Insurers and Other Users of SICP
Artificial Intelligence has become a buzzword in the automotive world — but for Glasscare®, it’s been a working reality for years. Developed and refined long before the rest of the market even considered it, Glasscare® AI combines advanced machine learning with real-world repair data to deliver the industry’s most accurate and trusted windscreen damage validation system.
Unlike systems trained on synthetic imagery, Glasscare® AI was built using genuine damage data captured across thousands of vehicle types, lighting conditions, and glass configurations. This allows the platform to recognise the full range of impact scenarios — from faint star breaks and edge cracks to complex, multi-impact damage.
Each image is classified and scored against repair viability thresholds established in partnership with repair professionals and insurers. This means Glasscare® AI doesn’t just “see” a chip or a crack; it interprets the context — size, depth, position, and risk factors — before recommending the correct repair path.
The result is AI that thinks like a glass expert, but with the speed, consistency, and scalability that only machine learning can deliver.
When a consumer uploads a photo of their damaged windscreen, Glasscare® AI analyses it instantly using multiple neural-network models trained to detect micro-fractures, reflection distortions, and even contaminants such as dust or water.
The system then determines:
This process takes less than a second, producing an immediate, validated decision that can be acted upon by insurers, networks, or repair partners.
By consistently identifying repairable damage, Glasscare® AI helps insurers and consumers avoid unnecessary replacements — a result that saves time, reduces costs, and eliminates the need for ADAS recalibration often required after replacement.
Where Glasscare® AI truly stands apart is its integration within the Glasscare® TPA (Third-Party Administrator) platform. Once validated, the AI result feeds directly into the claim workflow, triggering:
This end-to-end automation ensures no manual intervention, no mis-routed claims, and complete traceability from image capture to settlement.
Glasscare® AI’s accuracy is continually refined through feedback loops within the Glasscare® network. Every validated outcome — whether confirmed or overridden — feeds back into the model, strengthening its predictive capability and further reducing error rates over time.
With millions of images processed and validated, Glasscare®’s dataset has become the industry benchmark for real-world damage detection and classification accuracy.
Each validated repair avoids the carbon footprint associated with manufacturing, shipping, and fitting a new windscreen — as well as the additional journey time and resources involved in ADAS recalibration.
When scaled across insurer portfolios, these savings represent thousands of tonnes of CO₂ avoided annually, aligning perfectly with ESG and sustainability objectives.
From a crack in Melbourne to a chip in Manchester, Glasscare® AI ensures every decision is made with evidence, consistency, and confidence.
By combining deep learning, real-world training data, and a fully digital claim journey, Glasscare® continues to redefine what “intelligent glass care” means — not just for today’s connected vehicles, but for the autonomous future ahead.
Glasscare® AI — Smart. Sustainable. Proven.