Results from real infrastructure. Not projections.
AllAiz methods were developed through hands-on engineering work and validated through live deployments. Every number shown here comes from actual field work on actual infrastructure.
What the data shows.
These results come from real deployments on real infrastructure. Each one is sourced, dated, and attributable to a specific engagement.
Pavement defect detection accuracy
ICCSTE 2026 — Barcelona · June 2025Engineering validation resultLiDAR point density vs. conventional survey
Field deployment — Sharjah road network, UAE · 2024Live pilot resultRigorous methods. Validated workflows.
Computer vision system for pavement defect detection, validated against structured engineering benchmarks. 91.1% detection accuracy across crack classification, severity grading, and location mapping.
Engineering methodology for large-scale pavement condition mapping using street-level imagery. Enables network-level assessment without dedicated survey equipment.
Systematic comparison of mobile LiDAR point cloud density against conventional static survey on matched road sections. Produced the 21.7× density advantage.
Mobile LiDAR survey and pavement condition assessment. Produced condition index mapping, maintenance priority ranking, and a validated point cloud dataset.
UAE · 2024 · Pilot engagementFurther field deployment references will be added as client permissions allow. Current engagements are subject to confidentiality agreements.
⚑ Add when approved by clientsWe do not claim what we cannot defend.
Every number on this page is real, sourced, and attributable. If a result is not yet validated to engineering standards, it is not presented as proof.
We would rather show two real results than ten impressive projections.
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