How it works
Shorter test loops, then fewer of them
Three levels. Operate organizes your data so each build-test-learn loop is shorter. Simulate replaces some of the testing so you run fewer loops. Services puts our modeling team on the problem, or trains yours.
01Operate
A clean platform for cycler data, metadata, and protocols, and the base everything else builds on.
ROI
Shorter test loops
Priced per cycler channel
- Live sync from every major cycler brand
- Protocol library and scheduling
- Channel wall showing which channels are free, running, or stale
- Python SDK and API for your own ML models
Best with Operate
02Simulate
Physics-based models fit to your own cells answer design questions before anything is built.
ROI
Fewer test loops
Usage-based pricing
- Parameterize physics-based or empirical models
- Simulate protocols and drive cycles
- Optimize designs and charge protocols
- Best on top of Operate, also runs standalone
03Services
The team behind PyBaMM runs the study for you, or trains your engineers to run it themselves.
ROI
No ramp-up time
Scoped per project
- Parameterization and validation studies
- Custom modeling work
- Hands-on PyBaMM training
Frequently asked questions
No. Simulate runs standalone: upload cycler files or start from a published parameter set and go straight to parameterization. It works best on top of Operate, because models fit directly from synced measurements and every new test is there to refine them.
Start where the loop is slowest. If the time goes into finding files, rebuilding protocols, and working out what is running on which channel, start with Operate. If it goes into tests that only exist to answer a design question, start with Simulate. If you need a validated model before your team has the capacity to build one, start with Services.
Not per seat, so everyone who touches the data can have access. Operate is priced per cycler channel connected. Simulate is usage-based, so you pay for the simulations and fits you run. Services are scoped per project. Book a demo for a quote against your channel count and workload.
Yes. Everything in Operate is available through the Python SDK and API as structured, labeled data. Teams use it to train their own models, feed internal dashboards, or drive agents.
The parameterized model, a run-only script that loads it and runs simulations, the validation evidence, and a report with parameter provenance. Refitting against new data and team workflows live in Ionworks Studio.