Most startups build AI fast. That is normal. You ship, you learn, you fix, you ship again. But there is a quiet problem hiding under that speed: risk. Not “big company” risk. Startup risk. The kind that shows up as a surprise customer security review, a blocked enterprise deal, a model that fails in one […]
NIST AI RMF for Startups: A Simple Playbook Read More »
Export controls sound like a topic for big defense firms and government teams. But if you build AI or robots, it can become your problem much earlier than you think. Because the same model that helps a factory run smoother can also help a drone fly better. The same robot arm that packs boxes can
Export Controls and Dual-Use Risk for AI & Robotics Read More »
AI is moving into hospitals fast. It reads scans, flags risks, guides doctors, and watches patients from afar. But in the U.S., you cannot ship AI for health care just because it “works.” If your software helps diagnose, treat, prevent, or even guides a clinical decision, the Food and Drug Administration (FDA) may treat it
U.S. Food and Drug Administration Rules for AI in Medical Devices (SaMD) Read More »
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 »
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.
