Increase the safety and efficiency of industrial fleets in ports and airports with self-driving software and modular hardware designed for existing vehicles.
Helm.ai
Scale autonomous driving and ADAS systems from Level 2+ to Level 4 with AI-first perception, foundation models, and synthetic simulation tools.
About Helm.ai
Helm.ai develops AI-first software and simulation environments designed for advanced driver assistance systems (ADAS) up to Level 4 autonomous vehicle deployments. Its product ecosystem divides into real-time on-vehicle software and offline foundation models for training and validation. On the vehicle side, Helm.ai Vision offers surround-view perception without relying on lidar, while Helm.ai Driver acts as an end-to-end autonomy stack designed to scale into high-level autonomous driving. For offline development, Helm.ai provides foundational world models and generative AI systems, including GenSim-3, VidGen-3, and WorldGen-1. These models generate high-fidelity, synthetic driving video, re-stylize real-world capture, and synthesize multi-sensor feeds like camera, lidar, and semantic segmentation data. The entire framework is underpinned by Deep Teaching™, Helm.ai’s proprietary unsupervised learning technology that leverages applied mathematics to scale foundation model training without manual data labeling. Helm.ai is built specifically for automotive original equipment manufacturers (OEMs), Tier 1 automotive suppliers, and robotics companies looking to deploy production-bound autonomous driving programs. Automakers such as Honda and Volkswagen utilize Helm.ai's platforms to shorten R&D lifecycles, cut validation costs, and prepare next-generation consumer vehicles and commercial mobility applications for mass production. Unlike traditional autonomy platforms that rely on expensive lidar suites or slow, labor-intensive manual data annotation, Helm.ai emphasizes a vision-first architecture powered by unsupervised learning. The platform automatically adapts to regional driving conditions and newly encountered geographies while efficiently generating simulation data for rare and critical corner cases.
Helm.ai pros & cons
Pros
- Enables Level 2+ surround and bird's-eye view perception using camera inputs without requiring lidar.
- Proprietary Deep Teaching™ eliminates the need for expensive, time-consuming manual data labeling.
- Offers comprehensive generative models (GenSim-3, VidGen-3, WorldGen-1) for scalable corner case validation.
Cons
- No publicly listed pricing or self-serve access, requiring enterprise contact and custom demonstrations.
- Tailored strictly for automotive OEMs, Tier 1 suppliers, and robotics firms rather than individual developers.
Helm.ai use cases
- Automotive OEMs can integrate Helm.ai Driver to deploy vision-based Level 2+ highway and urban pilot features in mass-production vehicles.
- Perception engineering teams can use GenSim-3 to re-stylize real-world drive data into edge-case scenarios to scale AV validation.
- Autonomous vehicle software developers can utilize WorldGen-1 to simulate synchronized camera, lidar, and semantic segmentation streams for offline model training.
Helm.ai features
- automated regional adaptation and corner case resolution
- large-scale auto-labeling and simulation pipelines
- proprietary deep teaching™ unsupervised learning technology
- worldgen-1 multi-sensor simulation across camera, lidar, and semantic data
- vidgen-3 generative world model for synthetic driving video generation
- gensim-3 foundation model for transforming and re-stylizing driving scenarios
- helm.ai driver for level 2+ to level 4 production autonomy
- helm.ai vision for camera-first surround and bird's-eye view (bev) perception
Helm.ai FAQs
What is Deep Teaching™?
Deep Teaching™ is Helm.ai's proprietary unsupervised learning technology. It combines large-scale real-world data, deep learning, and applied mathematics to train foundation models without relying on manual labeling.
Does Helm.ai Vision require lidar for autonomous driving?
No, Helm.ai Vision is designed as a vision-first system. It delivers full-scene surround and bird's-eye view (BEV) perception from multi-camera input without requiring lidar for Level 2+ autonomy.
What offline simulation tools does Helm.ai offer?
Helm.ai offers foundation models including GenSim-3 for scenario re-stylization, VidGen-3 for high-fidelity synthetic video generation, and WorldGen-1 for multi-sensor simulation covering camera, lidar, and semantic segmentation data.
Which autonomy levels does Helm.ai support?
Helm.ai provides a unified approach that supports high-end ADAS and Level 2+ driver assistance today, scaling up to Level 3 perception and Level 4 fully autonomous driving architectures.
How can automotive companies request a demo?
Automotive OEMs, Tier 1 suppliers, and robotics developers can book a product demo by submitting the contact form on Helm.ai's website.
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