AI-Driven Experimental
Design Solutions

AI experimental design optimization uses machine learning to plan, explore, and refine experiments intelligently. SDLabs applies active learning and Bayesian optimization to recommend the next best experimental conditions, accelerating discovery while streamlining workflows, reducing manual efforts, trial-and-error, and wasted resources.

Traditional approaches such as One-Factor-at-a-Time (OFAT) or classical Design of Experiments (DoE) can be effective but often require large numbers of experiments, struggle with complex parameter interactions, and typically scale poorly in high-dimensional spaces.

Atinary SDLabs moves beyond these limitations by leveraging proprietary ML algorithms—Falcon for global optimization, Emmental for constraint handling, and SeMOpt for transfer learning. Using Bayesian optimization and advanced explore–exploit strategies, SDLabs intelligently guides experiment design, adapts to new data in real time, and uncovers hidden optima that traditional methods may miss.

Yes. Integration with laboratory robotics platforms (such as Chemspeed, IBM RoboRXN, Unchained Labs), as well as data management systems like LIMS and ELNs, is optional and seamless via API/SDK, enabling end-to-end closed-loop workflows. Manual experimentation is fully supported.

Pharma, biotech, materials science, chemistry, cosmetics, and flavors and fragrances organizations all benefit—especially those with complex, high-throughput R&D environments seeking faster, reproducible innovation.

SDLabs uses robust ML algorithms designed for real-world, noisy data and provides repeatable workflows. All data is protected with enterprise-grade security and compliance protocols.