01
Al model support
Trained on 4,000+ cycling tests and >10M material configurations, the platform learns which parameters to optimize for ECM and P2D models directly from your data.
Parametrisation
From weeks of manual curve-fitting to automated physical parameterization
Building accurate ECM and P2D models is critical for advanced battery design — but manual parameterization remains a major bottleneck. Teams spend weeks curve-fitting parameters across temperatures, SOCs, and C-rates by hand.
- 4,000+Real-life cycling tests
- >10MSynthetic material configurations
- < 2%Error for extracted KPIs
- >90%Parameterization time reduction
Your best engineers are losing weeks on manual curve-fitting routines
Testing teams currently spend weeks analyzing complex charge/discharge data across fluctuating temperatures and operational profiles, introducing human error and severely delaying time-to-market.
This is not an engineering failure. It is a process failure.
vetta
One engine. All physics-based models.
vetta integrates every raw charge/discharge test, cycling dataset, and synthetic material configuration into one automated physical parameterization engine. Engineering teams can instantly calibrate ECM and P2D models, simulate complex operational profiles, and completely eliminate weeks of manual curve-fitting routines.
Three steps from cell data
to physical battery intelligence
02
optimization
Minimal adaptation tailored to your cell chemistries and test protocols — building unique IP and deep battery intelligence on high-performance cloud infrastructure.
03
simulation
Simulate any test conditions using pre-built measurement templates or an integrated LLM that generates operating profiles automatically.
use cases
What your team
can do with it
01
rapid cell material screening & optimization
Extract parameters from raw test data to evaluate how physical modifications — electrode thickness, porosity, active material composition — impact cell performance across operating conditions.
02
cross-context cell models
Automatically optimize ECM models across temperature, C-rate, SOC, and SOH matrices. Generates production-ready assets for BMS firmware or thermal validation during fast charge profiles.
03
advanced aging and degradation analysis
Isolate specific degradation mechanisms early in validation — pinpointing exactly how and why a cell fails under thermal stress or extreme SOC conditions.
04
cloud computing for parallelizable optimization
Scale and accelerate internal calibration pipelines via high-velocity cloud infrastructure, deployed through secure European providers like StackIT.
how we deploy
Not a pilot. A permanent
operating relationship.
01
integrate into your data pipeline
We test the automated parameterization engine directly with a sample of your laboratory data, establishing flexible configuration arrays for experimental fitting with either ECM or P2D models.
Payback in months, not years
One unified physical parameterization platform trained on extensive material configurations, deployed
securely inside your own infrastructure.
80% OpEx savings
90% efficiency boost
minutes to results
unified battery intelligence