AI decision support for industrial experimentation
Know what to test next before running another experiment.
PeakyPhi learns from your experiment history, predicts outcomes, recommends the next run, explains uncertainty, and checks whether the answer is robust enough for real process variation.
Every experiment teaches the model. Every recommendation becomes smarter.
Decision-support run
Model confidence 87%
Next experiment
Temp 184°C · pH 6.8 · 42 min
18
experiments learned
4
objectives balanced
82%
less wasted lab time
Differentiator
Robust before optimal.
Finding the highest predicted response isn’t enough. PeakyPhi stress-tests recommended settings using thousands of Latin Hypercube simulations to identify operating windows that remain stable under real process variation.
Don’t just find a peak. Find a process window you can trust.
Robustness analysis
Stable window found
temperature
pH variation
5,000
simulations
91%
stable runs
Wide
operating range
Optimization loop
Turn each experimental result into a smarter next move.
The system keeps a live model of your process space, balancing exploration and exploitation so your team does not waste runs on low-information settings.
01
Run 10–20 planned experiments
Begin with a compact design that maps the useful parts of the process space without exhaustive trial-and-error.
02
Learn from every result
Upload each outcome and the model updates its understanding of how inputs affect yield, cost, quality, and throughput.
03
Choose the next best run
Get a ranked recommendation with tradeoffs, confidence, and the settings most likely to move the process forward.
Dynamic DOE for modern process development.
Classical DOE plans the experiment set upfront. PeakyPhi adapts after every result, using each experiment to update the model and recommend the next most useful run.
Plan less upfront
Learn after every result
Run fewer low-value experiments
What PeakyPhi helps teams decide
More than optimization: an AI decision-support loop for industrial experiments.
PeakyPhi learns from experiments, predicts outcomes, recommends what to test next, explains uncertainty, evaluates robustness, and turns the result into reports your team can use.
AI experiment recommendation
Know the highest-value next run before spending more lab time, material, or line capacity.
Automatic reporting
Turn optimization results, confidence, tradeoffs, and recommended settings into reports stakeholders can review.
Multiple objectives and constraints
Balance yield, purity, cost, time, and hard operating constraints in one decision workflow.
Robustness analysis
Stress-test promising settings and identify operating windows that stay stable under real process variation.
Learns from every result
Successful, failed, and inconclusive runs all update the next recommendation.
No statistics background needed
Domain experts see clear recommendations, confidence, tradeoffs, and constraints.
Designed for
Manufacturing
Materials R&D
Chemical Engineering
Process Development
Six Sigma teams
For industrial R&D
Built for noisy processes, practical constraints, and expensive runs.
Use it across formulation, reaction, coating, purification, machining, additive manufacturing, and other process spaces where every experiment consumes time, material, or line capacity.
Works with limited, messy, real-world experiment data
Handles constraints like safe ranges, unavailable settings, and blocked runs
Explains recommendations clearly enough for process owners to trust
Simple pricing for every stage of process optimization.
Start for free, upgrade when you’re ready to optimize real industrial processes.
Built on proven methods
Credible methods, productized for experiment teams.
The math stays in the product. The recommendations stay practical for engineers, scientists, and process owners.
Gaussian Process Regression
Bayesian Optimization
Multi-objective Optimization
Uncertainty Quantification
Latin Hypercube Sampling
Robustness Analysis
Start after signup
Create an account, subscribe, and know what to test next.
PeakyPhi is built for self-serve teams: add experiment results, define goals, and let the model recommend the next run with uncertainty and robustness checks.