StartupDrivingRedwood City, CA, United StatesFounded 2016
Develops AI software for ADAS and automated driving, including generative driving-scene models VidGen-1 and WorldGen-1.
Helm.ai is an AI software company co-founded in 2016 by Vladislav Voroninski and headquartered in Redwood City, California. It develops perception and driving software for ADAS and higher-level automated driving using an unsupervised training method it calls Deep Teaching. In 2024 it announced the generative models VidGen-1 and WorldGen-1 for driving simulation and validation.
Sources: helm.ai (16 Aug 2023) · silicon.co.uk (20 Jun 2024) · helm.ai (30 Jul 2024)
Private company · $55M disclosed in USD across 1 of 1 listed round
| Date | Round | Amount | Lead | Source |
|---|---|---|---|---|
| 16 Aug 2023 | Series C | $55M | Freeman Group | helm.ai, 16 Aug 2023 |
#82 of 104 model companies · score 9.1 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 0 of 2 listed models have open weights.
The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.
| Input | Weight | How it is measured |
|---|---|---|
| Published benchmarks | 35% | For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating). |
| Open weights | 20% | Share of the company's listed models whose weights the public can download. |
| Adoption | 20% | GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero. |
| Released models | 15% | Number of publicly announced, named models, capped at 5. |
| Disclosed funding | 10% | The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion. |
Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.
Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.
Closed weightsWorld model
Multi-sensor generative foundation model that jointly generates camera, lidar and perception data, extrapolates between sensor modalities and predicts ego-vehicle and agent behaviour.
No benchmark results on a shared public benchmark have been published for this model.
Closed weightsWorld model
Generative video model that produces driving-scene video sequences, unprompted or from an input image or video, for autonomous driving development and validation.
No benchmark results on a shared public benchmark have been published for this model.
