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AI quality assurance for construction scheduling data
Applying AI to check the quality of 4D construction scheduling and programme data before it reaches the people who depend on it.
The problem
Construction programmes carry thousands of linked activities, and small data errors — mis-linked dependencies, drifted dates, inconsistent codes — propagate quietly until they surface as rework on site.
Reviewing that quality by hand across a large programme is slow, and the same checks get repeated release after release.
The approach
Mapped the recurring quality checks a reviewer actually performs on programme and scheduling data, in the reviewer's own language.
Applied AI to flag the patterns worth a human's attention — not to auto-correct, but to point the reviewer at what matters first.
Kept every flag traceable back to the underlying record, so a person stays in control of the call.
The outcome
Routine quality review shifts from reading everything to checking what's been flagged, freeing reviewer time for judgement rather than scanning.
The checks run the same way every release, so quality no longer depends on who happened to review it.
Client work is confidential by default — no logos, no vanity numbers. The work shows up in the workflow, not the homepage.