Developed a full-stack AI-powered vehicle report platform that combines image analysis, VIN history, and market valuation. Used OpenAI APIs for condition scoring and risk analysis, React for frontend dashboards, and Supabase for backend data management. The system generates dynamic PDF reports with valuation, grade, risk level, and AI-summarized vehicle intelligence.
Lucas Will
May 2, 2025
AI-Powered PDF Generation Using Python Automation
I built the entire Chariot report generation engine in Python, using libraries like ReportLab and FPDF for PDF layout and rendering. The system dynamically pulls VIN history, AI-generated text summaries, and image grading results, then formats them into multi-section reports with tables, charts, and structured insights. This Python-based workflow ensures fast, automated report creation at scale.
Modular, Scalable, and API-Driven Architecture
The backend architecture is fully modular, making it easy to adjust layouts, add new data sources, or generate different report tiers (Quick Summary vs Full Report). The engine integrates with multiple APIs, including OpenAI for language processing and VIN decoding services for external data. I also built internal logic for error handling, rate limiting, and multi-threaded PDF generation for bulk dealer use cases.
This project highlights my ability to build scalable, API-integrated Python automation tools that generate professional, data-rich PDF outputs for real-world business use.
