Ohm offers an AI‑powered analytics platform that ingests raw electrochemical test data, simulation outputs, and manufacturing logs to predict capacity, degradation, safety metrics, and identify failure modes. The system provides automated data cleaning, model‑based experiment recommendations, and interactive dashboards accessible through REST APIs and Python SDKs, enabling battery manufacturers to accelerate R&D and production optimization.
Funding
$4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Battery development teams struggle to extract actionable insights from large, heterogeneous electrochemical datasets, leading to prolonged design cycles and delayed time‑to‑market for new cell chemistries and manufacturing processes.
Solution
Ohm delivers AI “co‑scientists” that are pre‑trained on electrochemistry, battery physics, and data engineering, embedding directly into a team’s existing design and manufacturing workflows. The platform ingests raw test data, simulation outputs, and production logs, then applies domain‑specific machine‑learning models to generate predictive performance metrics, identify failure modes, and recommend experiment variations. Results are presented through interactive dashboards and can be accessed via APIs, enabling engineers to iterate faster without building custom analytics pipelines. By automating routine data interpretation, Ohm reduces the time required for hypothesis testing and accelerates the overall innovation cadence for battery programs.
Target Audience
Primary customers are battery manufacturers—both traditional and next‑generation chemistry producers—and large technology firms that integrate batteries into their products and require advanced data‑driven R&D capabilities.
Features
- Pre‑trained electrochemical models that predict capacity, degradation, and safety metrics from raw test data
- Automated data cleaning and feature extraction pipelines tailored to battery research and manufacturing datasets
- Real‑time recommendation engine that suggests next‑step experiments or process adjustments based on model uncertainty
- RESTful and Python SDK integrations for seamless embedding into existing CAD, PLM, and MES tools
- Interactive visualization suite with trend analysis, heat‑maps, and anomaly detection dashboards
- Secure, role‑based access to cloud‑hosted analytics with end‑to‑end encryption and audit logging
- Scalable compute infrastructure that supports both small‑scale lab data and large‑scale production streams
- Customizable model fine‑tuning to incorporate proprietary chemistries or process parameters