Samp

Scan-to-Value: How Reality Capture + AI Are Redefining Asset Management for Industrial Facilities

Reality capture provides a reliable view of what is physically there. Its value grows when that reality is connected with the technical information people use every day to operate, maintain and evolve industrial facilities.

At INTERGEO 2026, Laurent Bourgouin, CEO of Samp, joined NavVis at the booth theater to present Scan-to-Value: how reality capture can become a foundation for Industrial Asset Intelligence.

At the centre is Shared Reality, Samp’s solution for bringing field reality and technical information together.

AI-assisted asset recognition identifies industrial equipment within reality capture, helping reconcile physical assets with their engineering information. This gives all teams a consistent operational context grounded in what is actually in the field.

As facilities evolve, Shared Reality helps keep 3D reality and technical information aligned with current field conditions.

The result goes beyond 3D: Industrial Asset Intelligence for the people who operate, maintain and evolve industrial facilities – and increasingly for agentic AI and robotics.

Watch Laurent Bourgouin’s full INTERGEO 2026 keynote below.

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Through automated spatial tagging—powered by in-scan Optical Character Recognition (OCR) embedded within Shared Reality and its Assetizer AI engine—operators can now extract physical label data directly from 360° panoramic imagery embedded in 3D scans. By anchoring physical plant labels directly to CMMS tags and 3D geometries without requiring an existing, validated P&ID as an intermediary, industrial operators can rapidly turn monolithic scans into interactive digital twins, streamline turnaround (TAR) preparations, and unlock comprehensive line-by-line P&ID validation via automated pipe crawling.

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Webinar: Automated Spatial Tagging

October 18, 2026

Discover how Shared Reality helps you visually isolate piping circuits and corrosion loops instantly, bridging the gap between engineering data and field execution.