I work with technical AI companies on GTM, market expansion, and partnerships. My path started in engineering and moved into commercialization: understanding the product, reading the buyer, and helping it work in a new market.
When buyer language is unstable, competitors are blurry, and the category is still changing names, I write down the real problem first, then choose the entry point.
A partnership is not “let’s meet.” I look for why the other side should meet now, what can move after the meeting, and how distribution, trust, or capital can connect.
I use AI workflows to speed up research, writing, and review. The tool is not the outcome; the outcome is seeing the important signal faster and missing fewer follow-ups.
Building the first North American commercialization path: buyers, events, partnerships, materials, and revenue signal.
Turning scattered market signals into a ranked opportunity list, so judgment does not stay trapped in chat logs.
Turning engineering records into contribution evidence that can be questioned, reviewed, and discussed.
My base is technical, but I do not position myself as a pure engineer. Electrical and computer engineering, plus early work at a YC robotics-adjacent intelligent manufacturing company, trained me to understand real operating environments, product constraints, and U.S.-China execution context.
The commercial work came later: GTM, market expansion, and partnerships. That work taught me to read the market and the customer more carefully: who has budget, who is only curious, and which situations are worth pursuing.
So I am not the person for “send a few more emails.” I am better suited to questions like: who should the company meet, how should it explain itself, why now, and how do we turn that judgment into meetings, materials, and shipped outcomes?