Battery data is too valuable to be trapped in brittle scripts, scattered spreadsheets, and proprietary silos. AmpLabs was built for those who refuse to accept that analyzing cutting-edge electrochemistry must feel like digital archaeology. We don’t just handle battery data—we orchestrate it.
We do not sell software. We provide infrastructure—the invisible scaffolding upon which next-generation battery science is built.
In the past, battery data workflows were an exercise in frustration: brittle scripts, version conflicts, haphazard folder hierarchies, and countless hours wasted converting files before analysis could even begin. AmpLabs was designed to abolish that paradigm—not merely improve it.
Cycler data should be a launchpad for insight—not a punishment for having collected it. We turn cryptic raw outputs into structured, queryable, shareable datasets—no matter the cycler, no matter the format
Cycler outputs vary wildly in format, quality, and structure. Yet regardless of source, your data deserves consistency and elegance.
AmpLabs embraces this complexity with confidence:
Ingest files from Neware, Arbin, BioLogic, and virtually any other source—even custom rigs and one-off experimental setups.
Our parsing engine doesn't just read your files—it understands them. It cleans, structures, labels, and organizes your data with surgical precision.
AmpLabs captures experimental nuance—temperature, current, capacity, protocol variations—without requiring you to hunt through poorly labeled columns.
Whether you are dealing with terabytes of historical data or the latest live experiment,
AmpLabs imposes order with mathematical precision—delivering harmonized, AI-ready datasets.
You didn’t spend weeks running experiments to get another “meh” matplotlib plot. Every plot is an opportunity to convey discovery with clarity and beauty.
AmpLabs provides:
Voltage vs. capacity, dQ/dV, degradation curves, full-cycle analysis—select your view, and we handle the rest.
Compare hundreds—or thousands—of cells, automatically aligned and filtered. Zero script. Maximum clarity.
Preparing your data for machine learning shouldn’t feel like a second thesis. AmpLabs delivers clean, annotated datasets that play well with notebooks, APIs, and AI pipelines.
AmpLabs is not static software—it is a living, intelligent layer for battery R&D. It evolves with your experiments and your needs.
Organize your team’s data by project, cell type, test format—or any schema you define. AmpLabs adapts to your workflow, not the other way around.
From raw files to processed plots to structured tables, AmpLabs speaks open formats fluently. Sync with GitHub, share with collaborators, or publish to open science repositories.
Jupyter. APIs. Custom ML stacks. AmpLabs plays beautifully with your existing tools and models.
Gone are the days of tortured data preparation. AmpLabs makes your battery data AI-native from the moment of ingestion.
Whether you are a lone researcher exploring fundamental materials or a corporate R&D team validating production cells, AmpLabs scales to match your ambition.
AmpLabs empowers the individual scientist, the enterprise R&D director, and everyone in between—with a single, coherent data architecture.
