Problem

Ohm addresses a persistent bottleneck in physical-product engineering: test data is fragmented across cyclers, external laboratories, spreadsheets, internal databases, and other disconnected systems. Engineers often have to build and maintain custom parsers for each format—typically consuming one to two weeks of engineering time whenever a new format or supplier system appears—before they can even begin analysis. The resulting delays are expensive because subtle design failures can remain hidden until the end of a test lasting six months, tying up scarce equipment and slowing product iteration.

The core use case is accelerating test-program investigation, especially in batteries and other complex hardware. Ohm reports that customers have cut root-cause analysis and investigation time by 90% or more, while anomaly detection can identify degradation or performance problems earlier and reduce average test duration by 20% or more. In one Fortune 10 technology company, a performance driver that previously took months to identify was found in a few days.

Product / Service

Ohm is an enterprise AI platform and collaborative workspace for engineering teams that develop, test, and validate physical products. It automatically connects to test equipment, supplier specifications, manufacturing databases, spreadsheets, documents, and time-series sources; ingests and normalizes the data; checks data quality; and creates a shared foundation for visualization, dashboards, reports, live monitoring, and operational workflows.

On top of that data foundation, Ohm uses a model-agnostic agentic harness that surrounds general-purpose language models with engineering context, tools, constraints, historical knowledge, and physics-informed methods. Its AI agents can perform multi-step analyses, predict outcomes, detect anomalies and process drift, investigate root causes, and produce reports in minutes rather than days. The intended benefit is to compress engineering iteration cycles, increase the productivity and capacity of test equipment, preserve institutional knowledge, and let engineers spend more time on high-value design decisions rather than data preparation and repetitive analysis.

Market

Ohm competes in enterprise vertical AI and engineering-lab software for hardware product development, test analytics, validation, and manufacturing quality. Its initial and most evident beachhead is battery R&D and manufacturing, but the company also targets automotive, consumer electronics, aerospace, wearables, grid-scale batteries, and data-center infrastructure. The evidence does not identify one definitive direct head-to-head rival; the closest alternatives are legacy engineering tools and internally assembled workflows built from spreadsheets, notebooks, databases, and custom data pipelines. Adjacent battery-analytics companies such as Voltaiq and TWAICE overlap with Ohm in battery-data management, centralized analytics, dashboards, and predictive insight, although the available evidence does not establish that they compete in identical workflows.

Ohm is not merely pre-revenue in the available evidence. YC lists it as active and says the platform launched in 2025 and is used by leading teams; Ohm's 2026 launch account describes customers across several hardware industries and cites a Fortune 10 deployment. The company was previously known as Byterat, participated in Y Combinator's Winter 2023 batch, raised a reported $4 million seed round in 2023, and had a BMW pilot plus smaller battery-manufacturer customers. A PitchBook record labels the 2023 financing stage “Generating Revenue,” but public sources do not disclose ARR or a current customer count, so the clearest traction signal is enterprise adoption and quantified workflow improvement rather than disclosed financial scale.