AdoptAI inside the processes you already run
The question isn't whether AI helps. It's where it goes.
AI put inside a process that already runs: the data collected, the systems connected, the decision it is allowed to make written down, and the people who use it trained until the gain no longer depends on us.
Get in touch 2 to 3 weeks · R$18,000 to R$35,000, fixed · half on signing
Who it's for
Companies with a real operation and enough of it to hurt: industry, logistics, distribution, agriculture, health, financial services, cooperatives. The person who calls is usually not from IT. It is operations, finance, or whoever noticed the team had started using AI without being asked — or that it has not started, and does not want to find out too late that it should have.
The clearest sign you're in the right place: someone in the company is doing the job a system should be doing, and nobody can say whether a model could take it.
The problem
Either the tools are already in the building and nobody has agreed anything, or they are not in yet and nobody knows where to start. Both stop in the same place: the decision about where AI belongs — which step it touches, which data it may read, what a person still has to sign off, and what happens on the day the answer is wrong. Without that, a licence for everyone buys a lot of private experimentation and no change to the process — and the first real cost is not a bad answer, it is finding out what has been pasted into which window.
What we do
We go through the process before proposing anything, including the part where it turns out AI is not the answer. Sometimes the step should be deleted rather than automated, and sometimes a tool you already pay for covers it. When that is the answer we say so, and the cheapest deliverable we have is the work we talked you out of.
When it is the answer, we put it where the decision is. That usually means the unglamorous half first: getting at data that is spread across systems that do not talk, giving the model the context it needs to be right rather than plausible, and putting a person in the path of anything that carries money or risk. Rules in code rather than in a habit, a record of what was decided and on what basis, and an alert when something leaves the range you expected.
Then we train the people who will actually use it, on their own work rather than on a demo, and we write down what is allowed and what is not. This is the part every workshop skips and it is the only reason the gain outlives us. You get the code, the prompts, the documentation and the rules. Running it, changing it or switching it off does not depend on us.
What we hear
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Half the team pays for a model out of their own pocket and nobody knows what they paste into it.
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Someone approves or classifies every case by hand, and the criteria live in that person's head.
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We know AI could do some of this, and nobody knows where to start.
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We ran a pilot, everyone was impressed, and nothing about the process changed.
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The data that would answer that question is spread across four systems that don't talk.
Why not just
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Buy everyone a licence.
Licences are the cheap part, and you may already have them. What a licence does not do is decide which step of which process the model is allowed to touch, what it may read to do it, and who signs off when it is wrong. Without that you have paid for private experimentation — useful, invisible, and impossible to hand to the next person.
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Wait until it settles down.
It is not going to settle down, and waiting has a cost you are already paying in shadow use. What can settle is your side: the process mapped, the data reachable, the rule about what a person must approve. Do that and swapping the model underneath becomes a small decision rather than a project.
Why this isn't our opinion
95% of generative AI pilots produced no measurable impact on P&L. The pattern among the ones that survived is integration into a process that already runs, not a tool bolted on beside it.
MIT NANDA, State of AI in Business, Aug 2025
Where AI belongs in your process, and where it does not
Fixed scope, fixed price, agreed before it starts. We go through the process the way somebody who has to live with the result would: what it costs today, who touches it, where it breaks, which step a model could actually take, and what a person still has to sign off.
What you get: a plain-language summary of where the gain is and what it is worth, the steps we would leave alone and why, what has to be true of your data before any of it works, a written rule for what needs a human, and a prioritised order to do it in.
It stands on its own. If you take the document and implement it with your own team, or with somebody else, it did its job — and we would rather that than an engagement nobody needed.
Common questions
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Do we need to have our data in order first?
No, and if you did you would not need us for the interesting part. Getting at data that is spread across systems that do not talk is most of the work, and it is in scope. What we do need is for somebody to be allowed to tell us how the process actually runs, as opposed to how the documentation says it runs.
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Will this replace anyone's job?
That is a question for you rather than for us, and we would rather you asked it out loud before we start than afterwards. What we look for is the step where a person is doing work a system should do — approving the same kind of case a hundred times, retyping data, waiting. Nothing we build decides who does what.
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What if the answer is that AI is not the answer?
Then we say so, and that is the cheapest thing we sell. Sometimes the step should be deleted rather than automated, and sometimes a tool you already pay for covers it. A diagnostic that ends in "do not build this" has done its job.
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How do you charge?
Fixed price, agreed in writing before anything starts, with the scope and the deadline in the same document. The diagnostic is priced on its own so you can start there without committing to what comes after it.
Tell us what's broken. The answer may be that you don't need us.
The call is free and ends in a yes, a no, or "you can handle this yourselves". The person who answers is the person who would do the work. We work remotely across Brazil, and in person in the north of Rio Grande do Sul — the agroindustry and the cooperatives there are close enough to visit, and a consultancy in São Paulo is not going to.