AI consulting and architecture
AI consulting from people who put AI into production.
Before you invest, you need to know where AI creates value, which architecture to use, what it costs to operate and what risks you're taking. We answer that with the experience of people who build and run AI systems.
The problem
Studies that no one can implement.
Many consultancies deliver studies that no one can implement, and many implementations start with no study at all. The result is a pilot that never reaches production.
What we deliver
Decision grounded in engineering.
Opportunity map
Processes where AI has impact, prioritized by value, feasibility and risk.
Recommended architecture
Models, data, integrations (MCP, APIs), security and observability.
Operating cost
Estimate of development and ongoing usage (models, infrastructure, monitoring).
Deployment plan
Proof of concept with real data, success criteria and path to production.
How we work
Four steps, from idea to operating system.
Assessment
We assess feasibility, data, risks and architecture.
Proof of concept
We validate the solution with real data.
Production
We build, integrate and put it into operation.
Evolution
We monitor, tune and add capabilities.
Who it's for
For those who need to decide about AI on technical grounds.
✓ Para empresas que
- Leadership and IT that need to decide about AI on technical grounds.
- Companies with a stalled pilot and want to know why.
Frequently asked questions
Common questions about AI consulting
What's the difference between consulting and the Assessment?
The Feasibility Assessment is the entry point, with fixed scope and price, focused on one project. Consulting covers the broader strategy: multiple use cases, architecture and governance.
Do you also implement?
Yes. That's the difference: the same team that recommends is the one that builds and operates.
Can the recommendation be not to use AI?
It can, and sometimes it is. A well-written business rule solves many problems better than a model.
Which models do you work with?
We choose by the problem: models from OpenAI, Anthropic, Google and open models, considering cost, quality and where data can be processed.
Read also
Start with the Assessment.
Before you invest, find out whether the idea is feasible, what it costs and what the path is.
Assess my AI project