Introducing Ohm: The AI Platform for Accelerating Hardware Development and Testing

Cursor and Claude Code reimagined how software products are developed. Our mission at Ohm is to address the next frontier: artificial intelligence that reimagines how complex hardware is developed, tested and launched.

We started Ohm because we saw a widening gap. AI was transforming how software teams build: generating code, catching bugs, shipping faster than ever. Meanwhile, the engineers designing batteries, wearables, electric vehicles, and aerospace systems were still working the way they had for decades: wrangling data across disconnected tools, running every test to completion regardless of whether the outcome was already predictable, and manually running analyses to identify root cause issues.

The race to develop a greener, cleaner planet is one of the most consequential engineering challenges of our time. The teams working on this deserved better tools, which is why Ohm exists today.

What Ohm does

Ohm is an enterprise AI platform purpose-built for engineering teams that develop, test, and validate physical products. We work with teams across battery development, automotive, consumer electronics, aerospace, and data center infrastructure.

Customers are seeing concrete outcomes: faster product iteration cycles, reduced costs, and faster time to signal. Root cause analyses and investigations are being accelerated by 90% or more. One of our customers — a Fortune 10 technology company that previously relied on legacy tooling — identified a key performance driver in a few days that originally took them months.

The metric we care most about is the one that's hardest to measure: the quality of decisions an engineering team can make when they have the right tools. When your engineers spend their time on electrochemistry instead of data wrangling, on interpretation instead of data assembly, the compounding effects are enormous.

How it works

Ohm operates at three levels of transformation, and each one builds on the last.

The first is an AI-ready data foundation. Most engineering teams have data scattered across cycler brands, external test labs, spreadsheets, and internal databases. Ohm automatically ingests from all of these sources, normalizes the data, catches quality issues before they corrupt downstream analysis, and gives your team a single source of truth. This sounds simple. It isn't. Building a custom parser for a single new data format typically takes one to two weeks of engineering time, and that investment has to be repeated every time a supplier changes their software. Ohm handles all of that automatically.

The second level is predictive intelligence. This is where Ohm starts to fundamentally change how test programs operate. We deploy predictive models directly into data pipelines so they run automatically as new data arrives, not through periodic manual analysis in Jupyter notebooks. Anomaly detection catches subtle degradation signals, slope changes, and performance deviations that fall outside noise bounds, surfacing failing designs early rather than at the end of a six-month test. Teams can train and deploy custom ML models directly in the platform with no coding required for standard model types. The practical impact: customers can cut average test duration by 20% or more, freeing equipment capacity for higher-value experiments.

The third level is our agentic AI co-scientist. This is what I'm most excited about. Ohm's co-scientists complete complex, multi-step engineering analyses in minutes. You delegate tasks in natural language — "compare degradation trends across these three formulations and surface any anomalies" — and Ohm figures out what data to pull, writes and runs the analysis code, searches both internal knowledge and external published research for context, and generates a report end-to-end. All generated code is fully transparent. Engineers can inspect algorithms, hyperparameters, and statistical methods directly. Once a workflow is validated, it can be saved as a reusable template that runs automatically on every new dataset.

The compounding advantage

There's a fourth dimension that cuts across everything I just described, and it may be the most important one.

Every insight and workflow compounds over time within Ohm's knowledge system. Through everyday use, your team builds a knowledge base that becomes a unique digital asset. New engineers onboard faster because they inherit the analytical practices of the team's best scientists. Departing engineers leave their workflows behind. Teams continuously uplevel as the knowledge base grows with every test.

Over time, this creates a lasting advantage that competitors cannot replicate. Too many engineering teams lose months of progress when a senior engineer leaves and their Jupyter notebooks, institutional context, and analytical intuition leave with them. Ohm makes that problem structurally impossible.

Why now

Two things changed that made Ohm possible.

First, foundation models reached a capability threshold where they can reason meaningfully about domain-specific engineering problems — not just generate generic text, but execute multi-step analyses that require expertise in electrochemistry, materials science, and test methodology.

Second, the economics of hardware development are under more pressure than ever. Battery companies are racing to scale manufacturing, automotive OEMs are compressing vehicle development timelines and AI is forcing a huge investment in data center development and build-out. All of them face the same constraint: testing and validation is the bottleneck, and the traditional approach of "run every test, analyze manually, hope nothing falls through the cracks" cannot scale.

We built Ohm to be the platform these teams deserve.

If your engineering team is developing and testing physical products and you recognize the problems I've described, I'd love to show you what Ohm can do.