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A premium technology retailer needed to offer lease-to-own financing but operated within one of the most restrictive digital environments—no local data storage, strict privacy rules, and a multi-application checkout. Codvo engineered a completely custom, AI-driven integration that works without browser storage, persists data inside the mobile app, and orchestrates secure, multi-application checkout flows using AWS.
The result: a seamless, compliant financing offer delivered in under 250ms, all while maintaining the retailer’s premium UX.
The platform needed a solution that respected all restrictions without altering the retailer’s closed architecture.
Codvo built a custom mobile-application–based integration framework powered by machine learning and AWS serverless services.
1. Real-Time Product Identification (No Local Storage)
2. Mobile App-Based Persistent Storage
3. Multi-Application Checkout Integration
4. AWS ML & Serverless Backend
5. Continuous Learning Pipeline
Full Compliance with Restrictive Data Policies
Solution operates entirely within allowed boundaries—no prohibited storage or transmissions.
Seamless Multi-App Data Flow
Accurate, real-time data synchronization across multiple applications.
Sub-250ms Processing
Maintains the retailer's premium user experience with no visible delays.
High ML Accuracy
Correctly identifies lease-eligible products with minimal false classifications.
Zero Privacy Violations
End-to-end encryption and rigid access controls meet enterprise security requirements.
Premium UX Preserved
No disruption to browsing, cart, or checkout flows.
This solution proves that innovative cloud architecture can overcome even the most restrictive environments. By combining AWS serverless capabilities, machine learning, and mobile-based persistence, the lease-to-own platform successfully integrated with a retailer known for its rigid security, privacy, and UX standards.
It stands as a blueprint for delivering financial services inside closed, premium ecosystems—securely, intelligently, and seamlessly.