MOOSE AI

Ask questions. Get answers. From your battery data.

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

Built In Workflows

Pre built analyses for common battery scenarios.

Consistent methodology across your organisation. No script writing required.

Grouped cycle life

Compare distributions across groups with statistical outlier detection.

Formation analysis

Track capacity formation, detect completion, flag anomalies.

HPPC

Pulse power capability, pulse resistance, discharge at different rates.

DCIR

Impedance evolution, temperature effects, degradation detection.

DRT

Distribution of relaxation times from EIS data. One click, automated.

Calendar aging

Capacity loss at rest, impedance growth, activation energy estimation.

Cell qualification

Pass/fail against specs with override capability and audit trail.

Application matching

Find cells for specific applications based on performance requirements.

Enterprise Security

Conversation isolation — per organisation, with RBAC.

Ephemeral containers — deleted after Python execution.

Knowledge base isolation — per-instance, never shared.

Full audit trail — every interaction, role-gated.

See MOOSE AI working with your data.

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

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