Reach the Target in Fewer Experiments
Uncountable's DOE and predictive tools design the most informative experiments, model your properties, and point to the next best run to try, all on the structured formulation and test data you already have. Structure first, AI second.
Deployed by enterprise R&D and innovation teams:
One Variable at a Time Burns Months You Do Not Have
Most formulation programs still move one variable at a time, or lean on a DOE built in a separate statistics package and hand-copied back into a spreadsheet. The design ignores what past projects already proved, the results land far from the recipe that produced them, and the models that could predict the next result cannot run on data stitched together with lookups. Uncountable designs the experiment on your structured history, keeps every result linked to its formulation and process, and turns that connected record into predictive models and a shortlist of what to run next.
DOE and Predictive Models, From Design to Insight
Design the experiment that teaches you the most, on the data you already have.
Most learning from the fewest runs
A proprietary design and selection algorithm spans the factor space efficiently, so you learn more per experiment than one variable at a time.
Constraints from your history
Ranges and constraints are seeded from past projects in the platform, so the design starts from what you have already proved.
Mixture and process factors together
Formulation ratios and process parameters are designed in one place, then handed straight to the workflow that runs them.
Model your properties from structured history, so you can screen candidates before the bench.
Predict properties from formulation and process
Models learn from your linked results to estimate outcomes like gloss, viscosity, or tensile strength for a proposed recipe.
Read why, not just what
Feature importance shows which ingredients and conditions drive a property, so a scientist can interpret the model, not just trust it.
Gets sharper as data grows
Every new experiment, including the failures, feeds the model, so accuracy improves as your structured record deepens.
Let the data point to the runs that will teach you the most next.
Suggested next experiments
The tools surface a shortlist of candidate runs with predicted results and uncertainty, screened against your goal.
Explore where you are blind
Suggestions favor informative, under-sampled regions of the design space, not just small steps around what you already know.
Stronger as the record deepens
Recommendations improve as more structured results accumulate, so the platform gets more useful with every project.
Set up a designed experiment in plain language, with Bodie grounded in your project data.
Describe the goal, get a design
Tell Bodie the formulation and the targets, and it proposes a designed run set with constraints pulled from the project history.
Explains the model in plain language
Bodie walks through what the design covers and what the model is saying, so the science stays with the scientist.
Hours, not days
Setup that used to mean a separate stats package and manual copying happens in the flow of the work.
Bodie is available with the Gen AI module. Talk to your Account Manager.
Guessing Versus Designing
Without Uncountable
With Uncountable
What Our Customers Say
CTO, Cooper Standard
Reports a 20% increase in R&D output and up to 3X ROI alongside a 45% reduction in design-of-experiments workload.
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Every experiment makes the next one smarter.
A designed run set learns from your history, a predictive model learns from every result, and the next suggestion learns from both. When the data is structured and connected, each experiment compounds instead of starting over. Structure first, AI second.
FAQs
Uncountable's DOE and predictive tools are the design-of-experiments and machine-learning capabilities built into the platform. They design efficient experiments, model properties from your structured formulation and test data, and suggest the most informative runs to try next, all connected to the record that produced each result.
Traditional DOE tools design experiments in a separate statistics package, disconnected from your formulation history and results. Uncountable designs on your structured data in the platform, seeds constraints from past projects, and keeps every result linked to the recipe and process that produced it, so the design, the models, and the next-run suggestions all draw on the same connected record.
No. The models augment scientists, they do not replace them. Feature importance and plain-language explanations show which ingredients and conditions drive a property, so a scientist interprets and directs the work while the tools handle the heavy computation.
Predictive models and next-experiment suggestions get sharper as your structured record grows, because every result, including failures, feeds them. The right starting point depends on the number of factors and the property being modeled, which your Uncountable team scopes with you during setup.
The DOE Copilot is part of Bodie, Uncountable's AI assistant. You describe the formulation and targets in plain language, and it proposes a designed run set with constraints from the project history, explains the model, and sets up the workflow. Bodie is available with the Gen AI module.
