The Micantis Platform

Battery testing software, built by battery engineers.

Name: Micantis Battery Analytics Platform
Author: Micantis

Automated cycler ingest, AI assisted analysis, and quality control workflows. From R&D to mass production, on day one.

Quality Control Cell Manufacturing Contract Testing Labs Battery R&D

Micantis platform flow: data sources flow into the Micantis Data Platform for ingest and processing, then into the Micantis Web App for analysis, visualization, and team outputs

Your data

Cyclers, files, metadata

Test stations and shared drives across your lab.

Offering 1 · Backend

Micantis Data Platform

Auto ingestion, format standardization, and Python access. Learn more →

Offering 2 · Full app

Micantis Web App

The web app: dashboards, QC, reporting, and MOOSE on top of the same data layer.

Features

Everything battery teams need.

Metadata Traceability

Every cell, every step, every supplier.

Track raw materials through finished pack with custom metadata fields. Tie every test back to the lot, channel, operator, and protocol that produced it.

Materials to pack traceability across the full process
Custom metadata fields per cell, batch, or program
Lot, channel, operator, protocol on every test
Multiple input paths: directly in Micantis, Excel upload, or LIMS integration
Searchable across millions of records in seconds

Metadata traceability table across cells

Crystallite size versus cycle 2 Coulombic efficiency, showing the relationship between metadata and cell performance

Graphing

The graphs you'd build by hand, automatic.

Voltage profiles, capacity fade, multi cell overlays, and statistical comparisons across batches. Build any view you need, then save it as a template.

Voltage, capacity, energy profiles out of the box
Multi cell overlays with statistical bands
Custom plots from any field, any axis
Reusable templates shared across the team

Cycle life graphing across multiple cells

Electrochemical Analysis

Battery science, built in.

Expert driven feature extraction from formation, cycle life, calendar aging, and qualification. Every parameter you need, computed automatically as data lands.

Core metrics: capacity, energy, Coulombic efficiency
Advanced techniques: dQ/dV, EIS, HPPC, DCIR, DRT
Pre built workflows for formation, cycle life, calendar aging
Automated processing across large datasets

Hybrid Pulse Power Characterization analysis tool

Equivalent circuit model with Nyquist EIS analysis

Quality Control

Pass / fail, automated.

Real time SPC monitoring, automated spec compliance, and full traceability from raw materials through customer shipment. Catch process drift before it ships.

Automated pass/fail evaluation against your specs
Real time SPC charts and monitoring
Full traceability from materials to pack
Risk assessment and burn down analysis

Reporting

Reports that write themselves.

Auto generate PowerPoint reports, custom dashboards, and shareable insights. Branded with your templates, delivered on your cadence.

Auto generated PowerPoint presentations
Custom dashboards and visualizations
Batch reporting across multiple tests
Brand customization with your templates

Lab Monitoring

Live across the lab.

Real time dashboards, equipment status tracking, smart alerts on critical events, and trend analysis as data lands. Know what's happening without checking every channel by hand.

Real time dashboards and visualizations
Equipment status and performance tracking
Smart alerts on critical events
Trend analysis across active programs

Lab monitoring view of tester channels with utilization and status

MOOSE AI

Ask questions in plain language.

MOOSE turns plain English into structured analysis. Charts, summaries, spec checks, scheduled reports, all from a question.

Structured answers with charts and findings
Scheduled reports delivered by email
Build specs and graphs from descriptions
Clean data by describing what to remove

Explore MOOSE

Python

Run Python where your data lives.

Cloud code editor, scheduled scripts, MOOSE in the editor, and a pre installed scientific Python stack. No local environment to maintain.

Cloud execution in secure containers
Save and share scripts as templates
Schedule recurring analysis runs
MOOSE in the editor writes code from descriptions
Pre installed: pandas, numpy, scipy, micantis

Cloud Notebook · scheduled daily

from micantis import MicantisAPI
import pandas as pd

api = MicantisAPI()

# Pull last 24 hours of formation data
df = api.get_data_table(
    search="Formation",
    since="24h",
)

# Your custom QC logic
flagged = df[df["first_ce"] < 0.92]
api.push_results(flagged, tag="low-ce-cells")

Why Teams Choose Us

Battery teams pick Micantis because it was built by people who lived their problems.

Speed to insights

Raw data to actionable insight in minutes, not weeks. Eliminates the bottleneck between testing and decision.

Domain expertise

Built by battery scientists who understand your workflows, terminology, and the failure modes you're chasing.

Proven results

85% reduction in analysis time. Customers ship 100M+ cells per year on the platform.

See what your battery testing looks like with Micantis.

Battery teams across power tools, aviation, defense, and energy storage trust Micantis for analysis and quality control.

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