- 01Raw clash reports can list thousands of items, most trivial, duplicated, or irrelevant — the bottleneck is triage, not detection.
- 02Machine learning classifies clashes by type and root cause and prioritises them by severity, turning noise into a ranked action plan.
- 03This makes coordination meetings productive: teams resolve the conflicts that actually threaten construction.
- 04It’s a concrete example of applied AI compressing the coordination timeline.
Anyone who has opened a raw clash report knows the real problem with clash detection isn’t finding clashes — it’s the thousands you find, most of which don’t matter. Manually sorting the critical from the trivial is where coordination time disappears. AI clash detection attacks that triage bottleneck directly.
The triage problem
Federate a few discipline models and a clash engine will happily return thousands of intersections: duplicates, self-clashes, insulation touching structure by a millimetre, and genuinely critical conflicts, all jumbled together. A human wading through that list is slow and inconsistent, and coordination meetings drown in noise.
Why it matters
- Faster coordination: teams spend meeting time resolving real conflicts, not filtering noise.
- Consistency: prioritisation follows learned rules instead of individual habits.
- Fewer escapes: critical clashes are less likely to be lost in a long list.
- Shorter timeline: quicker coordination directly compresses the design-to-construction schedule.
Coordinate on what matters
AI-assisted issue triage may help a coordination team focus its review, but the project should measure review time, precision, missed issues, issue closure, rework, and programme effects before claiming an improvement.
