AcquiLAB was founded to close the gap between collecting experimental data and deciding what to run next. Researchers upload raw instrument files (spectroscopy, electrochemistry, materials, imaging, chromatography and mass spectrometry), AcquiLAB analyses them with validated scientific code, and Bayesian optimization recommends the next experiment. Physics and chemistry simulations can run inside the same loop, so conditions can be screened in silico before anyone goes to the bench.

The goal is not to hide science behind an AI interface. It is to make each decision easier to reproduce: what data was accepted, which model was used, why a recommendation was made and what happened in the next experiment.