AI implementation and engineering

AI is already real. We get your company into it.

Knowing you need AI is the easy part. Having a process actually running with it is where almost everyone stalls. That is where we come in: we put AI inside processes that already work, we build limits and review around the agents your team already uses, or we fix what was built too fast and now has to carry real load.

How we can help you

• Adopt

  AI in your process

  For companies that need AI in the process and don't know where to start.Learn more
• Engineer

  AI in your code

  For people who write code and don't use AI yet, or use it without a method.Learn more
• Rescue

  Production ready

  For anyone with paying customers on something they're afraid to touch.Learn more

Why this isn't our opinion

• Almost half the code AI writes arrives with a known vulnerability.

  45% of samples introduce an OWASP Top 10 flaw, and the security pass rate has stalled at 56% even as the models improve on coding benchmarks. Bigger models do not write safer code.

  Veracode, GenAI Code Security Report, 2025 and 2026

• Your team got faster. Stability did not keep up.

  Every 25% rise in AI adoption was associated with roughly a 7.2% drop in delivery stability. A year later throughput had improved and stability was still falling.

  DORA / Google Cloud, 2024 and 2025

• Most AI projects fail, and it is not the model's fault.

  95% of generative AI pilots produced no measurable impact on P&L. 42% of companies abandoned most of their AI initiatives in 2025, against 17% in 2024.

  MIT NANDA, Aug 2025 · S&P Global, 2025

What we usually get called about

• Our team already uses AI, but everyone uses it differently and nobody knows what's allowed.

  AI your team can rely on Adopt

• Someone approves or classifies every case by hand, and the criteria live in that person's head.

  AI inside your processes Adopt

• The data I need is spread across four systems that don't talk to each other.

  Connecting the systems Adopt

• We bought an AI tool and half the team has never opened it.

  Training that sticks Adopt

• The team ships faster with AI and review has become the bottleneck.

  Agentic coding with guardrails Engineer

• Generated code gets merged unreviewed because nobody wants to be the difficult one.

  Agentic coding with guardrails Engineer

• Deploys take a full day and nobody can explain why.

  Pipelines and observability Engineer

• We built it in Lovable, it works, and now nobody wants to touch it.

  Production readiness Rescue

• Every small change breaks something that used to work.

  Production readiness Rescue

• We're about to start charging customers and I can't tell you whether their data is safe.

  An audit, then a path Rescue

• Our MVP already has customers paying, and it breaks every time we touch it.

  From MVP to product Rescue

How we work

• We start with your process, not the request

  A supplier who only builds what they are asked for is worth little more than an AI.

  Before building anything, we walk the process: what it costs today, who works on it, where it breaks, and what would happen if it simply stopped. Some of those conversations end in software. Others end in a process change, or in a tool you already pay for that covers it. We tell you which one yours is before you commit.

  The person having that conversation has spent more than ten years building and maintaining production systems and running engineering teams, and holds an MBA alongside the computer science. So the first questions are about margin, headcount and risk. Architecture and technology come after, and they arrive as trade-offs you can weigh rather than decisions handed down.

• We run this on our own work first

  We use coding agents in our own development, every day. Not as a position — as a working habit that stays under review.

  Which model is currently best for which job changes every few months, and we keep track, because the answer for writing a migration is not the answer for reviewing a diff or reading an unfamiliar codebase. What doesn't change is the flow the tooling runs inside: work split into pieces small enough that a human review is a real review, clear boundaries, tests that have to pass, a pipeline that refuses what breaks. That flow is the reason generated code is safe to ship, and it's the difference between using AI and being used by it.

  This matters because it is also what we sell. The tooling is the part that will be different next year; the flow is the part that transfers. If we recommend something to your team, it is because we have already had to live with it.

• You can fire us without losing anything

  Code, infrastructure, data and documentation in your name from day one. The person you talk to is the person who does the work. Running it, changing it or switching it off does not depend on us — and the diagnostic is written so somebody else can act on it, if you would rather.

"Why don't I just build it myself with AI?"

AI made the first version cheap, and you should use that. The hundredth change is where the real cost lives, and keeping it cheap takes a different set of practices — which can be learned, and which is almost always skipped because no tool asks for it.

Learn more

We work in what you already have

We don't bring a stack; we join yours. Below is what we know how to run without asking you to replace anything.

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.

Get in touch