How PavePal works.
From street-level imagery to decisions you can defend. Here's what happens in between.
The problems we solve.
Four places road-condition work breaks down today, and how PavePal fixes each one.
The problem
Manual inspection is slow and inconsistent.
Crews drive or walk the network recording defects by hand.
What PavePal does
AI reads the road automatically.
It detects pavement defects and assets from street-level imagery with the same standard everywhere, not one inspector's judgment.
The problem
Your condition data is scattered.
Across spreadsheets, PDFs, and people's memories.
What PavePal does
One structured record.
Every survey and field report lands in a single, geo-located dataset you can query, export, or push into your asset management system.
The problem
You only know a road's condition on the day it was last inspected.
Between inspections, you're working from data that's already out of date.
What PavePal does
A current picture.
Repeatable surveys plus on-demand field capture keep your data fresh, so you can see how the network changes over time.
The problem
Reporting to leadership and funders means compiling data you can't fully defend.
Numbers assembled by hand rarely hold up under scrutiny.
What PavePal does
Defensible outputs.
Counts and locations by asset type. Structured data that holds up in budget and audit conversations.
One pipeline turns raw road imagery into structured intelligence.
Atlas and Patrol both feed the same four stages.
- 1
Ingestion
Street-level imagery and location data come in, from Atlas survey vehicles or from your inspectors in the field via Patrol.
- 2
Processing
Imagery is cleaned, geo-referenced, and prepared for analysis.
- 3
Intelligence
AI detects and classifies pavement defects and roadside assets, and locates each one on the map.
- 4
Output
Structured condition data you can view, export, and integrate with your asset management system.
What the AI actually does.
PavePal's models read street-level imagery and identify what matters on a road: pavement defects like cracking, potholes, and surface deterioration, and roadside assets like signs, markings, and furniture. It detects 20+ asset and defect types and locates each one on the map, turning thousands of images into a structured inventory no manual team could match for speed or consistency.
Data in
- Street-level imagery (Atlas vehicles or Patrol captures)
- GPS / positioning data
- Your existing network reference data where available
Data out
- Structured condition records
- Asset and defect type, location, and imagery
- Exportable in standard formats
- Built to plug into your stack: ArcGIS, EAMS, and more