ProcessArtificial Intelligence inside the processes you already run
The question isn't whether AI is useful. It's where it goes in.
We find the bottleneck that stands to gain most from artificial intelligence, implement it, train the people and follow the result.
Who it's for
Companies with complex operations: industry, cooperatives, logistics, agriculture, health, financial services, agencies, software houses.
The problem
Either you do not use artificial intelligence yet, or you already use tools with no central decision and no training.
What is missing is the know-how to decide which process AI pays off in for the people doing the work — and whether it can carry autonomous agents at all.
95% of generative AI pilots produced no measurable impact on the bottom line. 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 2025What you get
A report, training, and the implementation of the right tool, the way to use it and the context it needs.
Where the process allows it, full automation by AI agents.
We work with what you already use
- Claude
- Codex
- CodeRabbit
- Microsoft 365
- Microsoft Teams
- Slack
- Notion
- Confluence
- Jira
- Trello
- ClickUp
- Asana
- Zendesk
- Salesforce
- HubSpot
- SAP
- TOTVS
- Power BI
- Tableau
Common questions
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How long until I see a return, and how do you prove it is there?
It depends on where the data and the systems you run today actually sit, because the bridges to them may have to be configured or built. With that base in place, the first cases go into production in 30 to 45 days. We measure the indicators before and after, and that is what proves the gain.
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Our data is in the ERP, in spreadsheets and on WhatsApp, and none of it is integrated. Do I have to sort that out before you come in?
No. That is exactly the work, and the work is ours. We start with the area whose data is easiest to reach, which is where the return shows up first.
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The AI will reach sensitive data — margins, payroll — for some of this. Where does that data sit, and what stops it becoming training for a model somewhere else?
The data stays where it already is. What leaves your systems is the passage the model has to read to answer that one question. Beyond that, the API contracts from Anthropic and OpenAI say that content is not used for training — which holds for the business plan, not for the personal account somebody signs up for on their own. Where sensitivity is high or regulation asks for it, the model can run inside your own cloud account (AWS, Google Cloud), and there it is guaranteed to stay fully isolated.
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I have people who have been here twenty years and will not want to learn it. Does the training cover that, or will we end up with a tool nobody uses?
Training is in scope. But what secures adoption is how the tool is fitted to the work. It has to sit inside the person's own flow and take steps away rather than add them. When it saves work for whoever uses it and is intuitive, adoption is assured.
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Six months after you deliver, am I running this myself or depending on you?
Whatever works better for your operation. We can hand over the source, the documentation and the access keys, and you carry on with your own IT team; or we run the support and the follow-up on what was built, for a recurring fee.
Tell us where AI stalled, or where you do not use it yet.
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. Remote across Brazil, in person in the north of Rio Grande do Sul, where agroindustry and the cooperatives are close enough to visit.