Harmattan AI

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.

We're Gatik, and we're building autonomous trucks that handle the middle-mile logistics for some of North America's biggest retailers. Instead of chasing long-haul trucking or robotaxis, we focused on the regional routes between distribution centers and stores - where goods move frequently but predictably. This is where our Level 4 autonomous system, Gatik Driver™, really shines. It's built specifically for fixed, repeatable routes that retailers depend on every single day, making it possible to remove the driver entirely while keeping operations safe and efficient. What makes us different is that we're not running experiments or small pilots. Our trucks are commercially deployed right now, moving freight for Fortune 50 companies like Walmart, Kroger, and Loblaw across multiple states. We've completed over 60,000 fully driverless deliveries without an incident, and we're the first company in the U.S. operating driverless trucks at commercial scale. Our team combines expertise in autonomous systems with a pragmatic approach to solving real supply chain problems - no hype, just reliable freight that keeps shelves stocked and businesses running smoothly.

In 2025, Zhou Xian and Théophile Gervet recognized a fundamental bottleneck in the robotics field: while digital AI had made extraordinary progress, physical AI - the intelligence enabling machines to perceive, understand, and interact with the real world - lagged dramatically behind. Physical labor contributes an estimated $30-40 trillion to global GDP, yet over 95% remains unautomated because current robotic systems rely on brittle, rigid, and overfitted software stacks that are narrow in scope, costly to deploy, and impossible to scale. They founded Genesis AI to bridge this gap, assembling a team of world-class talent from Mistral AI, NVIDIA, Google, CMU, MIT, Stanford, Columbia, and UMD. Their vision: build a universal robotics foundation model and horizontal robotics platform that would unlock unlimited physical labor. Genesis would achieve this through a scalable data engine fusing real-world robot interaction with high-fidelity physics simulation, an open-source ecosystem to accelerate field-wide progress, and robust robot deployments thriving in messy, unconstrained environments. With $105 million in seed funding from Eclipse Ventures and Khosla Ventures, Genesis emerged from stealth in July 2025 to build what's next in physical AI.

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.

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.

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.