01Capabilities
What the house knows how to do, and where each one has already been practised: the data floor a decision stands on, the team and the method that deliver with AI inside a harness, and the products and the operation that come out of it.
02The 3 capabilities
7 case studies3 positions
I design where data lives and who is allowed to do what with it: the Data Lake, Warehouse and Lakehouse layers, the pipelines between them, and the compliance and security framework that governs the whole thing. That framework answers to LGPD in Brazil and to GDPR in Europe.
The practice and the evidence: Data Architecture and Governance
2 case studies2 positions
I get a whole company working with AI, not only the engineers: mentoring, training, workshops and hackathons on the company's real challenges on one side, and on the other the operation written down, meaning each process, each role and who decides, so the repetitive work of a role can go to an agent while the decision stays with whoever approves. It is the part that decides whether an AI investment becomes a habit or stays a pilot.
13 case studies14 positions
I write the harness on the DDC Framework that a company builds software inside with AI, and the agentic core its products run on. I take a product from discovery through to delivery in increments, and I do the same for the operation that sells it: what earns a place on the roadmap, what it costs to run, how it is priced and how it reaches customers, then the system itself, with agents and language models that do real work. The deliverable is the thing running, not a deck about it.
Sold within
The practice and the evidence: Vibe Coding and AI-Based Products