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Intel & Outsight Strategic Agreement on Physical AI

Intel and Outsight Announce Strategic Collaboration to Bring Physical AI–Powered Spatial Intelligence to the Enterprise Edge

Outsight’s Shift platform integrated into Google Distributed Cloud Edge powered by Intel Xeon 6 SoC – Live demonstration at Google Cloud Next 2026

LAS VEGAS and PARIS, April 21, 2026,

Intel Corporation and Outsight, the global leader in 3D Spatial Intelligence, whose platform equips some of the world’s largest international airports, train stations, hospitals, and venues, today announced a strategic collaboration to accelerate the deployment of physical AI solutions for real-time people and objects flow monitoring at enterprise scale.

As part of this collaboration, Outsight’s Shift platform has been integrated into the latest version of Google Distributed Cloud (GDC) Edge, combining Intel Xeon 6 SoC with AMX (Advanced Matrix Extensions) for accelerated CPU-based inferencing and Outsight’s advanced 3D real-time processing platform to deliver turnkey physical AI capabilities directly at the edge of enterprise networks.

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A live demonstration of the integrated solution will be showcased at the Intel booth (#6901) during Google Cloud Next 2026, taking place April 22–24 at the Mandalay Bay Convention Center in Las Vegas.

Bringing Physical AI to Enterprise Infrastructure

The collaboration addresses the growing demand from operators of airports, train stations, industrial facilities, smart cities, and large venues for real-time, privacy-preserving intelligence about how people, robots and vehicles move through their physical environments.

Outsight’s physical AI software processes data from 3D LiDAR sensors, combined with multiple data sources including camera and business data systems (e.g. AODB, PoS), to continuously track the movement of people and objects.

What is Physical AI?

Physical AI is an advanced technology approach that processes real-world sensory data to understand and analyze physical environments in real-time.

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These insights are delivered through Motional Digital Twins, real-time 3D digital replicas of physical flows that serve as the operational interface through which end users monitor, analyze, predict, and optimize their environments.

By running natively on Xeon 6 SoC processors without requiring power-intensive GPUs, the solution delivers cost-effective, scalable 3D AI inference at the edge.

The integration into Google Distributed Cloud Edge enables enterprise customers to deploy the Shift platform as a managed, cloud-connected edge workload, benefiting from streamlined operations, centralized management, and Google Cloud’s security and scalability framework.

Intel Xeon 6 processors deliver exceptional performance for the widest range of workloads and are engineered for efficiency and low total cost of ownership. On Feb. 24, 2025, Intel launched the Intel Xeon 6 SoCs with P-cores – more power-efficient, edge servers with Intel vRAN Boost and media acceleration, and networking built in. (Credit: Intel Corporation)

Raising the Bar: Global Situation Awareness for Robots

The integrated solution goes beyond people and vehicle flow monitoring by enabling global situation awareness, a shared, real-time representation of reality with temporal and spatial continuity, available to all operators and mobile robots on site.

In complex environments such as factories, warehouses, and airports, mobile robots are inherently limited by their onboard sensors and available processing: blind spots, occlusions, and instantaneous perception prevent them from understanding what lies beyond their immediate field of view.

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How the third generation of Digital Twin technology is transforming the way infrastructure operators understand and optimize the movement of people and vehicles.

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Motional Digital Twins solve this by providing an infrastructure-level intelligence layer that continuously tracks every person, vehicle, and object across the entire premises, assigning a unique anonymous ID maintained over time.

In effect, the infrastructure becomes a global sensor and intelligence layer, giving each robot the capability to see and understand everything happening across the site, in real time.

This enables three foundational capabilities for robotics and operations:

Continuous Individual Tracking, precisely positions each person or object across large distances without interruption, overcoming the limitation of visual line-of-sight, and making every position immediately available to all robots and operators on site.

Situational Awareness, perceiving the environment globally, beyond the line of sight of each individual robot, and associating external data, event history, and attributes with each tracked entity.

Actionable Insights and Prediction, interpreting situations at scale, deriving decision-ready intelligence, and anticipating future states across the entire premises.

