Thomas Bordier
The AI frontier is a product problem.
I do product at Photoroom, the AI visual platform e-commerce businesses use to sell: 300m downloads, last valued at $500m. I own model quality. Generative AI has no ground truth, so I build it: the evals that tell us when a model is better, from a public benchmark to the product-fidelity push behind the 2026 AI bets. I trained as a scientist at École PolytechniqueThe schoolFrance's top engineering school. and the MVAThe degreeFrance's elite AI master's, where Mistral's founders trained., then taught neural networks to read cardiograms at Cardiologs until Philips bought it. Cardiograms, ships, cartoons, product photos: I have seen where AI creates real value and where it quietly fails. It is easy to demo, hard to make useful. My job is the second part.
Track record
The AI visual platform for e-commerce: 300m downloads, last valued at $500m. Built the evaluation stack for generative models, including a benchmark ranked by 9,000 public votes. In 2026, made product fidelity a company bet: turned “it’s not realistic” into criteria people could agree on, ranked failures by severity with trained annotators, and got the ML team to a fix that shipped.
AI-powered kids’ entertainment. Streamlined how a multilingual YouTube portfolio is run, through user research and rapid prototyping.
Vessel data turned into operational decisions. Doubled ARR and launched a wind-installation tracker that signed Tier 1 operators.
Four months testing ideas from zero at the talent investor. Learned to kill the weak ones fast.
Neural networks reading cardiograms, shipped to clinical production: two patents, enterprise deals in a regulated sector. Bought by Philips.
École Polytechnique · MVA (Mathematics, Vision & Learning), ENS Paris-Saclay
Writing
Notes on making AI useful: short, practical, and written from inside the building.
01An open arena on Hugging Face put four background removers head to head, blind, and let the public vote. 9,000 votes and an Elo ranking later, Photoroom placed first. The method is the point. Generative AI has no ground truth; votes at scale are the closest substitute.
Advisory
I advise a small number of teams on three things, usually with a founder or product lead shipping their first AI surface.
AI product strategy
Which capabilities deserve a bet, and which are demos in disguise.
Evals & AI quality
Whether the feature actually works, measured properly rather than theatrically.
0 → 1 discovery
From fuzzy idea to shipped product, quickly, without shipping junk.