Built by Battery Engineers, For Battery Engineers

At Micantis, we're passionate about data and passionate about our customers succeeding. We believe batteries and clean energy are the future, and our goal is to ensure all of our customers succeed in accelerating that future.

Every day, battery teams around the world face the same challenge: drowning in data but starving for insights. We've been there. We've lived that frustration. That's why we built Micantis not just as another software tool, but as the platform we wished we had when we were in your shoes.

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Why Teams Choose Micantis

Speed to Insights

Go from raw data to actionable insights in minutes, not weeks. Our platform eliminates the bottleneck between testing and decision-making.

Domain Expertise

Built by battery scientists who understand your workflows, terminology, and challenges. Not a generic analytics tool, purpose-built for batteries.

Proven Results

Our customers report 85% reduction in analysis time, 99.7% prediction accuracy, and millions of cells processed monthly.

Why Not Just Use Google Sheets?

Most teams start with spreadsheets. Here's why that doesn't scale for battery data:

Google Sheets / Excel

Manual data entry and copying
No automated file format parsing
Limited to simple charts and graphs
No real-time collaboration on analysis
No meaningful audit history or change tracking
No advanced battery-specific calculations
Breaks down with large datasets
No predictive modeling capabilities

Micantis Platform

Automatic data import from 50+ cyclers
Native support for all battery file formats
Interactive, publication-ready visualizations
Real-time team collaboration and sharing
Complete audit trail and version history
Battery-specific analysis (dQ/dV, EIS, HPPC)
Handles millions of data points instantly
Automatic report generation

The bottom line: Sheets are great for quick calculations. For serious battery analysis, you need purpose-built tools.

Beyond Google Sheets: Building It Yourself? Here's What You'll Need

Many teams start with Google Sheets or Excel, then consider building their own analytics platform. Here's the reality of what that entails:

Technical Requirements

Team Building

2-4 Software Engineers: Backend, frontend, DevOps specialists
1-2 Battery Experts: To determine how everything should be calculated (dQ/dV, capacity analysis, etc.)
1 Product Manager: To coordinate development priorities
1 UX Designer: For intuitive interfaces engineers actually want to use

Backend Data Infrastructure

File format parsers: Arbin, Maccor, Neware, Bitrode, and 50+ other cycler formats
Data pipeline architecture: Real-time ingestion, processing, storage
Database design: Optimized for time-series battery data at scale
API development: RESTful services for data access and analysis

Analytics & Visualization

Statistical analysis engine: dQ/dV, EIS, HPPC, cycle life algorithms
Specification management: Custom limits, pass/fail criteria, version control
Automatic pass/fail generation: Real-time evaluation against specifications
Interactive dashboards: Real-time charts, filtering, drill-down capabilities
Report generation: Automated PDF/PowerPoint creation with custom templates

Performance & Scalability

Query optimization: Sub-second response times for large datasets
Caching strategies: Redis, memory management, smart pre-computation
Load balancing: Handle multiple users and concurrent analysis jobs
Cloud infrastructure: Auto-scaling, backup, disaster recovery

Timeline Reality Check

Minimum viable product: 12-18 months

Feature-complete platform: 24-36 months

Enterprise-ready with all integrations: 3-5 years

Estimated cost: $2-5M+ in development costs

Still Want to Build It Yourself?

We get it. Sometimes you need custom solutions that fit your exact workflow.

If you still want to build it yourself, we offer comprehensive APIs to pipe in data and handle all the backend complexity. This way, your team can focus on analyzing and visualizing data exactly how they want, while we take care of:

Data ingestion: All major cycler formats automatically processed
Backend processing: Feature extraction, statistical analysis, ML predictions
Scalable infrastructure: Cloud-native architecture that grows with you
Real-time APIs: RESTful endpoints for seamless integration
Documentation & support: Comprehensive guides and dedicated support

Micantis API Example

# Upload and analyze battery data
import micantis_api as mc

# Connect to your Micantis instance  
client = mc.Client(api_key='your_key')

# Upload cycler data
dataset = client.upload_file('formation_data.csv')

# Run analysis
results = dataset.analyze([
    'capacity_fade',
    'cycle_life_prediction', 
    'dqdv_analysis'
])

# Get insights
print(f"Predicted cycles: {results.cycle_life}")
print(f"Peak analysis: {results.dqdv.peaks}")

Our Mission: Your Success

Batteries and clean energy are the future. Every breakthrough in battery technology brings us closer to a carbon-neutral world.

But innovation is bottlenecked by outdated analysis tools and manual processes. We're removing that bottleneck, one insight at a time. When you succeed, we succeed. When you innovate faster, the world moves closer to sustainable energy.

That's why we're not just building software. We're building the foundation for the next generation of battery innovation.

Ready to Accelerate Your Battery Innovation?

Join the teams already saving months of analysis time with Micantis.

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Benefits