What we've learned putting AI, data and cloud into production, written by the people who did it.
ISO/IEC 42001 doesn't judge whether your model is accurate. It checks whether your company knows where it uses AI, who owns each use, and what happens when something goes wrong. What the standard asks for, how it fits Brazilian regulation, and a 90-day plan.
Agentic AI is where software stops suggesting and starts acting on the company's behalf. The five layers that led here, what the market is showing, and how to decide how far an agent can go.
We rebuilt the Cortex IQ site with Lovable and saved weeks of frontend work. What the tool didn't handle (AI crawlers reading an empty page, analytics recording nothing, the wrong language on translated pages) was on us. The full account, including what we fixed later.
Terminal-based coding agents changed what counts as a normal day for a developer. The research shows real gains and also the illusion of gains. What changes, what doesn't, a case from this very site, and how a CTO should roll the tool out.
Cyborg architecture is the rule we use to decide, process by process, what stays with people and what goes to AI. Where the idea comes from, what the research says about it, and how we apply it to our own work.
Brazil has no AI-specific law in force yet, but the LGPD already covers every prompt, every RAG index and every agent that reads email. What the ANPD has already done, what Article 20 really requires, and a 30-day checklist.
A committee that reviews every AI request at a monthly meeting produces one thing: AI use outside the committee. How to tier risk, who sits at the table, which deadlines to set, and what to do in the first 60 days.
When someone asks "why did the model decide this, for this customer, on this date?", the answer has to be on record already. What Brazil's LGPD already lets regulators demand, what to log for every decision, and a test to run this week.
The right question is not "AI or RPA." It is whether each step of the process needs interpretation or just repetition. Criteria, hidden costs, an accounts payable example and what the Klarna case teaches.
An agent's ROI is measured on the cost of the whole process, before and after, including the time people spend reviewing its work. What to measure, when to measure it, and a sample calculation you can take to the board.
An AI agent amplifies whatever process it finds. Five signs, each with a test you can run in a week, to tell whether your company can move past isolated pilots or needs to close some gaps first.
In 2024, Gartner predicted at least 30% of generative AI projects would be abandoned after proof of concept. Later it said at least 50% were. The reasons are rarely the model, and most can be spotted before day one.
Each stage answers a different question, and picking the wrong one costs budget, time and credibility. How to tell PoC, MVP and product apart in an AI project, with a worked example and a checklist for your next scoping meeting.
An AI proof of concept ends well when someone can make a decision with it: scale, adjust or drop. Here is the checklist we use to get there, starting with what has to exist before day one.
With open table formats like Apache Iceberg, lake and warehouse are no longer an either-or choice. The decision now comes down to which answers the business needs every week, who will query the data, and how you'd rather pay for it.
The model works in the notebook and dies before it becomes part of a process. The research points to the same places: a poorly defined problem, production data that doesn't match training data, and nobody owning the model after launch.
In CB Insights' latest analysis, 70% of shut-down startups ran out of capital. But running out of cash is the symptom. The cause behind it, in 43% of cases, is a product without market fit.
An AI PoC is a short, fixed-scope experiment that tests a hypothesis against the company's own data before it becomes a project. Most stall because they start without a success criterion, a baseline or an owner for the decision.
AI governance is the set of rules, roles and controls that defines how a company selects, uses and monitors AI systems. It starts with an inventory, an owner and a minimum policy, not a committee or a tool. 90-day plan.
AI agents in healthcare deliver more and expose less when working on procurement, clinical engineering and regulatory documents, away from the medical record. Patient health data is sensitive under LGPD: specific legal basis, minimization and human review.
In retail, the AI that pays for itself first improves product data and forecasts demand per SKU. Personalization comes later, once there is enough purchase history. A 2,000-listing catalog rewritten with an LLM shows where to start.
A B2B distributor gains from AI at three points, in this order: structured order intake, demand forecasting per customer and product, and delivery routing. Until the order becomes data, there is nothing to forecast and nothing to route.
Predictive maintenance starts with the data a fleet, a shop or a line already generates, not with new sensors. Pick one component that is expensive when it stops, measure the history and test a model in a short cycle.
Matching, dynamic pricing and anti-fraud start from the same data the app already collects: position, time and behavior. Start with one case and a simple rule, measure, and switch to a model when volume justifies it.
A consultancy when the need is urgent, initiative volume is low or uncertain and no team can absorb it. An in-house team when AI is part of the product. Hybrid when you need results now and own capacity later.
CTO as a Service makes sense when the company depends on software and cannot yet justify a full-time CTO. An in-house CTO makes sense when technology is the product and the team needs daily leadership.
Advisor as a Service is ongoing strategic support, in weekly sessions, for founders and small teams to decide on business model, MVP and technology with clear criteria. The advisor guides; decisions and execution stay with the founder.