The problem: projects that can't learn from each other
Each project uses its own raw materials, so results rarely combine. Without a shared structure, a model trained on one project can't learn from the others.
Each coatings project used different binders and additives, so their results can't simply be stacked. A model trained on one project has little to say about the others.
What this demo shows
- Structure raw materials by category and property.
- Train one model across three coatings projects.
- Read what drives drying time in Effect Sizes.
Why it matters
Stop losing the learning your team already paid for. Describing raw materials by category and property lets one model learn across projects.
Effect sizes from that model show what drives a property, such as drying time, using far more data than any single project holds.
See it with your own data
This video is part of DOE and Predictive Tools. To see how it would work with your own formulations and records, request a demo. Our team replies within 30 minutes during business hours.
Read the transcript
Projects too different to compare? Uncountable still finds the patterns. Uncountable structures your R&D data. Raw materials carry a category, like binder, and properties, like solids. Your enamel, primer, and floor finish use different raw materials. Analyze with AI trains one model on all three. It learns from what every formula shares, category totals and calculated properties, like formula solids.
Effect sizes then show what drives drying time across all three projects. Formula solids comes out on top. One pattern across three projects. Different projects, one set of patterns. Uncountable.
