Why I Work at the Intersection of AI and Global Trade

The question I get most often is: what connects Zentria Flow, Trazeroad, and FixerCV? Why these specific domains?

The answer is always the same: information asymmetry. Every company I've built targets a situation where one party — an importer, a job seeker, a logistics operator — is making an expensive decision with significantly less information than they need. AI is what makes closing that gap possible at the scale and speed required to be genuinely useful.

Why Global Trade Specifically

I started in freight — not as an analyst studying the industry from the outside, but as an operator moving goods across borders. The longer I worked in cross-border logistics, the more obvious one thing became: the cost structure of international trade is almost entirely opaque to the people paying it.

An importer agrees to a product price. They know they'll pay freight. They roughly know they'll pay duties. But the true landed cost — once tariffs, customs fees, port handling, broker fees, currency conversion, and compliance costs are included — is almost never known until after goods have cleared customs and the final invoices arrive.

The gap between what importers think they'll pay and what they actually pay is consistently 20–50% of the product price. That gap costs businesses money on every shipment, distorts profitability calculations, and makes accurate sourcing comparisons essentially impossible.

This is a structural problem in a market that moves trillions of dollars annually. It doesn't exist because businesses are careless — it exists because the information is fragmented across dozens of data sources that have never been properly integrated. Tariff rates live in government databases. Freight benchmarks come from brokers. Port fees appear on invoices. Nobody assembles the complete picture in real time.

Why AI Is the Right Tool for This Problem

Building a system that integrates real-time tariff data from 180+ countries, current freight rate benchmarks, port fee schedules, and customs clearance cost patterns — and delivers an accurate landed cost in seconds — isn't a spreadsheet problem. It requires AI to classify products automatically, match them to tariff schedules, integrate data from multiple sources, and surface a reliable estimate fast enough to actually influence a buying decision before the order is placed.

The same pattern applies to FixerCV: the gap between how a candidate presents their experience and how an ATS scores it is an information asymmetry problem. AI is what makes it possible to bridge that gap at scale — analyzing each specific job description, comparing it against each specific resume, and producing guidance that's actually personalized rather than generic.

The Bigger Pattern

Where AI creates the most genuine business value is in information-dense domains where the cost of getting the right information manually is too high for most participants to bear. Global trade is one of the clearest examples: the information exists, but assembling it, keeping it current, and applying it to a specific product on a specific route has historically been too expensive for most importers to do systematically.

AI closes that access gap. That's what Zentria Flow is building: not a better spreadsheet, but a fundamentally different way to access and apply trade cost intelligence. The goal is making the true landed cost of an import as easy to know as the supplier's quoted price — before you place the order, not after it clears customs.

What Comes Next

The intersection of AI and global trade is early. The infrastructure for real-time, comprehensive, accurate trade cost intelligence is still being built. Most importers are still working with manual estimates, outdated tariff data, and reactive cost discovery.

The companies building the right infrastructure now will sit at the center of trillions of dollars of global commerce. That's a worthwhile place to build — and it's where I intend to stay.