The problem: trial and error toward a spec
Hitting several targets at once by trial and error takes many rounds in the lab. Knowing which ingredients matter, and what to try next, cuts that down.
An SPF 50 mineral sunscreen has to hit SPF, viscosity and pH at the same time, and changing one ingredient moves all three. Without a model, each round of trials adjusts one thing and hopes the others hold.
What this demo shows
- Set the target.
- Find out which ingredients matter most.
- Get AI-suggested formulas with predicted results.
- The lab confirms which one hits every goal.
Why it matters
You can't test every formulation. Make the next experiment count. Learning which ingredients drive each property, from past results, focuses the lab on the changes that matter.
Suggested formulas come with predicted values, and lab results turn green or red against the spec, so the team can see quickly how well the model is performing and which candidate to take forward.
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
Uncountable keeps every experiment as structured data. Turn a target into new formulas worth testing. The target goes into a spec. SPF of at least 50, a viscosity range, and a pH range. Analyze experiments learns from past results and shows which ingredients drive SPF and viscosity. Suggest experiments trains a model on past results and proposes four new formulas, each with predicted SPF, viscosity, and pH.
The lab runs four candidates, and each result turns green or red. One formula meets every goal. Uncountable. Every stage, one platform.
