CES demos include targetless calibration and an exclusive preview of Deepen AI’s Vision-Language-Action framework
Deepen AI, a leading full-stack data lifecycle platform for autonomous vehicles (AV) and robotics, announced it will showcase new platform advancements at CES 2026 designed to help autonomous vehicle and robotics teams improve sensor fusion reliability, reduce calibration friction, and scale deployment with greater confidence in safety-critical environments.
As autonomy expands into public and industrial settings, developers and operators are being held to higher standards for repeatability, validation, and auditability. Deepen AI’s platform is built to address these real-world deployment challenges across the full data lifecycle, from sensor fusion and annotation to calibration and validation, so teams can operationalize autonomous systems beyond pilots.
“Autonomy doesn’t scale on demos, it scales by addressing as many real-world scenarios as possible,” said Mohammad Musa, Founder and CEO of Deepen AI. “At CES, we’re showing the practical, hands-on capabilities that teams need to deploy safety-critical systems in the real world, from targetless calibration to the next evolution of autonomous workflows using Vision Language Action models.”
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At LVCC, West Hall, Booth 3161, Deepen AI will provide live demonstrations and briefings featuring:
- Targetless Calibration (Live Booth Demo): Visitors will be able to physically adjust sensor positions and see how the Deepen system can automatically calibrate multiple sensor modalities to each other, a major workflow improvement designed to reduce downtime, complexity, and calibration error in real-world AV operations.
- Vision-Language-Action (VLA) Framework (Exclusive Preview): A preview of Deepen AI’s upcoming VLA framework designed to connect multi sensor data with higher-level understanding and action planning for autonomous robotic systems.
- Safety Pool™ integration with World Foundation Models(Expanded): A growing scenario library designed to support testing and safety assurance efforts across AV development programs now supports data generated using leading World Foundation Models
Industry Momentum Signals a Growing Need for Deployment-Grade Data Infrastructure
Deepen AI’s growth reflects a broader shift in autonomy. As programs move from controlled pilots to scaled, regulated deployments across mobility, logistics, manufacturing, and mining, the bottleneck is increasingly the data lifecycle, annotation, calibration, validation, and safety assurance. Teams also need provable, auditable evidence that systems perform reliably in the real world.
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