AI is moving fast inside real products. That is exciting. It is also risky. Most teams do not fail because they ignore risk. They fail because they do not see it early. Or they see it, but they do not have a clear way to write it down, rank it, and fix it before launch. […]
AI Risk Assessments: A Practical Template for Teams Read More »
Most AI teams do not fail because the model is “bad.” They fail because something goes wrong in the real world, and nobody can clearly explain what happened, who knew, what they did, and when they did it. That is what incident reporting is for. If you are building AI or robotics, you are shipping
Incident Reporting for AI: Triggers, Timelines, Templates Read More »
Most founders I meet are not trying to “move fast and break things.” They are trying to ship something real—into hospitals, factories, banks, schools, or government systems—without getting blocked by legal, security, or trust reviews. That is where model cards and system cards earn their keep. Think of them as the plain-language “truth document” for
Model Cards and System Cards for Real-World Compliance Read More »
Bias testing used to be something teams talked about “later.” After launch. After growth. After the first big customer. That is not how it works anymore. If you build AI that touches hiring, lending, insurance, health, education, safety, public services, or even basic customer support, people will ask the same hard question early: “How do
Bias Testing That Regulators and Buyers Expect Read More »
If you build AI, you feel this pull every day. On one side, you want the model that wins. The one that ships fast, hits the numbers, and makes users say, “Wow.” On the other side, you need answers. Clear reasons. Proof that the system is safe, fair, and steady when the real world gets
Explainability vs Performance: How to Balance Both Read More »
If you build AI, you build with data. And the truth is simple: the quality and safety of your model can never be better than the data you feed it. Data governance is just the set of rules you follow so your training data stays clean, fair, legal, and useful. It is not paperwork. It
Data Governance for AI: Training Data Rules That Matter Read More »
Collaborative robots, or “cobots,” are no longer a small pilot project in a corner of the factory. They are on real floors, in real warehouses, next to real people, moving real product. And that is where the hard part begins. Because the promise sounds simple: let robots and humans work side by side, safely, so
Collaborative Robots: Safe HRI Without Slowing Delivery Read More »
Most small AI teams do not fail because the model is “bad.” They fail because simple decisions get made in a messy way. Someone trains on the wrong data. Someone ships a feature without a clear test. A customer asks, “How do you know it’s safe?” and the team has no calm answer. AI governance
AI Governance for Small Teams: Roles and Routines Read More »
Side letters show up in cross-border deals when one side wants something “extra” that is not in the main contract. Sometimes that “extra” is harmless, like a small process note. But in global deals—where laws, cultures, time zones, languages, and regulators collide—side letters can quietly become the most dangerous pages you sign. This matters a
Side Letters in Global Deals: When to Avoid Them Read More »
When you raise money across borders, the term sheet stops being a “simple” deal summary and starts becoming a translation project. Not just language. Legal systems. Tax rules. Currency movement. How courts work. How fast you can enforce a contract. Even what a “share” really means in that country. And here’s the hard truth: most
Cross-Border Term Sheets: Clauses That Get Messy Read More »
Tran.vc invests $50,000 at the pre-seed stage to empower AI, software, and robotics startups. We help technical founders build strong IP moats, raise seed funding, and grow without relying on endless capital. Focused on speed, innovation, and ownership, we back the builders shaping the future with AI.
