Keltbray–Multiverse AI training: data-led project controls explained for engineers
Reviewed by Tom Sullivan
First reported on New Civil Engineer
30 Second Briefing
Keltbray has enrolled 25 employees on Multiverse-run upskilling courses focused on artificial intelligence and data tools, targeting staff involved in infrastructure delivery and project controls. Training is expected to cover practical use of analytics platforms and automation for tasks such as programme optimisation, cost forecasting and site data capture. For engineers, this signals growing demand for capability in handling large project datasets and integrating AI-assisted workflows into planning, temporary works design and risk management.
Technical Brief
- Cohort size is fixed at 25 Keltbray employees for the Multiverse-delivered programme.
- Training is delivered by Multiverse, a specialist external digital skills provider rather than in-house trainers.
- Programme structure is described as “upskilling courses”, implying modular, short-course delivery rather than formal degrees.
- Multiverse’s involvement indicates content aligned with current commercial data tooling rather than bespoke internal software.
- For civil and infrastructure contractors, third-party AI/data academies like Multiverse are becoming a common delivery route.
Our Take
Keltbray already features in our Infrastructure coverage through schemes such as the planned redevelopment of Glasgow’s 4.4-acre Chinatown site, so structured AI and data training for 25 employees is likely to feed directly into its capability on complex urban regeneration projects.
Across the 756 Infrastructure stories in our database, explicit AI or artificial intelligence themes are still relatively sparse, so Keltbray’s Multiverse tie-up positions it among the earlier UK contractors formalising data skills rather than relying on ad hoc digital champions.
For a contractor of Keltbray’s scale, training 25 staff suggests a targeted cohort—probably project controls, planning, and commercial teams—rather than a broad rollout, which typically signals a pilot phase before embedding AI-driven workflows more widely if the productivity gains are proven.
Prepared by collating external sources, AI-assisted tools, and Geomechanics.io’s proprietary mining database, then reviewed for technical accuracy & edited by our geotechnical team.
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