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Modernization and Scalable Customization of Telematics Applications for Enterprise Clients

altoroslabs.com

Insurance

Automotive

Transportation & Logistics

Challenges in Customization and Technical Debt Management

The client's apps suffer from tightly coupled architectures, making it difficult to implement business-specific customizations without risking system instability. Legacy codebases (e.g., Objective-C) and fragmented workflows across 50+ enterprise clients create operational inefficiencies, technical debt, and scalability limitations.

About the Client

A leading provider of IoT and AI-driven telematics solutions that improve road safety and driver behavior for insurers, ridesharing platforms, and fleet management companies.

Key Goals for System Modernization

Enable zero-downtime updates to core functionality while maintaining business-specific customizations
Modernize legacy iOS applications through migration to Swift
Implement a scalable architecture for cross-industry feature interoperability
Reduce technical debt through standardized development practices

Core System Capabilities

Configurable SDK for enterprise-specific workflows
Cross-platform mobile applications (iOS/Android)
Real-time crash detection and claims automation
Driver scoring system based on telematics data
Multi-layered data visualization dashboards

Technology Stack Requirements

Swift (iOS)

Kotlin (Android)

MVVM architectural pattern

Automated CI/CD pipelines

Code linting and review tools

System Integration Needs

IoT telematics devices
Third-party insurance claims systems
Enterprise fleet management platforms

Operational Requirements

Zero-downtime deployment capabilities
99.99% system availability SLA
Cross-platform compatibility testing
Scalable architecture for 20M+ drivers
Security compliance with GDPR and ISO 27001

Expected Business Outcomes

Successful implementation will enable 50+ enterprise clients to adopt new features with 80% faster deployment cycles, support 5M+ app installations, and maintain 99.99% uptime during critical updates. The modernized architecture is projected to reduce maintenance costs by 40% while enabling rapid innovation in driver safety analytics.