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I will build ai vehicle damage detection for insurance claims
Mujeeb
Level 1
Sobre este Serviço
Your adjusters are spending 3 days per claim on something a camera and AI can do in 10 seconds. Worse, human assessors miss damage, get argued with, and create liability. AI doesn't.
I build vehicle damage detection systems that analyse photos of any vehicle, classify damage by type, location, and severity, and generate a structured assessment report instantly.
What the system delivers:
- Damage detection: dents, scratches, cracks, structural deformation
- Panel-level location mapping (hood, door, bumper, fender, roof)
- Severity classification (cosmetic/moderate / severe)
- Confidence scores per damage region
- Structured JSON output + PDF assessment report
- REST API for integration into your claims/fleet platform
- Mobile-friendly photo capture workflow (web or app)
- Before/after comparison for pre-existing damage flagging
Built for: motor insurance companies, fleet operators, rental car companies, lease return platforms, used car dealerships, and roadside assistance services.
One photo. 10 seconds. A defensible damage report.
Conheça mais sobre Mujeeb
Mujeeb
AI Chatbot Developer Machine Learning and AI Automation Expert
Level 1
- A partir dePaquistão
- Membro desdejul. de 2024
- Responde em aprox.:3 horas
- Última entrega2 meses
Idiomas
Inglês, Espanhol, Alemão
Hi,
I’m Mujeeb from Pakistan.
I'm an AI Developer specializing in AI Chatbot Development, Machine Learning, Computer Vision, and Agentic AI Solutions. Since 2022, I've been helping startups, businesses, and entrepreneurs build intelligent, scalable, and high-performing AI-powered applications.
I offer custom AI solutions including chatbot development, AI agents, machine learning solutions, computer vision applications, AI-powered web and mobile app development, API integration and automation, bug fixing, performance optimization, deployment & cloud integration.
Message Now.
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Perguntas frequentes
How many images per vehicle does the system need?
For reliable detection, 8–12 photos covering all panels (front, rear, sides, roof, underbody if required). I configure the guided photo workflow to prompt the user through each angle.
How accurate is the damage detection?
On a well-annotated vehicle damage dataset (which I can source and train from): 87–93% mAP for damage detection, 84–90% for severity classification. Full evaluation report included
Can it detect pre-existing damage to prevent fraud?
Yes. The Scale package includes a before/after comparison module that flags damage appearing between two inspection sessions — key for rental and fleet fraud prevention.
What format is the damage report output?
Structured JSON (for API consumers) and a PDF report with annotated vehicle diagram, damage table, severity scores, and timestamp. Both included from the Growth package up.
