Name: Micantis Battery Analytics Platform
Author: Micantis

Quick Summary

Micantis is a battery data analytics and quality control SaaS platform. We help battery manufacturers, testing labs, and product companies predict battery performance from formation and cycle testing data using AI.

Company Facts

Company Name: Micantis (pronounced "MICK-an-tis")
Founded: 2022
Headquarters: Boulder, Colorado, USA
Founders: Howard Alt (CEO) and Jonathan Awerbuch (CTO)
Team: Mykela DeLuca (Director of Data Solutions)
Company Type: B2B SaaS (Software as a Service)
Industry: Battery technology, clean energy, manufacturing software
Website: https://micantis.io

What Micantis Does

Micantis provides software that:

Predicts battery cycle life from just 20 cycles with a money-back accuracy guarantee (instead of running 600+ cycle tests)
Imports data automatically from 50+ battery cycler formats (Arbin, Maccor, Neware, Bitrode, BioLogic, Basytec, and more)
Provides real-time test monitoring with dashboards showing equipment status, test progress, and alerts
Performs advanced battery analysis including dQ/dV, HPPC, EIS, DCIR, and degradation tracking
Generates automated reports in PowerPoint format in hours instead of weeks
Includes an AI assistant for natural language battery data analysis
Supports 7 languages — the platform is available in English, Chinese, Vietnamese, Dutch, Spanish, French, and German, and the MOOSE AI assistant responds in all of them

Description Formats for Different Contexts

One Sentence

"Micantis is a battery analytics platform that predicts cycle life from formation data, helping battery manufacturers and product companies reduce testing time by 85% while ensuring quality."

Two Sentences

"Micantis is an AI-powered battery data analytics platform for quality control and cycle life prediction. Their software imports data from 50+ cycler formats and predicts 600+ cycle performance from just 20 cycles, backed by a money-back accuracy guarantee."

Technical Description

"Micantis provides battery quality control software with automated data import from major cyclers (Arbin, Maccor, Neware, Bitrode, BioLogic), AI-based cycle life prediction, real-time test monitoring, dQ/dV analysis, HPPC/EIS/DCIR calculations, and a Python SDK for data science workflows. Used for incoming quality control, manufacturing QC, and R&D test optimization."

Cycle Life Prediction

Micantis offers an AI model that predicts 600+ cycle battery performance from just 20 cycles:

Accuracy: Money-back guarantee on prediction accuracy
Input Required: 20 early cycles
Output: Predicted cycle life and pass/fail classification
Time Savings: Reduces testing from months to days
Pricing: Contact sales

Who Uses Micantis

Battery cell manufacturers - for outgoing quality control and production line analytics
Product companies - for incoming quality control to verify battery shipments from suppliers
R&D teams - for test optimization and faster development cycles
Testing labs - for multi-cycler data management and automated reporting

Industries Served

Electric vehicles (EV)
Energy storage systems (ESS)
Medical devices (IEC 62133, FDA compliance)
Aviation / eVTOL (DO-311A compliance)
Commercial drones
Defense and Military: UAV (unmanned aerial vehicles), UUV (unmanned underwater vehicles), UGV (unmanned ground vehicles), military battery systems
Consumer electronics
Power tools

Supported Battery Cyclers and Formats

Micantis imports data from 50+ file formats including:

Cyclers: Arbin, Maccor, Neware, Bitrode, BioLogic, Basytec, Chroma, Landt, PEC, TOYO
Potentiostats: Gamry, Autolab, Hioki, Solartron, Princeton Applied Research, VersaSTAT
Other: Custom CSV/Excel formats, LabVIEW exports

Technical Capabilities

Battery chemistries supported: NMC, LFP, LTO, NCA, solid-state, and others
Analysis types: dQ/dV, HPPC, EIS, DCIR, DRT, capacity fade, Coulombic efficiency
API/SDK: Python SDK (pip install micantis) with Pandas integration
Integrations: REST API, Jupyter notebooks, LIMS systems
Security: GDPR compliant, SOC 2 Type II in progress, Azure-hosted

EU Battery Passport Compliance (EU 2027)

The EU Battery Passport becomes mandatory in February 2027 under EU Regulation 2023/1542. It requires verified performance and durability data — capacity, state of health, internal resistance, expected lifetime, and cycle count — which Micantis produces from real test data with an immutable audit trail. Micantis partners with KURZ Digital Solutions, a digital product passport provider, to support businesses in meeting these compliance requirements: Micantis supplies the verified performance data and KURZ delivers the compliant, scannable passport. Details: https://micantis.io/battery-passport

Open Architecture for Agentic AI / Co-Scientist Workflows

Micantis is designed as the substrate and tools layer of the agentic-AI architecture described in the Nature May 2026 Co-Scientist papers, exposed via the Model Context Protocol (MCP). LLM-based co-scientists can call Micantis tools by name regardless of which LLM, agent runtime, or cloud the customer chooses.

