How does
Tractable
Use AI?
Helps you process auto insurance claims faster.
Project Overview
Using AI to analyze vehicle photos for damage and give repair estimates without waiting for someone to visit and inspect.
Layman's Explanation
Take a few pictures of a car, and the AI can tell you what's damaged and how bad—no need to wait for a mechanic or inspector.
Details
Tractable’s solution lets customers capture guided photos or video of a vehicle on a phone, then runs quality checks to ensure the right angles and clarity. Images are uploaded to the cloud, where computer vision models identify parts, detect and segment damaged areas, and estimate severity. Outputs feed a cost model that maps parts and labor to a repair plan, along with a confidence score and flags for potential issues. The result is an estimate delivered in minutes that supports straight through processing.
Under the hood, the system uses large scale image models trained on extensive labeled crash and repair data to recognize specific vehicle parts and typical damage patterns at pixel level granularity. It evaluates whether components are repairable or need replacement, aggregates costs, and returns a structured estimate that integrates with insurer and repair shop systems through APIs. (Assumption: models include CNNs and vision transformers trained via transfer learning on proprietary datasets.)
For operations, this reduces inspection bottlenecks, speeds decisions at first notice of loss, and improves consistency across adjusters and shops. It also helps with triage, routing total losses quickly, and provides clear audit trails with certainty scores for quality control and fraud checks. The net effect is faster cycle times, lower handling costs, and improved customer experience.
Analogy
It’s like having a car-savvy friend in your phone who can spot dents and dings just from photos.
Machine Learning Techniques Used
- Object Detection and Instance Segmentation; to locate parts and outline damaged regions precisely.
- Damage Severity Regression; to quantify impact levels and support repair vs replace decisions.
- Anomaly and Fraud Detection; to spot image manipulation and inconsistent submissions.
- Uncertainty Estimation and Calibration; to produce confidence scores and guide human review.
- Transfer Learning; to adapt pre trained vision models to automotive damage domains.
- Active Learning; to prioritize edge cases and continuously improve model accuracy.
- Image Quality Assessment; to validate angles, lighting, and completeness of photo sets.
- OCR; to read plates or VIN snippets when needed for identification and parts mapping.
More Use Cases in
Technology
Novelty Justification
AI-powered, photo-based vehicle assessment is a leading-edge process in insurance and auto sales.
Project Estimates
