Author
Rudinei Gehm
Founder, Cortex IQ
Cortex IQ started long before it had a name. In 2009 Rudinei Gehm entered IT through infrastructure and industrial automation. He moved into consulting and development for the financial sector, became a project manager and, from 2017 to 2021, led a Growth Pod with global teams and projects. In 2025 he founded Cortex IQ around a simple idea: AI only matters once it leaves the slide deck and runs inside the operation, with results you can measure.
Articles by Rudinei Gehm
- ISO 42001: when AI stops being an experiment and starts requiring managementISO/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. 7 min read · December 17, 2025
- From predictive AI to agentic AI: what changes when software starts to actAgentic 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. 6 min read · December 17, 2025
- How Lovable sped up our own website, and what was still our jobWe 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. 6 min read · March 29, 2026
- What Claude Code changed about developers' work, and what it hasn'tTerminal-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. 6 min read · March 29, 2026
- The cyborg concept: how Cortex IQ splits the work between people and AICyborg 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. 6 min read · March 29, 2026
- LGPD and generative AI: what actually changes for companies operating in BrazilBrazil 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. 7 min read · July 23, 2026
- How to set up an AI governance committee without slowing innovationA 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. 7 min read · July 23, 2026
- Auditing AI models: what to track before the regulator asksWhen 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. 6 min read · July 23, 2026
- AI agents vs. RPA: when each one makes senseThe 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. 6 min read · July 23, 2026
- How to measure the ROI of an AI agent in its first 90 daysAn 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. 7 min read · July 23, 2026
- 5 signs your company is ready for agentic AIAn 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. 6 min read · July 23, 2026
- Gartner predicted 30% of generative AI PoCs would be abandoned. It was worseIn 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. 6 min read · July 23, 2026
- PoC, MVP or product: which stage your AI initiative is really inEach 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. 6 min read · July 23, 2026
- An AI proof of concept checklist: before, during and at the endAn 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. 6 min read · July 23, 2026
- Data lake, data warehouse or lakehouse: how to decide in 2026With 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. 6 min read · July 23, 2026
- Why so many machine learning models never reach productionThe 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. 6 min read · July 23, 2026
- Startups don't fail for lack of ideas: what CB Insights' data showsIn 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. 6 min read · July 23, 2026
- What an AI PoC is and why most of them never leave the slide deckAn 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. 6 min read · September 29, 2026
- AI governance in the enterprise: pillars, risks and how to implement itAI 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. 6 min read · September 29, 2026
- AI in healthcare: where agents and models help without breaking LGPDAI 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. 6 min read · September 29, 2026
- AI in retail: personalization and demand forecasting without overstockingIn 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. 6 min read · September 29, 2026
- AI for B2B distributors: routes, demand forecasting and orders without spreadsheetsA 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. 6 min read · September 29, 2026
- Predictive maintenance with AI in the automotive sector: where to startPredictive 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. 6 min read · September 29, 2026
- AI in mobility apps: matching, dynamic pricing and fraud detectionMatching, 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. 7 min read · September 29, 2026
- When to hire an AI consultancy and when to build an in-house teamA 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. 6 min read · September 29, 2026
- CTO as a Service or in-house CTO: when each one makes senseCTO 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. 7 min read · September 29, 2026
- Advisor as a Service: how the on-demand technology advisor worksAdvisor 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. 7 min read · September 29, 2026
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