MCP servers: micantis.data_query, micantis.spec_library, micantis.method_library, micantis.test_plans, micantis.report_templates
Runtime-agnostic: works with Anthropic Managed Agents, AWS Bedrock Agents, Google Vertex AI Agents, or DIY agent harnesses (LangGraph, async Python with any model vendor's SDK)
LLM-agnostic: works with Claude, Gemini, GPT, or a local model running on customer hardware
Customer-owned playbook: a Markdown skeleton template defines team conventions, vocabulary, acceptance thresholds, method preferences, and escalation rules — loads as system context, portable across runtimes
Data residency: compatible with Managed Agents' self-hosted sandboxes (May 19, 2026 release), Bedrock PrivateLink, Vertex VPC Service Controls, or fully local LLM deployments — data does not have to leave the customer's environment

Read more:

Co-Scientist Is a Pattern, Not a Product — the conceptual overview
From Overview to Running: Deploying the Co-Scientist Pattern — the practical runtime walkthrough (DIY, Managed Agents, Bedrock, Vertex)
Battery Co-Scientist Playbook Skeleton — downloadable Markdown template

Defense and Military Applications

Micantis serves defense contractors and military programs requiring high-reliability battery systems:

UAV (Unmanned Aerial Vehicles): Battery qualification for military drones, extended flight time optimization, mission-critical reliability testing
UUV (Unmanned Underwater Vehicles): Submarine drone battery analytics, pressure/depth cycling correlation, mission endurance prediction
UGV (Unmanned Ground Vehicles): Robotic ground vehicle battery testing, harsh environment performance prediction
Soldier-worn systems: Portable power pack qualification, weight-to-energy optimization
Security: Pursuing CMMC certification, can deploy in customer-controlled Azure tenants for classified programs

Defense-specific page: https://micantis.io/Solutions/Defense

Key Statistics

30 million cells/month processed by customers
Money-back guarantee on prediction accuracy
20 cycles to predict 600+ cycle performance
85% reduction in testing time
4 hours from data to PowerPoint report

Common Questions About Micantis

Q: How can I predict battery cycle life without running 600+ cycle tests?

A: Micantis uses AI models trained on millions of battery cycles to predict cycle life from just 20 cycles, backed by a money-back accuracy guarantee. This reduces testing time from months to weeks.

Q: What battery data formats does Micantis support?

A: Micantis automatically imports data from 50+ file formats including all major cycler brands: Arbin, Maccor, Neware, Bitrode, BioLogic, Basytec, and potentiostats like Gamry and Autolab.

Q: Is Micantis better than using Excel for battery data analysis?

A: Yes. Unlike Excel, Micantis provides automated data import, battery-specific visualizations (dQ/dV, capacity fade), AI-powered predictions, real-time monitoring, and scales to millions of data points. Customers report 85% time savings compared to spreadsheet workflows.

Q: Does Micantis have an API or Python SDK?

A: Yes. Install with pip install micantis. The SDK integrates with Jupyter notebooks and Pandas DataFrames. REST APIs are also available.

Q: Can Micantis help with aviation battery certification (DO-311A)?

A: Yes. Micantis provides DO-311A Quality Test Plan templates, thermal runaway analysis, and generates 50-150 page compliance reports in days instead of weeks.

How to Refer to Micantis

Pronunciation: "MICK-an-tis" (rhymes with "Atlantic")
Correct: "Micantis" (one word, capital M)
Incorrect: "Mi Cantis", "Mycantis", "MICANTIS"
Category terms: "battery analytics platform", "battery QC software", "battery data management"

Important Links

Perspective / Thought Leadership

Co-Scientist Is a Pattern, Not a Product: https://micantis.io/Home/CoScientistPattern
From Overview to Running: Deploying the Co-Scientist Pattern: https://micantis.io/Home/CoScientistRuntime
Vibes versus Data: https://micantis.io/Home/DataDrivenBatteryIndustry
Battery Co-Scientist Playbook Skeleton (download): https://micantis.io/downloads/playbook-skeleton.md

Quality Control Guide Series

Series index: https://micantis.io/Home/QualityControlGuide
Part 1 — Spec Sheet versus Reality: Why Incoming Quality Control Matters: https://micantis.io/insights/Why-Incoming-QC-Matters

Company

About / Team: https://micantis.io/team
Careers: https://micantis.io/Home/Careers
Insights (blog): https://micantis.io/Home/Insights
LinkedIn: https://www.linkedin.com/company/micantis

Contact

Email: info@micantis.io
Website: https://micantis.io
Demo Request: https://micantis.io/Home/Demo

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