The demo works. The system does not.
It answered well in a meeting. There is no way to measure how often it is right.
Applied AI, engineered for production.
We integrate language models and machine learning into real business software, with the evaluation, controls and operations that production demands.
- AI integration and retrieval
- Evaluation and guardrails
- Human oversight by design
When an AI prototype cannot reach production.
Four reasons promising demos stall.
No grounding in your data.
Answers are fluent but not tied to your documents, your systems or your permissions.
Risk that nobody owns.
Questions about privacy, LGPD, cost and who approves an automated action are still open.
Value that is hard to judge.
The team is experimenting with AI without a defined task or a way to tell whether it helps.
How we engineer AI features.
The model is one component. The system around it decides whether it can be trusted.
Start with the task, not the model.
Define what the system should do, what a wrong answer costs and where a person must stay in the loop.
Ground it in your data.
Retrieval, tools and integrations that connect the model to the right sources and respect access rules.
Evaluate before and after launch.
A test set built from your real cases, run on every change, so quality is measured instead of assumed.
Operate with controls.
Logging, monitoring, cost limits, fallbacks and approval steps for actions that are hard to undo.
What you receive.
An AI capability your team can run, judge and stop.
An integrated AI capability
Designed, built and connected to your systems within the agreed scope.
Evaluation set and harness
Cases drawn from your domain, including the ones that must fail safely, and a way to rerun them.
Guardrails and permission model
Rules for what the system may read, write or trigger, and when a person approves.
Operating notes
Monitoring, cost, data-handling and escalation guidance for the team that will run it.
Often paired with.
Complex problems rarely sit in one discipline. These practices connect to this one.
Software engineering
AI features live inside applications. They need the same architecture discipline.
Quality engineering
Evaluation sets and release gates apply to AI as much as to any other code.
Assessments
A structured look at readiness, risk and data before committing to a build.
Questions we expect.
Do you train our own models?
Rarely at the start. Most business problems are better served by existing models combined with retrieval, tools and good evaluation. We recommend training only when that approach is not enough and your data justifies it.
Can our data stay private?
Data handling is designed with you from the start: what is sent to which provider, what is stored, and how that fits LGPD and your own policies.
Which model providers do you use?
We select models by task, cost, latency and data requirements, and design the system so the model can be replaced.
Can AI agents act on our systems?
Yes, within defined permissions. Actions that are costly or hard to reverse keep a human approval step until evaluation shows it is safe to relax.
Take AI from prototype to production.
Tell us what you want it to do, and what must never happen.