Generative AI has transformed how we process information in digital spaces, but the next frontier of innovation is taking place in the physical world. Intelligence is leaving the browser and moving directly to factory floors, sports arenas, transportation networks, and surgical suites. By 2030, 80% of humans will interact with smart robots on a daily basis, driving a market projected to reach $960 billion by 2033.
To lead this transition, we are consolidating our specialized IoT and Robotics practices into a unified service delivery engine: The Physical AI Studio.
Globant is recognized as a Specialist in the 2026 Gartner® Emerging Market Quadrant for Physical AI Services (PAIS). Gartner states “Specialists quadrant represents vendors that bring deep expertise in narrowly defined areas of physical AI, delivering innovative impact within specific domains with less impact to broadly disrupt market structures. PAIS offerings in this quadrant are characterized by focus, precision and specialization, rather than scale or ecosystem reach. This focus and precision makes their value to clients equally anchored to that domain specificity, which buyers should require, evaluate and use as the basis of selection.” [1]
Four Core Practices Powering the Physical AI Studio
Our studio unites hardware enablement, edge processing, and cloud orchestration across four interdisciplinary practices:
- Edge AI Engineering: Deploying Vision-Language Models (VLMs), computer vision, and model quantization (pruning and distillation) directly onto physical devices. This enables millisecond-level inference, enhanced data privacy, and zero-latency decision-making without relying on the cloud.
- Embedded Systems: Building efficient solutions that take the best of the hardware in terms of energy, efficiency, speed, and time, enabling powerful AI edge-first capabilities, real-time and secure solutions that can only be achieved from the silicon first level while maintaining portability and scalability.
- Robotics: Designing end-to-end autonomous movement and interaction. Leveraging ROS2 and the NVIDIA tech stack, we build, stress-test, and validate robotic behaviors using digital twin simulations and synthetic data before physical deployment.
- Physical AI Platforms: Managing multi-cloud edge infrastructure across AWS, Azure, and GCP. We provide end-to-end IoTOps and RobotOps for fleet provisioning, over-the-air (SOTA/FOTA) updates, and continuous MLOps lifecycle monitoring.
Engineered for Reality: Proven at Scale
Physical AI requires bridging digital models with physical realities where latency, safety, privacy, security, and reliability are non-negotiable. The Physical AI Studio builds on battle-tested enterprise implementations:
- Intuit Dome (LA Clippers): Orchestrated a phygital stadium ecosystem integrating 20,000+ edge devices across biometric access control, real-time crowd tracking, and 40+ frictionless grab-and-go retail locations.
- Theme Park Attraction Safety: Deployed an air-gapped, on-premises computer vision system achieving ~500ms response times for guest and object detection, delivering instant GO/NO-GO safety verification for dispatch operators.
- Autonomous Mobility: Developed ROS-based C++ state machines to bridge AI perception stacks, cloud infrastructure, and in-vehicle systems for autonomous taxi fleets.
Physical AI demands more than software—it requires mastering the laws of physics. The Physical AI Studio provides the engineering depth, ecosystem partnerships, and hardware-level precision needed to bring real-world intelligence to life safely and reliably.
[1] Source: Gartner Report, Emerging Market Quadrant for Physical AI Services — Established Vendors, By Katie Gove, Jonathan Davenport, etc., June 2026.
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