Outsight Multi-Level Continuous Monitoring

Live Demonstration at Google Cloud Next 2026

At Google Cloud Next 2026, Intel and Outsight will present a live, full-scale demonstration of the integrated physical AI solution at Intel’s booth.

Attendees will experience firsthand how the platform processes millions of 3D data points per second to detect, classify, and track individuals and objects in real time, all while preserving privacy through anonymous, non-image-based sensing.

The demonstration will showcase key capabilities including real-time crowd density monitoring, queue management analytics, flow visualization through a live Motional Digital Twin, and predictive operational insights, all running on Intel-powered edge infrastructure within the Google Distributed Cloud framework.

Industry Perspectives

“This strategic collaboration with Intel represents a major milestone in making physical AI accessible at scale across critical infrastructure worldwide. Said Raul Bravo, President and Co-founder of Outsight. By integrating our Shift platform into Google Distributed Cloud Edge, powered by Intel’s processors, we are enabling operators of airports, stations, factories, and smart cities to deploy a real-time 3D replica of everything moving across their premises, while providing global situation awareness to mobile robots, as a standard component of their edge infrastructure. Together, we are setting a new standard for how organizations understand and optimize the physical world.”

“Enterprise AI is about bringing intelligence closer to where decisions are made, at the edge. Our collaboration with Outsight demonstrates how Intel’s edge computing platform, combined with best-in-class physical AI software and the Google Distributed Cloud, enables organizations to transform their physical operations with real-time, CPU-based 3D AI inference. Emphasized Bhupesh Agrawal, General Manager, Private 5G and Enterprise AI, Intel. This is exactly the kind of scalable, cost-effective solution that enterprises need to unlock the full potential of physical AI.”

Key Benefits of the Integrated Solution

Real-time Physical AI at the Edge: Outsight’s Shift platform processes massive 3D LiDAR data streams in real time on Intel Xeon 6 SoC processors, delivering sub-100-millisecond insights without cloud round trips.

Privacy by Design: The solution uses anonymous 3D point cloud data, with no facial recognition and no personal data captured, ensuring compliance with GDPR and other privacy regulations.

Global Situation Awareness for Robots: Motional Digital Twins provide mobile robots with premises-wide, context-aware intelligence that extends beyond their onboard sensors, enabling safer navigation, smarter path planning, and coordinated fleet operations.

Scalable and Cost-Effective: CPU-based AI inference eliminates the need for expensive GPU infrastructure, lowering total cost of ownership while enabling deployment across thousands of locations.

Hardware-Agnostic 3D Sensing: The Shift platform is compatible with LiDAR sensors from all leading manufacturers, giving operators the freedom to choose the best hardware for their specific environment.

Multi-Modal Data Fusion: Contextual information from LiDAR, cameras, business data systems, and other sources is fused into a unified spatial reference, enriching situational awareness beyond what any single sensor can provide.

Cloud-Managed Edge Deployment: Integration with Google Distributed Cloud Edge provides centralized management, automated updates, and enterprise-grade security for distributed edge deployments.

Proven at Scale Across Five Continents

Outsight’s Spatial Intelligence solutions are already deployed at scale in major airports, train stations, stadiums, industrial sites, and smart cities across five continents, monitoring the journeys of over 280 million people per year.

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The company has received seven citations as emerging leader from Gartner in the digital twin and spatial computing markets, and has been recognized with numerous industry awards including the CES Best of Innovation Award, the Frost & Sullivan Global Technology Innovation Leadership Award, the Edge AI and Vision Product of the Year, the LiDAR Leader Award, and the Airport Technology Excellence Award.

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Gartner® Recognizes Outsight as a Key Player in Spatial Computing

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TECHNOLOGY

Outsight enters the Intel® Partner Alliance as a Gold Member

Together with Intel, we are set to deliver unprecedented solutions in the emerging field of Spatial AI.

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Frequently Asked Questions

  • Can LiDAR-based spatial AI run on a CPU without a dedicated GPU at the edge?

