Project Summary

This project delivers an AI-powered data validation engine that helps infrastructure teams work smarter and faster. By automatically checking construction data against client standards, such as the National Highways Asset Data Management Manual, it removes the burden of manual compliance checks and integrates seamlessly with common data environments. The engine validates 3D models to ensure naming conventions and metadata fields are consistent across all objects, cutting down on administrative tasks, streamlining workflows, and reducing costly delays.

Project Achievements

Major transport projects such as highways, railways and maritime generate large volumes of information. Every scheme produces thousands of written documents, detailed 2D drawings and complex 3D models. Checking that all such information meets the required data quality standards has always been slow, expensive and difficult for project teams to achieve. To address that, our new cloud-based system brings together 3D Repo and Google Gemini in order to create a powerful data validation engine that can scan hundreds of thousands of objects in a matter of seconds. It checks every element of a 3D model against the National Highways Asset Data Management Manual (ADMM) and delivers instant clarity on what is compliant and what is not. Project teams can now verify data quality automatically, remove manual checking and accelerate project delivery. The result is faster decision making, higher confidence and a dramatic improvement in the consistency of information being delivered across every stage of a project.

Conclusions

Our technology has the potential to transform how infrastructure projects are delivered and managed. Instead of relying on manual rule based data validation tools on desktops, the engineering teams have now gained a scalable cloud-based system that understands data requirements semantically and can validate large projects automatically. This reduces errors, strengthens compliance and saves significant time for all stakeholders. By making data quality checks faster and more reliable, we believe we also support safer and more efficient infrastructure delivery throughout.

Next Steps

We need to advance through several TRL stages by strengthening real world evidence, technical robustness, and commercial readiness. This includes expanding pilot deployments to validate performance in operational environments, completing formal compliance and security assessments, and running extended reliability and drift. We also need to finalise integration pathways with existing CDEs and PMO systems, refine user workflows through human in the loop testing, and establish the commercial framework licensing, SLAs and onboarding.