Gatik 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.

Lyte AI is building the perception layer of Physical AI, creating end-to-end vision systems that enable robots to see, understand, and operate safely in the physical world. The company unifies sensing, compute, and software to deliver reliable 4D perception in messy, real-world conditions, addressing the perception gap that has plagued robotics for decades. Founded in 2021 by former Apple Face ID engineers, Lyte AI provides reliable sensing and perception across weather, lighting, and terrain conditions with ultra-low latency AI processing of dense 4D data at the edge. The company emerged from stealth with $107 million in funding to power autonomous mobile robots, robotic arms, quadrupeds, robotaxis, and humanoids. Their technology enables real-time edge intelligence, allowing machines to turn raw sensor data into intelligent action safely and efficiently.

Mistral AI is a French artificial intelligence company founded in April 2023 by Arthur Mensch, Guillaume Lample, and Timothée Lacroix - researchers with prior experience at Google DeepMind and Meta who share academic roots at École Polytechnique. The company develops and releases open-weight generative AI models, positioning them as competitive alternatives to proprietary solutions while maintaining a commitment to transparency and accessibility in frontier AI development. The company's core technical approach centers on training large language models in-house and releasing them with open weights, allowing developers, enterprises, and institutions worldwide to deploy these models while maintaining full control over their data and applications. Mistral AI's models are designed to compete with closed, proprietary systems, and the company frames community-backed development as a methodology for addressing censorship and bias in AI systems. This technical philosophy distinguishes their work from the conventional closed-model paradigm dominant among major AI providers. Mistral AI's business model targets a broad user base spanning software developers, enterprises, and institutions globally. The company positions itself as building a credible alternative to what it characterizes as an emerging AI oligopoly, advocating for open access to frontier AI capabilities rather than concentration of advanced models within a small number of organizations. Their released models are intended to enable external parties to build and operate AI applications independently, without dependency on proprietary API services or restricted model access.

Robust AI develops collaborative mobile robotics solutions for warehouse and manufacturing environments, with an emphasis on human-centric design and AI-powered workflow integration. The company was founded by a team that includes robotics pioneer Rodney Brooks and is currently led by CEO Anthony Jules. Their engineering approach prioritizes robots that work alongside human workers in material handling and logistics operations rather than replacing them. The company's flagship product, Carter, is a collaborative mobile robot designed for rapid deployment in dynamic warehouse and manufacturing settings. The system is offered as an all-inclusive, no-CapEx solution that requires zero infrastructure changes to existing facilities. Carter integrates AI-powered workflows to operate in environments where adaptability to changing conditions is essential. The platform has been deployed at DHL, where the company reports 60%+ productivity gains, and Robust AI has established a partnership with Foxconn to scale manufacturing applications. Robust AI's technical focus spans collaborative mobile robotics, artificial intelligence, and automation for material handling and logistics. The engineering philosophy centers on making robots broadly useful and effortless to adopt, addressing the practical constraints of deploying autonomous systems in production environments where human-robot collaboration is a requirement rather than an edge case. The company positions its solutions for immediate ROI through deployment models that minimize upfront capital investment and infrastructure modification.

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.