A starting material for one customer, an advanced intermediate for another, a building block for a third - often in the same multipurpose plant, within the same year.
That is the reality for many fine and specialty chemical teams. The chemistry sits close to pharma - cross-couplings, amidations, hydrogenations - but development windows are short, portfolios are broad, and each project arrives with its own constraints and a fixed delivery date.
The challenge is reaching a robust, transferable process quickly, project after project.
Bayesian optimization reaches good conditions in fewer experiments than one-factor-at-a-time screening or DoE, while mapping where the process is robust and where it is sensitive. Multi-objective campaigns weigh yield against impurity, catalyst loading, and solvent choice together, making trade-offs visible while they are still cheap to act on.
Lab-scale conditions are only half of it. A plant runs with different heating profiles, addition rates, and hold times. Kinetics360 builds kinetic models directly from time-course data, ranking candidate mechanisms by fit without overfitting. It then simulates the reaction across temperatures, concentrations, and times not tested in the lab - so conditions reach the plant with an understanding of why they work, not just evidence that they did.
Breadth also becomes an advantage. Every completed project is data on a related transformation, and MemoryBO carries it forward to warm-start the next campaign, alongside the 25,000+ data points from our HTE lab across Suzuki, Buchwald-Hartwig, and amide couplings.
