WeRide.ai

Skild AI is developing a foundation model for robotics - a universal controller designed to operate across different robot morphologies including arms, humanoids, and quadrupeds. Founded in 2023 by Carnegie Mellon professors Deepak Pathak and Abhinav Gupta, the company is building systems that learn from observation and adapt to new robot bodies and environments without requiring task-specific training. The technical approach centers on massive simulation combined with deep learning to create what the company describes as physically-grounded intelligence. The architecture aims to enable robots to generalize across tasks and form factors, analogous to how large language models generalize across text domains. The company raised $300 million in Series A funding and has established partnerships with NVIDIA and Amazon. Target applications include manufacturing and logistics, where the ability to deploy a single model across different robot types could reduce the engineering overhead of task-specific programming. Technical domains span computer vision, deep learning, and robotics, with emphasis on simulation-based training methods that can scale across diverse robotic platforms. Skild AI applies foundation model techniques to the control problem in robotics, tackling the complexity of physical interaction, real-time control constraints, and the reality gap between simulation and deployment. The company is research-driven, focused on moving algorithms from academic settings into physical systems that operate in industrial environments.

We are Shield AI, a US based defense technology company founded in 2015 to protect service members and civilians with intelligent systems. We design and deploy autonomous mission systems for aircraft and unmanned platforms, built around our Hivemind autonomy backbone and a family of mission ready software and hardware modules. Our products include X-BAT and V-BAT aircraft, Hivemind Enterprise, ViDAR and other capabilities built to operate in GPS and comms denied environments. We develop EdgeOS, Pilot, Forge and Commander to enable rapid development, testing and deployment of resilient autonomy across domains. Our approach is aircraft agnostic, battle tested, and focused on human directable autonomy that can scale across platforms and theaters of operation. Our team emphasizes servant leadership and a mission first mindset. Since founding we have grown to over a thousand teammates with offices across the United States and internationally, and we continue to invest in building safe reliable autonomy for the modern battlefield. We collaborate with allies to advance AI pilots powering a safer world, aiming to reduce risk to service members while enhancing decision making and effectiveness in complex environments. Autonomy for the world remains our guiding vision and the greatest victory, we believe, requires no war.

At Gather AI, we believe warehouse intelligence shouldn't just be efficient - it should be transformative. Founded by Carnegie Mellon Robotics Institute alumni, we've pioneered Physical AI that uses autonomous drones and existing equipment to capture real-time inventory data, completely digitizing workflows that have historically been manual and error-prone. Our team brings deep learning, autonomy, and computer vision to the supply chain, delivering 99.9% accuracy and ROI in under six months. We're not just building software; we're redefining how warehouses understand their physical reality. We make the complex simple by leveraging autonomous technology to unlock valuable data and propel our understanding of the world around us. Our engineers combine classical Bayesian techniques with neural networks to create systems that are curious about boxes, barcodes, and workflows - not just scanning labels, but truly understanding condition, placement, movement, and context. This people-first, innovation-driven approach means our technology solves real problems: reducing pallet emergencies by 90%, improving accuracy by 70%, and helping teams move goods efficiently without adding complexity.

Sanctuary AI, founded in 2018 and headquartered in Vancouver, Canada, develops industrial-grade humanoid robots designed to address labor challenges in manufacturing, logistics, and automotive sectors. The company focuses on embodied AI systems that model human movement and cognitive processes, enabling robots to learn from and interact with physical environments in human-like ways. Sanctuary AI ranks third globally in humanoid robotics patents, reflecting substantial technical development in the field. The company's flagship product, Phoenix, is positioned as a general-purpose humanoid robot built for industrial deployment. Phoenix features human-like dexterity, tactile feedback systems, and fine manipulation capabilities, enabled by a proprietary AI control system. The robot is designed for collaborative operation alongside human workers in automotive plants, manufacturing facilities, and logistics operations, with particular emphasis on tasks characterized as dull, dirty, or dangerous. Sanctuary AI's technical approach centers on several core domains: robotic dexterity and tactile sensing for fine manipulation tasks, proprietary AI control architectures for autonomous operation, and systems designed to achieve general-purpose utility across multiple industrial environments. The company's engineering efforts focus on creating robots that can perform varied tasks within existing industrial infrastructure, rather than requiring specialized environments or fixed automation sequences. Under CEO James Wells, the team develops solutions intended to scale across multiple industry verticals while maintaining industrial-grade reliability standards.

VOLT AI transforms existing security infrastructure into intelligent, proactive safety systems through AI-powered real-time video intelligence. Founded in 2019 and commercially available since 2022, VOLT AI serves educational institutions, Fortune 500 companies, healthcare facilities, and government entities across the United States. Their platform detects weapons, fights, medical emergencies, unauthorized access, crowd formation, theft, and vandalism with exceptional accuracy, achieving response times as fast as 15 seconds for critical incidents like medical emergencies at Prescott High School. The company's technology integrates seamlessly with existing IP camera infrastructure, eliminating the need for costly hardware replacements while providing 3D facility mapping, continuous subject tracking across multiple cameras, and human-verified alerts through their Virtual Security Operations Center (VSOC). Built on Amazon Web Services with edge computing capabilities, VOLT AI maintains SOC 2 Type 2 certification and has received Developmental Testing and Evaluation Designation under the U.S. Department of Homeland Security's SAFETY Act. Following $12 million in Series A funding, the company has rapidly scaled from 3 to 30 employees, positioning itself as a mission-driven leader in AI-powered physical security.

Radical AI operates autonomous laboratories that combine machine learning, robotics, and closed-loop experimentation to discover inorganic materials for aerospace, clean energy, and semiconductor applications. The company's self-driving laboratory system executes synthesis and characterization experiments without human intervention, feeding results back into AI models that screen billions of material compositions to identify candidates for physical validation. This architecture transforms materials R&D from sequential workflows into parallel feedback loops where prediction, synthesis, and performance measurement operate as a continuous integrated system. The technical stack spans Python and C++ for core systems, ROS/ROS2 for robotic control, computer vision and SLAM for laboratory automation, and reinforcement learning for adaptive experimental design. Simulation environments include MuJoCo and IsaacSim for developing manipulation and navigation behaviors before hardware deployment. Infrastructure uses gRPC/protobuf for inter-service communication, OpenTelemetry and Prometheus for instrumentation, with Grafana for monitoring closed-loop experiment execution at scale. With $55M in Series Seed+ funding, the company addresses multi-decade materials challenges in industries where novel inorganic compounds enable next-generation performance - higher-temperature aerospace alloys, more efficient energy conversion materials, and advanced semiconductor substrates. The approach grounds AI prediction in physical constraints: every screened composition must survive automated synthesis protocols, pass characterization under real operating conditions, and demonstrate reproducibility across experiment cycles before qualifying as a discovery.