We build AI agents that read your data, use your business systems and finish real tasks from start to end. They ask a person before anything important happens, and every step they take is logged.
Goal
Follow up on every invoice more than 30 days overdue
Read: Pulled 23 overdue invoices from the ERP
Check: Matched recent payments, 4 were already paid
Draft: Wrote 17 reminder emails in your usual tone
Ask: Held 2 large accounts for finance to approve
Waiting for your approvalLog: Added a note to each customer record in the CRM
The basics
An AI agent is software that is given a goal instead of a script. It works out the steps, uses the tools it is allowed to use, checks what came back and keeps going until the job is done or it needs a person.
That makes agents a good fit for work that is too varied for fixed automation but too repetitive to deserve a skilled person's full attention.
Example agents
Each agent has one clear job, a short list of tools and a line it doesn't cross without a person. Here are some we are often asked about.
Reads the ticket, checks the customer's orders and account, and resolves routine requests such as address changes, resends and delivery questions.
Uses
A person decides: Refunds, and any customer who is clearly unhappy
Before each call, pulls the account history from your CRM, reads the prospect's website and news, and writes a one-page brief with talking points.
Uses
A person decides: The rep decides what to use. Nothing is sent to the prospect
Matches invoices to purchase orders and payments, chases missing documents by email and prepares journal entries for review.
Uses
A person decides: Posting entries and anything above your approval limit
Reads contracts, forms and shipping documents, pulls out the fields you need, checks them against your rules and routes anything unusual.
Uses
A person decides: Only the exceptions it cannot resolve
Answers staff questions from your actual policies and handles simple IT and HR requests, like password resets or leave balances, through your existing tools.
Uses
A person decides: Admin-level access changes
Reviews pull requests, writes test cases from user stories and turns vague bug reports into clear tickets with steps to reproduce.
Uses
A person decides: Engineers review and merge every change
Services
From deciding which task to hand over to keeping the agent reliable after launch, we cover the whole job.
We look at your processes with you and pick the tasks where an agent will pay for itself, and the ones where a simple automation would do the job better.
Learn more about AI Agent Strategy & Use-Case SelectionSingle-purpose agents built around one job in your business, with the instructions, tools and permissions that job actually needs.
For longer processes, several specialist agents that hand work to each other, with a coordinating agent that checks the result before it moves on.
Learn more about Multi-Agent SystemsWe connect agents to your ERP, CRM, help desk, email and internal APIs, including through the Model Context Protocol (MCP), so they can do real work.
Learn more about Tool & System IntegrationAgents that look things up in your documents and remember the context of a case, so they don't ask the same question twice or invent an answer.
Learn more about Agent Knowledge & MemoryTest suites built from your real cases, permission limits, spending caps and a full log of every step, so you can see exactly what the agent did and why.
Learn more about Testing, Monitoring & GuardrailsHow it works
The language model is only one part. Most of the reliability comes from everything we build around it.
The language model that reads the situation and decides what to do next. We pick it by testing, not by habit.
The agent's role, the rules it must follow and what a finished job looks like, written with the people who do the work today.
The specific actions it may take, such as looking up an order or drafting an email. No tool, no access.
Your documents, policies and the history of the current case, retrieved when needed rather than guessed.
Permissions, spending limits, approval steps and topics it must not touch, enforced in code rather than in the prompt alone.
A record of every step and tool call, plus tests that run on each change so quality doesn't quietly slip.
You decide, task by task. Most of our agents start at level 1 or 2 and move up only when the results show they can be trusted.
Level 1
The agent prepares the work and a person carries it out. A safe place to start for anything customer-facing.
Level 2
The agent does the work, then waits for someone to click approve before anything leaves the building.
Level 3
It acts on its own within set limits and sends a daily summary of what it did and anything it skipped.
Level 4
Fully automatic inside a narrow, well-tested lane, with alerts the moment something looks unusual.
Our process
Agents fail in the details: the odd invoice format, the customer who replies in two languages, the system that times out. Our process is designed to find those details before your customers do.
We sit with the people who do the job today and write down every step, every system they open and every exception they handle.
We choose measures such as time per case, error rate and the share of cases handled without help, and build a test set from your past cases.
One job, the fewest tools it needs, read-only access at first. Small agents are easier to trust, test and fix.
We run the agent against hundreds of past cases, compare its work with what your team actually did and fix what it gets wrong.
It runs in shadow mode first, then with a small group, with dashboards and logs so you always know how it is doing.
Technology
We pick the stack for each agent based on your systems, your data rules and the budget per task.
Why Infilon
We have been building web, mobile and ERP software since 2009. Most of the hard work in an agent project is integration and testing, which is what we do every day.
We test GPT, Claude, Gemini and open-source models on your task and recommend the one that gives the best result for the cost.
If a fixed workflow, a report or a simple chatbot would solve the problem, we will say so. Not every task needs an agent.
Since 2009
Building software from Ahmedabad
680+
Projects delivered
275+
Clients across the globe
FAQ
An AI agent is software that uses a large language model to work toward a goal, not just answer a question. It can look things up, use tools such as your CRM or email, check its own results and decide what to do next, all within the permissions you give it.
A chatbot talks. An agent does the work. A chatbot might tell a customer how to change their delivery address, while an agent can check the order, update the address in your system, confirm with the customer and log what it did.
Tasks that take several steps, involve more than one system and need a little judgement, but follow a pattern your team could explain to a new colleague. Invoice follow-ups, ticket triage, document checks and research briefs are good examples. Tasks that are rare, high-risk or depend on relationships are usually not.
It can be, if it is built carefully. We give each agent only the tools and permissions its job needs, require human approval for anything costly or irreversible, set spending and rate limits, and log every step. Most agents start by suggesting actions and only act on their own once the results have earned it.
It depends mainly on how many systems the agent connects to, how much freedom it has and how much testing the task needs. A focused agent for one job costs far less than a multi-agent system. After a short discovery phase we give you a fixed-scope estimate before development starts.
A first version working on your real data typically takes a few weeks. Getting it ready for production takes longer, because of integrations, security reviews and testing on past cases. We agree the timeline with you once the scope is clear.
Yes. If a system has an API, the agent can use it. If it doesn't, we can build a small integration layer or work through exports and scheduled imports. We have integrated with many ERPs over the years, including Cetec ERP.
Whichever suits the job. We often use LangGraph, the OpenAI Agents SDK or the Claude Agent SDK, and connect tools through MCP. For the model, we compare two or three options on your task and choose on accuracy, speed and cost.
In a 30-minute call we can usually tell whether an agent is the right tool, what a sensible first version would do and which decisions should stay with your team.
Book a CallNone of it is required. We can work it out together.
