Synthace was built around a simple observation: the bottleneck in modern biology is no longer the ability to run experiments, but the ability to design experiments that produce data rich enough to be useful. The platform gives scientists a browser-based environment for multivariate experiment design, replacing one-factor-at-a-time workflows with structured DOE approaches that vary many parameters at once and reveal interactions that would otherwise stay hidden.
Execution is the second half of the story. Synthace connects to laboratory automation, including liquid handlers and dispensers, so an experiment designed in software can be translated into automated instructions and run at the bench without bespoke scripting. Because the experiment definition, execution parameters, and results stay linked, the resulting datasets carry their context with them rather than arriving as disconnected spreadsheets that need weeks of manual cleanup.
The company pairs the software with a services layer: field application scientists, starter projects, and training courses on DOE and automation. That combination is aimed squarely at pharma, biotech, and industrial life-science organizations that want to move toward AI-assisted discovery but first need the underlying experimental data to be high-quality, standardized, and machine-readable.