    Yes. Intel's Xeon 6 SoC includes Advanced Matrix Extensions (AMX), a hardware acceleration block built into the CPU specifically for matrix-heavy AI inference workloads. This allows 3D point cloud processing and entity tracking to run at sub-50ms latency without a discrete GPU. Outsight's SHIFT platform demonstrated exactly this capability when integrated into Google Distributed Cloud Edge powered by Intel Xeon 6 SoC, showing that infrastructure-based Physical AI can operate at the enterprise edge without dedicated GPU hardware. The practical consequence for operators is lower rack power draw, fewer hardware SKUs to qualify and maintain, and a total cost of ownership profile that supports deployment across many distributed sites rather than a small number of GPU-equipped data centers.

  • What is Google Distributed Cloud Edge and why does it matter for physical AI deployments?

    Google Distributed Cloud Edge is a managed hardware-and-software stack that extends Google Cloud's control plane to on-premises or near-premises locations. For physical AI workloads, this means the inference pipeline runs at the site (sub-100ms latency, no cloud round trips), while configuration, software updates, and security policies are managed centrally through Google Cloud. Enterprise operators running dozens of airports or factories benefit because they avoid building separate edge management infrastructure for each site. Outsight's SHIFT platform deploys as a managed workload on Google Distributed Cloud Edge via the Intel Xeon 6 SoC integration, allowing real-time 3D perception from infrastructure-mounted LiDAR sensors to run directly at each physical site without requiring a dedicated cloud uplink for every processing decision.

  • How does infrastructure-mounted LiDAR give autonomous mobile robots a wider view than their onboard sensors?

    A robot's onboard sensors see only what is within line of sight and within the sensor's detection range, typically a narrow forward cone. Infrastructure-mounted LiDAR covers the entire floor plan continuously, tracking every person, vehicle, and object with a persistent anonymous ID. Outsight's approach places LiDAR in the infrastructure rather than on the robot itself, feeding a real-time Motional Digital Twin that the robot can query to know what is happening around a blind corner, behind a shelf, or across the warehouse before it physically reaches that position. This shared spatial map extends effective perception radius from a few meters to the full premises, removing the fundamental blind-spot limitation that onboard sensors cannot solve on their own.

  • What is multi-modal data fusion in a spatial intelligence platform?

    Multi-modal fusion means combining position and trajectory data from LiDAR with attributes from other sources: camera streams, access-control logs, business databases such as point-of-sale systems, flight information (AODB), or Wi-Fi dwell signals. LiDAR provides the spatial backbone and the persistent anonymous ID for each tracked entity, while the other sources attach contextual attributes to that ID. The SHIFT platform by Outsight operationalizes this approach through open integrations, allowing operators to enrich a real-time 3D scene with data layers from existing site infrastructure. The result is a richer situational picture than any single sensor type can produce alone, without requiring any one sensor to do everything.

  • How is 'anonymous by definition' different from video anonymization software?

    Video anonymization processes images or video frames after capture and blurs, pixelates, or masks faces before storage. The raw identifiable image still exists in memory or on the network during that processing step, creating a window of exposure. LiDAR-based capture never records pixels at all: the sensor emits laser pulses and records return distances, producing a 3D point cloud that contains shape and motion but no facial geometry or license plate text. There is no post-processing anonymization step because no biometric data was captured in the first place. Outsight describes this property as "anonymous by definition" and builds it into the SHIFT platform.

  • What does 'continuous individual tracking' mean in a large venue or factory, and why is uninterrupted ID important?

    Continuous individual tracking means each person or object is assigned a single anonymous ID the moment it enters a monitored area, and that ID persists without interruption until the entity exits, even when moving between overlapping sensor zones, passing through crowds, or briefly leaving one sensor's field of view. Outsight's SHIFT platform handles this through infrastructure-based 3D LiDAR perception, where the Motional Digital Twin maintains consistent object identity across the entire sensor network in real time. Uninterrupted ID matters because analytics that depend on journey time, dwell, or behavioral sequence (such as detecting a worker who enters a restricted zone, leaves, and re-enters) require a consistent identity reference throughout. Fragmented IDs from handoff gaps corrupt those calculations and generate false alerts.