Build agentic physical AI systems with a modular, production-ready stack on the GAIN (Grid Dynamics AI-Native) Platform for robotic manipulation, visual inspection, IoT quality control, and digital twin simulations, delivered by Forward Deployed Engineers who work inside your environment to design, validate, and scale factory and warehouse workflows.
The platform
Unified stack for physical AI deployment in production
Robotic manipulation
Automate robotic workflows with a low-code layer for AI-driven industrial operations, reducing manual handling costs and increasing throughput on existing lines.
- Orchestrate pick-and-place, flexible assembly, and toolpath execution
- Apply Vision-Language-Action (VLA) models for robot perception and motion
- Run on ROS-compatible, hardware-agnostic robots and edge devices
- Manage fleets and monitor robot and cell performance
- Handle complex bimanual and multi-step tasks
Digital twin simulation
Validate and optimize lines, cells, and vision systems in high-fidelity digital twin simulations before deployment to improve throughput, reduce errors, and de-risk changeovers.
- Integrate with NVIDIA Omniverse and Siemens Tecnomatix
- Program and validate robotic cells in virtual environments
- Test computer vision with configurable virtual cameras
- Keep a live digital twin synchronized with operational data
- Optimize scheduling and routing against ERP and MES constraints
AI for visual process monitoring
Turn CCTV and visual feeds into actionable events with vision-language models to improve safety compliance, reduce shrink and defects, and give supervisors real-time visibility into floor activity.
- Optimize space and layout across production, warehouse, and retail sites
- Track labor activity and process performance over time
- Verify packaging and order accuracy at the line and dock
- Detect PPE violations, unsafe proximity, and restricted-area access
- Trigger incident workflows from natural-language policies
IoT control tower
Unify machine, camera, robotics, and environmental data in an IoT control tower to detect issues earlier, cut unplanned downtime, and support better, faster decisions on the production floor.
- Fuse multimodal device and sensor streams into a single view
- Detect anomalies early and support predictive maintenance
- Improve quality control with visual and sensor analytics
- Simulate throughput and facility changes with digital twins
- Deliver prescriptive guidance to operators and analysts
Build, deploy, and scale with Forward Deployed Engineers embedded in your team
Design low-code workflows that combine AI and existing automation, refine foundation models with task data, and use the GAIN Rosetta framework, an AI agent standards enforcement layer, to clarify requirements before deployment.
Run on ROS-compatible robots, edge servers, and cloud infrastructure, validate workflows in high-fidelity digital twin simulations, and accelerate setup with cloud-agnostic IoT architecture and integration blueprints.
Expand fleet management, telemetry collection, and continuous model updates across sites while tracking KPIs in real time and synchronizing live digital twins with operational data.
Proven applications
1000x faster inspection cycles
Robotic AI feasibility analysis
Challenge: A precision manufacturing leader, SmartRay, relied on a manual feasibility process for quality inspections that took several days per component.
Solution: Created a Physical AI platform that automated the analysis of technical specifications using robotic vision and AI reasoning.
Impact: Transformed the inspection timeline from days to minutes, representing a 1000x increase in operational speed.
Real-time shelf inventory visibility
Computer vision shelf intelligence
Challenge: PepsiCo lacked visibility into retail shelf compliance and real-time out-of-stock scenarios.
Solution: Deployed a Shelf Intelligence solution using AI-driven analytics and mobile computer vision to monitor retail execution.
Impact: Enabled immediate restock triggers and drastically reduced out-of-stock occurrences through automated compliance reporting.
24x faster anomaly detection
AI analytics & real-time observability
Challenge: A high-tech manufacturer, Jabil, struggled with delayed reactions to production line anomalies, leading to increased scrap and rework.
Solution: Integrated an AI-driven observability layer across the factory floor to monitor machine telemetry in real-time.
Impact: Accelerated anomaly detection from days to hours, a 24x improvement in time-to-awareness.
Build on production-grade physical AI infrastructure
Physical AI systems depend on simulation fidelity, sensor integration, and production infrastructure that generic AI stacks do not provide. GAIN Platform for Physical AI is built with NVIDIA technologies, integrates with major cloud providers, and supports joint go-to-market motions across manufacturing, logistics, and related sectors.
Core technology stack partner. The GAIN Platform for Physical AI is built on NVIDIA’s software stack, including Omniverse and Metropolis, with joint go-to-market for manufacturing and CPG companies.
Choose an engineering team that builds in your environment
Complex physical AI projects break when requirements look clean on paper but change on the factory floor. Grid Dynamics Forward Deployed Engineers (FDEs) work inside your environment, build against your systems and constraints, and adapt the solution as edge cases appear.
Embed engineering where the work happens
Your team works directly with Grid Dynamics FDEs who write code, build integrations, and adapt the system to your specific environment, data, and workflows.
Production-proven IP, not POCs
You inherit proven implementation patterns from deployments across manufacturing, logistics, and digital health, rather than starting from a generic proof of concept.
Platform + FDE = faster time to value
You start with a validated solution for a defined problem. A modular stack and engineers who can close gaps fast shorten the path from discovery to deployment and ROI.
Extend into the GAIN ecosystem
Physical AI is one of four GAIN domains, alongside agentic commerce, SDLC, and risk and compliance. That gives you access to adjacent capabilities under the same Forward Deployed Engineering delivery model.
Ready to deploy smarter automation, safer operations, and highly adaptive production environments?
Start with a half-day workshop, follow with a 2-3 week discovery sprint, then validate with a scoped prototype.