In short: your recipes and process data go in, the engine learns your chemistry, and you get predicted properties and candidate recipes back — before anything is mixed. Here's the path from data to decision.
Upload recipes & process logs as CSV/XLSX — with your own column names. Polymers, fillers, cure system, mixing & vulcanization settings.
The platform translates your columns into its canonical fields automatically, so you don't have to reformat anything.
Models train on your chemistry and combine with a shared master model — so predictions are useful even before you have thousands of rows.
Get the predicted cure curve, hardness, compression set and more — each with a confidence band and the key drivers behind it.
You have a compound. The engine tells you how it will behave — before you weigh a single gram.
You have a spec to hit. The engine proposes candidate recipes that should land inside it.
Your raw recipes never leave your workspace and are never pooled into a shared model. Any cross‑lab learning happens through federated learning — opt‑in and revocable at any time. Read more on security →
Request a walkthrough and we'll run the platform against the elastomers you actually work with.
Request a demo →