Battery Data Platforms: What's Changed and What to Look For

If you are evaluating battery data platforms, you have almost certainly encountered legacy tools from companies like Voltaiq, Micantis, Batalyse, and Astrolabe, along with many others that defined the first wave of battery data infrastructure.

For more than a decade, these tools helped battery teams aggregate raw test data, unify it, and run basic visualizations. Before them, the internal "stack" was a loose collection of CSVs, Excel files, a thumb drive, a lot of Python, and even more time spent on manual data wrangling. Given the tooling available at the time, these platforms represented a major step forward. During the EV boom and peak government funding cycle, they were good enough, and a meaningful improvement over the status quo.

That market environment no longer exists. EV demand forecasts have been revised down, federal incentives are tightening, and investment dollars are far more selective. The operating question has shifted from "how much runway do we have?" to "how much revenue are we generating, and at what margin?"

Battery businesses now run with sharper edges. Teams need capabilities that can answer questions like:

Can I predict when this test will fail?
Can I cut my qualification timeline by two months?
Can I leverage all the data I already have to drive performance improvements?
Can I build a state-of-the-art information engine that powers technical breakthroughs for my team?

For teams that need to do more with less, the question is straightforward: stay with infrastructure designed around yesterday's bottlenecks, or adopt a platform built to accelerate outcomes today and into the future?

What the First Platforms Got Right

Early platforms automated data ingestion and gave battery teams point-and-click tools to visualize test data. They pulled data engineering off the scientists' plates: import files from the major cycler brands, aggregate across sources, and view cycling performance in one place. For teams whose primary pain was "get data from cyclers into one place and plot it," legacy tools solved it. In the pre-AI era, when building an internal system was expensive and slow, these vendors offered a faster, cheaper path.

That problem is no longer the bottleneck. Modern data engineering has made internal aggregation systems cheaper to build and increasingly an IT responsibility. A reliable aggregation layer with API export may still earn its place, but its role has shrunk to commodity infrastructure.

The core value has moved downstream to the signals in the data and what teams do with them.

Where Legacy Platforms Stop

The gap becomes clear when you ask what happens after the data lands in the platform.

With legacy tools, engineers learned to export aggregated data and figure out the rest themselves. Complex analyses started with an API call, moved through Jupyter notebooks and manual metadata work, and sometimes required custom software development just to reach a result.

Reporting workflows stayed fragmented. Engineers exported visualizations into slide decks for leadership reviews. Operational metrics like channel utilization lived in disconnected spreadsheets or sat behind costly add-on modules, creating a paywall between customers and a view of their own data.

Native predictive modeling was largely absent. Anomaly detection was not embedded in ingestion pipelines. One engineer's validated workflow rarely carried over to the next hire.

The Shift to Intelligent Systems

During the first generation, these tools represented the state of the art. The underlying economics and tooling landscape have since shifted. Foundation models now make domain-specific analytical systems genuinely intelligent. Tools like Cursor and Claude Code have reset the cost and speed of software creation. And the economics of hardware development have turned intelligent tooling into a requirement, not a nice-to-have.

Feature Comparison

The distinction becomes clearer when the platforms are compared side by side.

Capability	Legacy Platform	Ohm
Data foundation
Ingest from all cycler brands	✓	✓
External test lab + PDF/CSV ingestion	Limited	✓
Auto-stitching of multi-file sequences	Add-on	✓
Metadata sync from schemas	Limited	✓
Automated quality checks + format alerts	—	✓
Custom metric/KPI definitions	—	✓
Visibility & monitoring
Control room / live dashboards	—	✓
Cross-build comparison views	—	✓
Channel utilization visibility	—	✓
Operational alerts	Limited	✓
Downloadable HTML/PDF reports	✓	✓
AI-powered analysis
Natural language analysis delegation	—	✓
Full code transparency	—	✓
Reusable workflow templates	—	✓
Literature search (internal + external)	—	✓
PyBaMM model execution	—	✓
AI co-scientist experience	—	✓
Predictive intelligence
ML Playground (train + deploy models)	—	✓
Deploy models into live pipelines	—	✓
Anomaly detection + root-cause workflows	—	✓
Early test termination recommendations	—	✓
Int+ workflow capture	—	✓
Knowledge inheritance for new engineers	—	✓
Searchable data lineage	—	✓
Platform
Configurable foundation model / BYO LLM	—	✓
Unlimited named users	✓	✓
Dedicated forward-deployed engineer	Add-on	✓

Legacy platforms cover the basics of data ingestion. Ohm matches and extends that foundation with operational visibility — but that is just the starting point. Beyond it, Ohm adds predictive intelligence, agentic AI workflows, and institutional memory capabilities that define what a modern battery data platform should deliver.

