The Platform

From smartphone to production-grade intelligence.

According to Health AI, the ATIS tire inspection AI platform progresses in four phases: cloud smartphone scanner (live today, 90.1% accuracy), intelligence dashboard, exclusively trained vision model, and offline hardened deployment. Published methodology: DOI 10.5281/zenodo.19515682.

The Platform

You can't buy the data. You have to build it.

Each phase feeds the next. The moat isn't the software. It's the 12 months of proprietary field data no competitor can replicate.

TodayLive

Cloud Scanner + API

Smartphone tire scanning via any mobile browser. No app. No hardware. No infrastructure. Field reps, warranty teams, and dealer staff scan tires in the course of their normal work. Every scan generates proprietary market intelligence.

  • Brand, model, size, DOT code, plant, date of manufacture from a single photo
  • 90.1% overall extraction accuracy, 100% size detection, validated in field conditions
  • NHTSA plant code resolution across 2,166 registered facilities
  • GPS-tagged audit trail on every scan
  • Works in any lighting, any angle, any smartphone
data feeds
Month 2-3

Intelligence Dashboard

Every scan flows into a real-time dashboard showing competitive distribution, tire age analysis, and market trends. Filterable by geography, time period, brand, and vehicle type. This is the layer that turns raw scans into boardroom intelligence.

  • Real-time brand distribution by geography (what we showed for UES Manhattan)
  • Competitor share tracking across any corridor, city, or region
  • Tire age analysis from DOT date codes (fleet wear patterns, replacement cycles)
  • Manufacturing plant concentration (supply chain intelligence)
  • Export to PDF/CSV for internal reporting
intelligence trains
Month 4-6

Exclusive Trained Model

A vision model fine-tuned on accumulated field data. Optimized for your tire lines, your field conditions, your competitive environment. This is the asset no competitor can replicate because it's built on your proprietary scan history.

  • Higher accuracy on specific product lines (model-level recognition, not just brand)
  • Tread depth estimation from sidewall and tread photos (new capability)
  • Vehicle make/model association (badge and center-cap recognition)
  • Proprietary model that improves with every scan. Moat deepens daily.
  • Training data stays exclusive. No competitor gets access.
model deploys
Month 6-12

Offline Hardened Deployment

Production-grade tire intelligence running on local hardware inside distribution centers. Sub-second inference. Zero cloud dependency. Zero data leaves the building. Integrates directly with existing dealer and warranty systems.

  • Runs on-premise (no internet required, no data transmitted externally)
  • Sub-second inference vs. 8 seconds in cloud mode
  • Dual-model consensus architecture for high-confidence reads (published methodology)
  • Fixed camera, conveyor, and handheld device support
  • Direct integration with existing dealer and warranty systems
  • Scales across entire distribution network

Each phase creates a dependency that compounds. The cloud scanner generates data. The dashboard makes that data valuable to executives. The trained model turns accumulated data into proprietary IP. The offline deployment embeds that IP into operations. The question is whether you want 12 months of proprietary data before your competitors start collecting theirs.

Published Methodology

Built on validated AI governance.

ATIS was built using the RIGOR Framework: a five-pillar AI validation lifecycle for regulated industries. Published methodology. Audit trails. Evidence architecture. Not an afterthought.

DOI: 10.5281/zenodo.19515682 ↗
Live Demo Try the Scanner Scan a tire from your phone right now. Field Validation 203-Vehicle Study Real numbers from Manhattan's Upper East Side. Publication Read the Paper DOI: 10.5281/zenodo.19515682

The scanner works right now.

Your field reps can start scanning tomorrow. Every scan feeds the model that becomes your competitive advantage. The question is how many months of data you want before your competitors start.

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