MOOSE AI

Battery analysis, in plain language.

Name: Micantis Battery Analytics Platform
Author: Micantis

MOOSE AI is the built in analysis assistant in the Micantis platform. It understands your battery test data, specifications, and workflows. Type a question, get a structured answer with charts, key findings, and specific data points.

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MOOSE AI

The plain language assistant for your battery data.

Type a question, get a structured answer with charts, key findings, and references. MOOSE knows your data, your specs, and the workflows your team runs.

Ask in plain language. Charts and findings come back inline.
Schedule recurring reports delivered to your inbox.
Clean data in domain terms ("remove the DCIR pulses").
Pin and share findings with colleagues, no login required.
Search your reference docs with citations to the page.

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MOOSE AI × Python

Your code, MOOSE's reach. Running in our cloud.

Write, run, and schedule Python scripts directly in Micantis. Scripts execute in secure cloud containers with full data access. MOOSE is built into the editor to write and debug.

MOOSE writes the code when you describe what you want to analyse.
Cloud execution. No local Python or environment setup.
Save and share scripts as templates across your team.
Schedule recurring Python jobs with email alerts on failure.
Ask MOOSE to debug scripts that hit errors.

View on PyPI

Use Cases

MOOSE AI in real workflows.

Automated Data Cleaning

Using AI for fixing bad cycles.

Original cycle data with a bad first cycle that drops to roughly 3,000 mAh

Cleaned cycle data after MOOSE removes the bad first cycle

Real cycler files come with bad first cycles, restarts, and outliers that wreck downstream analysis. Describe the issue and MOOSE produces a cleaned dataset, side by side with the original — raw data is never modified, every cleaning step is logged.

Domain language — "remove the bad first cycle"
Side by side original vs. cleaned for verification
Non destructive — raw files stay untouched
Audit trail for every cleaning operation

Python

Custom dQ/dV peak extraction, on a schedule.

Output written to cell metadata Per-cell dQ/dV peak voltage values written back as a metadata column across IQC-Ampace-JP21 and IQC-SDI-25RM cells

A user wanted specific dQ/dV outputs for their cells. They configured a Python script with their selected cells, a smoothing factor, and a target voltage to extract the values they needed. The script runs on a cadence and writes the values of merit back to cell metadata — ready to filter, plot, or feed into specs.

Their inputs — selected cells, smoothing factor, target voltage
Their script — written by MOOSE, refined to match the analysis
Scheduled — runs at whatever cadence they pick
Outputs to cell metadata — values plug into specs, plots, and reports

See MOOSE AI working with your data.

30 minute walkthrough. Ask any question about your battery test data.

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