The best thing we can do for Physical AI is measure it properly
We met at Google, and we both worked on Search there. Evaluation there is a whole engineering discipline, and we took it for granted for years, until we found out that it is not a universally solved problem. Physical AI does not have one yet, so we are building it.
Both founders are engineers
Sergey Arkhangelskiy
Co-founder and CEO
Ten years at Google, including search ranking. Co-founded WANNA, an augmented-reality try-on company with Gucci and Louis Vuitton among its clients, and sold it to Farfetch in 2022.
We train no models and we sell no robots
Nothing of ours is on the leaderboard, so no result of ours is a result about us. A private eval runs the same way: the same rigs, the same tasks and the same scoring as the public board, on your checkpoint, and the numbers go to you alone.
The rigs are in the EU (Cyprus), with an operator who resets the scene after every attempt. Every run is recorded, and the scoring is fixed before it runs: the method is in the PhAIL paper, the harness is on GitHub. On the leaderboard those recordings are public. On your eval they are yours.
Our pre-seed round is led by 33East, with participation from RTP, Davidovs Venture Collective, Orion VC and multiple angels.
Nebius is a founding partner of the leaderboard.
Bring us the checkpoint you cannot score
Leave an address and a line about what you are training, or take half an hour now.