | Time staff spend reviewing per task | The real running cost; often larger than the model bill |
| Cost per task | Model tokens, hosting and messaging divided by tasks | Whether the economics hold at full volume |
Mistakes we see in AI agent projects at SMEs
- Starting with the hardest task. Quotations for engineered-to-order products are tempting, but a PO intake or collections agent proves the approach faster and teaches the team how approvals work.
- Letting the agent post to Tally. Convenience today, an audit problem tomorrow. Drafts only.
- No owner for approvals. If drafts wait two days for a manager, the agent saves nothing. Name the approver and a response time.
- Testing on invented examples. Real enquiries are shorter, ruder and more ambiguous than the ones people make up. Use last quarter’s actual messages.
- Ignoring the people who do the work today. The engineer who reads pump curves knows the edge cases. Bring them into the test set, not just the sign-off.
- A shared admin login for the agent. Give it its own account with the narrowest access that works, so every action is traceable.
A first agent in about seven weeks
One task, one agent, measured against how your team does it today. Fixed scope and price by milestone, agreed after discovery.
Pick the task
We sit with the people who do it (remotely, or on site by agreement), write the tool list and approval rules, and say honestly if a workflow would do.
Test set and baseline
A sample of real enquiries, POs or complaints with the right answers, plus how long the task takes today and how often it goes wrong.
Build and evaluate
Tools with read-only access first, the agent itself, and daily test runs against the sample. Tamil and voice inputs tested on your own data.
Supervised pilot
Your staff approve every draft. You get time saved, error rate and cost per task, and decide whether to widen it.
Tool-using AI we run ourselves
Both products below take real actions for real users, which is where our rules on approvals, traces and handover come from.
Clinic Answering Service
HIPAA-compliant AI receptionist for US practices, 24/7 A voice agent that books real appointments and checks insurance eligibility while the caller waits: tools, permissions and handover in production. Visit clinicansweringservice.com
Appointment Reminder App
WhatsApp & SMS reminders that grow into an AI inbox Its AI inbox and agent work through replies to WhatsApp and SMS reminders on the Claude API, with staff taking over whenever the agent is unsure. Visit appointmentreminderapp.comGuides and related pages
Guides
Technologies
Glossary
Where we work
Questions about AI agent development in Coimbatore
Agents for specific back-office tasks in local businesses: turning enquiries into draft quotations, reading purchase orders into draft sales orders, logging dealer complaints with warranty checks, preparing payment reminders from Tally data, drafting export documents and pulling together 8D reports. Each agent prepares work in your systems for a person to approve.
Yes, as a reader. The agent reads outstanding bills, ledgers, customers and items and prepares drafts, while your accounts team posts entries. Keeping Tally as the book of record protects the accounts and keeps you within Tally’s licensing for virtual and remote access, which uses Tally Virtual User entitlements.
Current models handle Tamil, English and Tamil typed in English letters reasonably well, and speech-to-text for Tamil voice notes has improved, but accuracy varies with the task and the speaker. We test on a sample of your own messages and recordings before committing, and route anything uncertain to a person.
A chatbot answers questions, usually customers’ questions, from your content. An agent works through a task: it looks things up in several systems, decides the next step and prepares an action such as a quotation or a sales order. Many Coimbatore businesses need a chatbot for customers and an agent for the back office.
A first agent for one narrow task typically takes about six to seven weeks in our plans: a week to define the task, a week to build a test set, three weeks to build and evaluate, and a supervised pilot. Messy or scattered data adds time, so we check data readiness first.
We price after a short discovery and do not publish rates. Build effort depends on how many systems the agent touches, whether it writes into them and the size of the test set. Running cost depends on volume and model. Our AI agent development cost guide gives engineer-weeks by tier and worked token costs.
No. Radiatus is registered in Dover, Delaware and as an MSME in India, and works remotely on Indian time through video calls, WhatsApp or Teams and shared repositories. Site visits for a shop-floor walk-through, a workshop or a go-live are arranged by agreement when they help.
It can be. Agents get read-only access by default and only to the systems they need, credentials stay on the server, logs redact personal data, and model providers are configured so API data is not used for training. Hosting stays in India where possible, under a processing contract that fits the DPDP Act.
Have a task in mind for an AI agent?
Tell us what the task is, which systems it touches (Tally, Excel, email, WhatsApp, ERP) and roughly how often it happens. A senior engineer will reply with whether it needs an agent, a workflow or neither.