Modernizing a B2B Mobile Platform for Vehicle Damage AssessmentModernizing a B2B Mobile Platform for Vehicle Damage Assessment
Improved overall app stability and performance through architectural refactoring
Increased test coverage from 1% to 55%, enabling safer releases and easier refactoring
Enhanced developer experience, making onboarding and feature delivery more efficient
About the Project
To make damage inspections easier and speed up claims processing, we helped modernize a mobile app used across the U.S. by both insurance agents and individual users. The app allows users to collect and process all necessary data, from vehicle photos and geolocation to VIN scans and odometer readings, all powered by advanced recognition technologies.
We worked on a full renovation of the app’s architecture, UI, and network layer to improve performance, stability, and long-term maintainability. As a result, the overall experience for both developers and app users became faster and more reliable.
Business Challenge
The mobile platform, originally developed years ago, began to struggle with outdated architecture and limited scalability. As the system grew to support more insurance providers and use cases, maintaining performance and stability became increasingly difficult:
- Legacy architecture and deprecated components in the network and UI layers made updates time-consuming and risky
- Tight integration with ML-based features like VIN scanning and OCR required consistent performance across a wide range of devices and environments
- Lack of test automation and low code coverage made refactoring difficult and reduced confidence in production releases
Contributions
As part of the client’s engineering team,we helped modernize and stabilize the Android app through a series of long-term improvements:
Modernized the app’s core foundation by fully renovating its architecture, networking, and UI layers by adopting best practices to achieve a cleaner, more modern codebase and faster development and maintenance.
Integrated ML Kit-powered features like VIN scanning and OCR to enhance the vehicle inspection flow.
Improved testing practices and code quality by growing unit test coverage from 1% to 55% over time.
Actively contributed to CI/CD adoption and code review processes, improving release consistency and team velocity.
Key ExpertiseThe project showcases our ability to modernize large-scale mobile systems while working within complex, production environments:
Expertise in refactoring legacy Android apps without disrupting production stability
Deep familiarity with ML-powered workflows like VIN scanning and OCR via ML Kit
Strong ownership of architecture, testability, and maintainability improvements
Proven success operating as part of a large cross-functional team
Case Studies
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Real Results
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