CodeYour development team using Artificial Intelligence with method
Your team knows how to code. Coding with AI is a different skill.
We build a flow that makes productivity come with quality inside the tools your team already uses: full context for the agents, automated review and a complete DevOps flow.
Who it's for
Development teams that have not adopted coding agents yet, or adopted them with no process.
When one or two people use AI very well and the rest do not.
When throughput went up, but so did the incidents and the rework.
The problem
An agent with nothing configured is a fast autocomplete.
The number of features shipped goes up, but the cost arrives later: incidents, new code born legacy, the same short blanket as always.
45% of AI-generated code samples introduce an OWASP Top 10 flaw, and the security pass rate has stalled at 56% even as models improve. Bigger models do not write safer code.
Veracode, GenAI Code Security Report, 2025 and 2026What you get
We put in a complete working flow that makes productivity come with quality, inside the ecosystem you already use.
We build a full context environment for the agents, automated review, and a complete DevOps flow that makes delivery come with quality.
We work with what you already use
- Claude
- Codex
- CodeRabbit
- GitHub Actions
- GitLab CI
- Confluence
- Jira
- Trello
Common questions
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Do you need access to our repository? And what do you sign about it?
It is not mandatory. With access we help more and help faster, with less back and forth. Our contract also carries clauses forbidding us from using a client's proprietary source in another project. Either way it can be done without access; it will simply take more work from your own team.
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How much of my team's time does this take? Do you do it with us, or hand it over configured?
We need a few hours from one key person who knows the business and the process. Budget 2 to 4 hours a week. The rest is ours: the team gets the flow and the training with everything already built and running.
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We already use Copilot and Cursor, and half the team likes it and half hates it. Are you going to tell us to switch tools?
No. Any paid subscription to a large model does the job today, and the one your team uses is the right one. The gain is in what goes around it: the repository's conventions, the examples, the tests, the rules and the flow — that context is what makes the agent get your code right.
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How do I show the board it improved? What do you measure before and after?
With four numbers the board reads without translation: the time between writing a change and it being in production, the time it spends in review, the share of changes that break or have to be reverted, and how long a new developer takes to ship their first.
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Our repository is big, old and thinly tested. Does that rule it out, or is it exactly the case?
It is the typical case, and usually where the biggest gain is. Understanding code nobody understands any more, writing characterisation tests before touching behaviour, and documenting what exists are exactly the tasks an agent helps most with, and that is what unlocks the speed afterwards. A big repository needs context work, because the model does not read all of it, and that work is ours.
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.