applied experiment

Models can now read papers, write code and propose experiments. What they still can't do reliably is read the result. The evidence that decides whether science is true comes from instruments: a spectrum, a diffraction pattern, a chromatogram, a reaction that didn't work. The answer keys models learn from are scraped from literature, and many of them are wrong.

We run an AI-operated wet lab that produces verified experimental data. We prepare and measure real samples, so the right answer is known before the instrument runs, and turn every measurement into graded tasks. What was actually made? How pure is it? What went wrong, and what should be tried next? Every answer is checked by code and by chemists, never guessed.

We sell this data, and the training and evaluation environments built on it, to AI labs teaching models to do science and to teams building agents that run real instruments. Over time the same lab becomes the place where AI-designed molecules and materials are made, measured and verified.

Email yug@appliedexperiment.com.