01
The same question, fifty times a day
Prices, lead times, the status of an order, how to file something. The answer already exists in writing somewhere, and someone drafts it again every time.
AI agents in your operation
Put AI agents to work on the repetitive tasks inside your operation
This isn't a chatbot in the corner of your website. These are software workers with judgment that live connected to your systems and do one concrete job every day: answering what gets asked all the time, sorting and replying, moving data where it belongs and flagging anything out of the ordinary.
Connected to the tools your company already uses
Our own operation runs on them
With written limits and human review where it matters
The breaking point
You hired people to think, and their day goes into answering the same thing
Repetitive work doesn't disappear because your team is good: it gets spread across everyone and eats the hours that should go to what matters. And when high season arrives, the client feels it.
02
The inbox as the system of record
Everything lands mixed together in the same mailbox and someone reads it, sorts it and hands it out by hand. What's urgent gets lost among what could have waited.
03
Nobody is watching when something goes off the rails
An order stuck, a payment that never arrived, a client who stopped buying. It surfaces weeks later, when it's already a complaint instead of a heads-up.
What they do
The work an agent can take off your hands in the first week
An agent doesn't replace a role: it takes over a task. These are the ones that work best once the operation already lives in a system.
Answering what gets asked all the time
It answers with your own information —prices, lead times, policies, the status of an order— and hands a person whatever it doesn't know, with the conversation summarized.
Sorting and routing what comes in
It reads the email, the form or the message, works out what it's about, tags it and drops it in the queue of whoever owns it, with the urgency it actually has.
Drafting and waiting for your sign-off
The reply to the client, the meeting summary, the proposal built from a template. It arrives written and someone on your team approves before it goes out.
Reading documents and pulling out the data
Supplier invoices, delivery notes, contracts, PDF forms. It extracts what the process needs and leaves it in the system in the right format.
Moving data between systems with judgment
When the data arrives incomplete or written twenty different ways, the agent works out what it belongs to before saving it, instead of rejecting it.
Watching and flagging when something drifts
It checks the operation over and over and alerts whoever is responsible when something goes out of the ordinary, with the context for why it stood out.
Where the line is
Three of our pages talk about AI: here is the difference, one line each
We say it here because it's the first question that comes up in the diagnostic, and because picking the wrong entry point makes the project more expensive for no reason.
If the path is always the same
When the process can be written as rules —this comes in, this gets calculated, this goes out— you don't need an agent: automating it is cheaper and more predictable.
That's workflow automation.
If something has to be read, decided or drafted
When what arrives is messy and someone has to understand it before acting, that's where the agent's territory begins. That's this page.
It usually lives alongside automation: the agent understands, the rules execute.
If what's missing is the whole system
When there's nowhere to put the agent because the operation lives in spreadsheets and emails, the application gets built first.
All three are branches of the same trunk: keeping what's already in production running is application support and maintenance, and that's where an agent goes to work.
How it's built
What's inside an agent you can trust without watching it all day
A loose agent on top of a generic model dazzles in the demo and disappoints in the operation. What makes it trustworthy is boring, and it's exactly what we build.
One task, not a general-purpose assistant
We define what it does, what it doesn't do and when it has to hand over. An agent with one clear job can be evaluated; one that does everything can't.
Access to your data, with permissions
It sees what it needs to see to do its job and nothing more. Permissions are written down, reviewed, and can be revoked without touching the rest of the system.
Human review where it hurts
Anything that puts money, a deadline or a client relationship on the line goes through a person. The agent leaves the work done and someone approves.
A record of every call it made
What it saw, what it did and why. When someone asks about a case there's an answer with a timestamp, not a guess about what the model was thinking.
Scope
Four answers define the size of your first agent
There's no catalogue price because the work is defined by the task, not by the word «agent». This is what we look at before proposing anything.
Which concrete task it will do
A single one, written in one sentence. If it doesn't fit in a sentence it isn't a project yet: it's an intention, and it's worth splitting.
Where it will get its information
An agent answers with what it can look up. If your information is scattered across emails and spreadsheets, putting it in order is part of the work.
What it's allowed to do on its own
Reading and proposing isn't the same as writing into the system or replying to the client. That line is agreed with you and put in writing.
Who supervises it afterwards
An agent gets reviewed the way you review someone who just started. We can hand you the dashboard to do it, or stay on top of it ourselves.
In the diagnostic we go through these four points, and out of that comes a proposal with scope, deliverables and commitments in writing. Book a free assessment.
What we already run
Our own company is run with agents, and that's the proof we can show you
We don't say it as a sales line: it's how we work every day, and it's the one case we can open up from the inside without asking a client's permission.
A fleet of agents, each with its own trade
Each one has its role, its memory and its rules: development, support, marketing, project management. We write them and we keep them running.
Our own caseThey work inside our systems
They don't chat off to the side: they open tasks, log time, review work and leave a trail in the same management system the people work in.
Our own caseWith review and with limits
Anything that goes out to a client passes through a person. The controls we apply on your project are the ones we already apply to ourselves, including the ones we learned the hard way.
Our own caseWe tell the whole story in the systems we run.
Fit
When we will tell you not yet
You want a chatbot on the website and nothing else
There are catalogue tools for that; they install in an afternoon and cost a fraction. An agent earns its keep when it has to do work inside your systems, not when it only has to talk.
The information isn't in order yet
If what the agent needs to look up lives in three people's heads and in loose folders, the first project is putting it into a system. We tell you that before charging you for an agent with nothing to read.
Questions
What people ask us before putting an agent to work
Is an AI agent the same thing as a chatbot?
No. A chatbot talks; an agent does work. The practical difference is whether, when the conversation ends, something got done in your system —a case classified, a record saved, a task opened— or just an answer on a screen.
Do we have to change the systems we use today?
Almost never. The agent connects to what you already have: your CRM, your email, your ticketing system, your database. We only propose replacing something when there's no legitimate way to reach its information.
Will my data be used to train a public model?
That's not what we do. We work with configurations where your information is used to answer and not to train, and that is put in writing in the contract. If a case requires the data never to leave your infrastructure, it's designed that way from the start.
And what if the agent gets it wrong?
It's designed assuming it will. That's why anything with consequences goes through a person, everything is logged, and the agent has explicit instructions to hand over when it isn't sure instead of making something up.
How many agents do I need to start with?
One. You pick the most repetitive, most bounded task, put it to work and measure it. Starting with several at once stretches out the rollout and makes it impossible to tell which one is failing.
Next step
Tell us which task your team repeats every day
With that task and the systems it lives in, we can already tell you whether an agent solves it, whether automating it with rules is cheaper, or whether the information has to be put in order first.
Prefer to write to us? Tell us what you need.