How Ohm Works

Ohm serves battery teams differently than legacy tools. At the foundation layer, Ohm delivers the core data engineering capabilities expected of an enterprise platform: automated ingestion from all major cycler brands, external test house integration, live synchronization, and access to structured processed data.

That is where the similarity ends.

Predictive Intelligence Built Into the Platform

Ohm deploys predictive models directly into data pipelines, so predictions run automatically as data arrives.

Anomaly detection surfaces subtle degradation signals and performance deviations that fall outside expected noise boundaries. Teams can also train and deploy custom machine learning models through Ohm's ML Playground without writing code.

The practical outcome is measurable operational leverage. Customers can terminate predictable or low-value tests earlier, freeing cycler capacity for higher-priority programs.

For a battery test program costing $1 million annually, reducing average test duration by 20% creates roughly $200,000 in recoverable testing capacity, based on average cycler-hour cost allocation. That budget and infrastructure can be redirected toward accelerating development programs rather than waiting on unnecessary cycle time.

All of this is delivered within Ohm's fully secure and scalable enterprise environment.

AI Systems Embedded in the Engineering Workflow

Ohm introduces agentic AI workflows directly into the battery engineering environment.

Engineers can delegate complex analyses using natural language, while Ohm manages the underlying workflow end to end: data retrieval, code generation, execution, literature search, visualization, and report generation. All generated code remains fully transparent and inspectable.

Validated workflows can be saved as reusable templates that automatically execute on future datasets, allowing organizations to standardize analytical best practices across teams.

Ohm also includes native PyBaMM integration, enabling physics-based battery models to run directly alongside experimental datasets inside the same environment. In many organizations, comparable functionality would otherwise require purchasing and integrating a separate modeling platform.

Institutional Memory That Compounds Over Time

Every workflow, analysis, assumption, and engineering rationale is captured within a searchable knowledge system.

When engineers leave, their work does not leave with them. When new engineers join, they inherit validated analytical workflows immediately instead of rebuilding methods from scratch. Over time, this creates a compounding organizational knowledge base that becomes increasingly valuable as more experiments, models, and decisions accumulate.

Migration Without Disruption: Designed for Parallel Deployment

Whether a customer is replacing an incumbent platform or deploying a battery data platform for the first time, onboarding to Ohm is designed to be fast, structured, and low-risk.

Ohm can operate in parallel with existing systems. One Fortune 100 customer completed a full migration from a legacy platform to Ohm in 2025 using this approach. The migration succeeded because Ohm's data foundation achieved functional parity with the outgoing system while also providing a controlled parallel validation period. Customer teams verified data fidelity, workflows, and operational continuity before executing a full cutover.

The result was a managed, predictable transition, not a disruptive replacement.

For organizations currently under contract with legacy providers, the deployment model is straightforward: deploy Ohm in parallel during the remaining contract term, validate in production conditions, and complete the migration once the legacy agreement expires. By the time the cutover occurs, Ohm is already fully operational and trusted by the engineering team.

What Matters Now

The platform decision is no longer about aggregating battery data. That problem has largely been solved.

The real question is what happens after the data arrives.

If your primary requirement is consolidating cycler outputs into a centralized system of record, legacy platforms and modern aggregation layers can accomplish that. But if your competitive pressure is reducing qualification timelines, increasing cycler throughput, preserving engineering knowledge, or accelerating decisions under tighter budgets, the requirement changes fundamentally.

At that point, the platform cannot just store data. It has to participate in the engineering process itself.

The battery industry is entering a period where capital efficiency and execution speed matter more than ever. The teams that win will not necessarily be the ones running the most tests; they will be the teams that learn faster from every test they run.

That requires infrastructure built for intelligence, not just aggregation. Systems that can predict outcomes before tests are complete. Systems that operationalize expert workflows across an entire organization. Systems that compound institutional knowledge instead of losing it every time a team changes.

This is the transition now underway across engineering software broadly: from systems of record to systems of reasoning.

That is the transition Ohm was built to power.

Ohm is an enterprise AI platform purpose-built for hardware teams, including battery engineering teams. To see how Ohm compares to your current data infrastructure, .