As machine-learning workflows take a larger role in drug discovery, the bottleneck has shifted from prediction to physical execution: turning model-generated plans into precise, reliable wet-lab actions. This talk covers how the Formulatrix liquid-handling portfolio—the Mantis, Tempest, F.A.S.T., and FLO i8 PD—provides an AI-friendly execution layer for closed-loop R&D at highly miniaturized volumes.

Together with our industry-leading support team, these systems offer a precise and reliable physical layer that computational pipelines can trust. Each instrument reports detailed dispense metrics and run data, ensuring that model-directed experiments are executed as intended and can be reproduced. This execution fidelity is what ultimately enables machine-learning models to close their experimental loops at speeds and scales that meaningfully accelerate drug discovery.