AI-powered scientific optimization for faster experimentation.
AcquiLAB combines Bayesian optimization, scientific analyzers, and AI-assisted workflows so teams can converge on better experimental conditions with fewer runs.
Problem
Scientific iteration is still slow.
Teams still spend cycles on fragmented tooling, manual analysis, and trial-and-error experiment design. Decision quality drops when insights arrive late.
Solution
One platform from data to next experiment.
AcquiLAB unifies analysis, optimization, and reporting into one workflow so every run improves model quality and moves your project forward with traceable decisions.
Core platform capabilities
Built for rigorous experimental teams.
Bayesian Optimization Engine
Gaussian process models with uncertainty-aware next-step recommendations.
Scientific Analyzers
Process spectroscopy, chromatography, NMR, electrochemistry, and microscopy data in one workflow.
Plot Studio
Create publication-ready figures with structured templates and clean exports.
AI Workflow Assistant
Use grounded AI to interpret results and accelerate your next experiment decision.
Core Engine
The Experiment Designer
Acquilab uses Gaussian Process models to learn the relationship between your experimental parameters and outcomes. After each run, it updates its belief and recommends the experiment most likely to reach your goal — balancing exploration and exploitation automatically.
Define your optimization target — maximize yield, minimize particle size, hit a specific absorbance. Acquilab handles the mathematics.
- Gaussian Process surrogate model with uncertainty bands
- Acquisition functions: Expected Improvement, UCB, Thompson Sampling
- Multi-parameter optimization (up to 8 variables)
- Constraint-aware: respects your experimental bounds
- Convergence visualization: watch the model improve run by run
- Works with any measurable outcome from any analytical technique
Gaussian Process Surface
Suggested Next Experiment
pH
7.3
Temp
62 °C
Conc.
4.8 mM
Expected yield: 87.3 ± 4.2%
Run #7 of estimated 12 to convergence
How it works
From goal to optimum in five steps
Define, run, iterate, converge. No installation. No configuration.
Define Your Goal
Set your optimization target and parameter space. Example: "Maximize fluorescence yield. Parameters: pH (5–9), temperature (20–80°C), concentration (0.1–10 mM)"
Run Experiment #1
Upload your first data file. Acquilab analyzes it and extracts the outcome value automatically.
Get Your Next Experiment
The Gaussian Process model recommends the parameter combination most likely to improve your outcome. Includes uncertainty estimate.
Iterate to Convergence
Repeat. The model improves with each run. Acquilab shows convergence progress and flags when you have likely found the optimum.
Export Campaign Report
Full optimization campaign: all runs, the surrogate model surface, convergence plot, and final recommended conditions. Publication-ready.
Run History
Convergence
Estimated 5 more runs to convergence
Product screenshots
Preview of key product surfaces.
Launch faster, learn faster, converge sooner.
Bring your scientific workflow into one optimization-ready system and cut iteration cycles with model-driven decisions.