Helm.ai is an Autonomy Software & Simulation company founded in 2016 and based in Menlo Park, United States. It has raised $102M in total funding, most recently a Series C in 2023.
| Date | Stage | Amount | Valuation | Lead investors |
|---|---|---|---|---|
| Aug 16, 2023 | Series C | $55M | — | Freeman Group |
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A camera-based perception stack for ADAS and autonomous driving trained using Helm.ai's Deep Teaching unsupervised-learning method, which extracts patterns from large unlabeled datasets rather than relying on costly manual annotation. Helm.ai Vision provides production-grade object detection, semantic understanding, and scene perception that automakers can deploy across Level 2 to Level 4 systems. The unsupervised approach is the company's core differentiator, aimed at delivering high-accuracy perception while reducing the data-labeling cost that dominates conventional autonomy development.
A vision-based, real-time path-prediction and driving-policy system that turns perception output into safe vehicle behavior for hands-free and automated driving. Designed to run on automotive-grade compute, Helm.ai Driver targets production ADAS and higher levels of automation for OEM customers. It works alongside Helm.ai Vision and the company's generative-simulation models so automakers can develop, validate, and ship an end-to-end driving stack, and it underpins Helm.ai's joint development with Honda for next-generation consumer vehicles.
A suite of generative-AI foundation models that synthesize realistic driving video, labeled images, and full driving scenes for training and validating autonomous systems. By generating high-fidelity, diverse, and labeled data on demand, these models let developers stress-test perception and driving policies against rare and dangerous scenarios that are hard to capture in the real world. Generative simulation extends Helm.ai's Deep Teaching philosophy into the data-generation layer, supporting scalable development of ADAS and autonomous-driving software.
4 patents on file, but none with both an extractable figure and an abstract on Google Patents yet.
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