Construction & Infrastructure

Construction & Infrastructure

Project-driven, asset-heavy, and earlier on the data-maturity curve than most heavy industry.

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Key takeaways

01

Idle plant bleeds quietly.

Idle equipment on a site runs into thousands of dollars a day in lost labour, missed milestones and subcontractor penalties, and it tends to be nobody's single job to notice.

02

The data is scattered.

Work is organised as one-off projects, so data is spread across sites and jobs and rarely gathered into anything that learns from the last project.

03

The foundation comes first.

This sector is earlier on the maturity curve, so the honest path is to build the base before the use cases.

The pressure

Why this matters now

Taking in marine and dredging work as well, the situation across all of it is an industry of enormous assets and tight margins relying more on experience and less on its own record than almost any other heavy sector.

Operations & AI

Projects don't learn from each other.

Because each job is bespoke and the data scatters, hard-won lessons rarely carry from one project to the next, and the same costly mistakes recur.

Asset utilisation is opaque.

Heavy plant moves between sites with little visibility into where it sits idle, which is a direct and recurring drain.

Data maturity is genuinely lower.

Less instrumentation and less structured data means the work starts with foundations, not with a flashy model, and a vendor who pretends otherwise will disappoint.

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Operations & AI
What we plug into

Integrations & data sources

We've built data platforms for our clients, designed to ingest from the telematics, SCADA, historian and ERP/EAM systems used across construction, including the ones below.

OEM telematics

Caterpillar (VisionLink)KomatsuVolvo CE (CareTrack)JCB (LiveLink)John Deere (JDLink)LiebherrHilti (ON!Track)

Site / fleet platforms

TrackunitTrimbleTopcon

ERP / EAM / project

SAPIBM MaximoProcore

Capability statement, not a checklist. No per-vendor endorsements implied.

From signal to value

How to get there

It tends to run in a sensible order, and we can join at any point.

1

understand

where the operation's data actually stands

2

build

the foundation

3

prove

the value on a contained use case

4

embed

and scale as maturity grows

Bring us the asset you worry about most.

Consultation

That is usually where the clearest, fastest-paying use case is hiding.

Tell us what you run and where it hurts - we'll tell you the smallest first move that pays.

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Consultation