Experience Engineering

Computer vision solutions: A new line of sight for physical operations

For many organizations, physical operations across factories, warehouses, retail environments, and supply chains continue to operate blindfolded. Despite decades spent optimizing machinery and digital systems, manual processes and visual checks still create operational blind spots—making inventory, assets, and workflows difficult to monitor and contributing to undetected defects, slow processes, and avoidable errors.

Experience Engineering removes these blindfolds by extending AI into the physical world. By combining computer vision, machine learning, edge AI, digital twins, and robotics, organizations can capture physical activity as actionable data, analyze it in real time, and turn insights into direct enterprise action.

The opportunity now is to create AI-supported warehouses, production lines, supply chains and other physical systems that monitor, adapt and optimize themselves. Computer vision can track inventory and identify issues, AI can determine the right response, and robotics can act on those decisions. Digital twins can further replicate physical assets and environments, enabling teams to generate insights, simulate changes, and collaborate before taking action in the physical world.

Turning the physical world into digital value

Real-world operational environments are inherently complex and unpredictable. Variations in lighting, packaging, layouts, and human movement create edge cases where off-the-shelf AI models fail. Achieving reliable accuracy requires models to be fine-tuned to the specific operational context, but capturing and manually annotating enough real-world imagery to cover those edge cases is slow, costly, and hard to scale.

And detection is only half the battle. To generate real ROI, visual intelligence must translate into action within existing enterprise systems (WMS, ERP, and MES) without major infrastructure overhauls or unnecessary cloud and bandwidth costs. Our computer vision engine addresses both challenges through a continuous train, deploy, and optimize loop designed to accelerate development, bring intelligence closer to operations, and continuously improve performance.

Train: Pre-trained open-source models are paired with programmatic synthetic data to accelerate model development and reduce reliance on capturing and manually labeling millions of real-world images.

Deploy: Models run in real time on hardware-agnostic edge nodes, detecting patterns, triggering alerts, and prompting automated operational interventions.

Optimize: Data collected from live operations feeds back into digital twins and closed-loop MLOps pipelines to validate assumptions, simulate scenarios and continuously refine model accuracy.

Unlock the potential of real-time 3D technology to enhance your product portfolio.