CASE STUDY MOBILITY Retail & eCommerce

ChooseApp: AI-Powered Mobile Platform for Enhanced User Engagement

Intelligent mobile application with AI-driven recommendations and real-time analytics for enhanced user engagement.

ChooseApp Inc.

Category MOBILITY

Industry Retail & eCommerce

The Challenge

ChooseApp, an emerging lifestyle and shopping platform, was struggling with low user retention and engagement. Their existing mobile app offered a static, one-size-fits-all experience that failed to cater to individual user preferences.

Key challenges included:

Low user retention — Over 60% of users dropped off within the first week due to irrelevant content and recommendations.
No personalization — The platform served the same generic content to all users regardless of their browsing history or preferences.
Poor performance — The legacy app suffered from slow load times and frequent crashes on mid-range Android devices, leading to negative reviews.
Limited analytics — The team had no real-time visibility into user behavior, making it impossible to identify drop-off points or optimize the user journey.
Fragmented codebase — Separate native apps for iOS and Android resulted in duplicated effort and inconsistent feature rollouts.

Our Solution

AaiNova partnered with ChooseApp to completely reimagine their mobile platform, building an AI-first application that delivers hyper-personalized experiences at scale.

1. Cross-Platform Rebuild with Flutter

We rebuilt the app from the ground up using Flutter, unifying the iOS and Android codebases into a single performant application. This reduced development time by 40% and ensured feature parity across platforms.

2. AI-Powered Recommendation Engine

Using TensorFlow and custom ML models, we built an intelligent recommendation engine that analyzes user behavior in real-time — browsing patterns, purchase history, time-of-day preferences, and location data — to deliver personalized product suggestions, content, and deals.

3. Real-Time Analytics Dashboard

We implemented a comprehensive analytics layer using Firebase Analytics and custom event tracking, giving the ChooseApp team real-time visibility into user funnels, engagement metrics, session duration, and conversion rates.

4. Performance Optimization

Through lazy loading, image optimization, Redis caching, and CDN integration, we reduced average app load time from 4.2 seconds to under 1.5 seconds. Crash rates dropped by 95%.

5. Intuitive UX Redesign

Our UX team conducted extensive user research and A/B testing to redesign the entire user journey — from onboarding to checkout — resulting in a cleaner, more intuitive interface that guides users toward conversion.

The Results

The reimagined ChooseApp platform delivered transformative results within the first 6 months of launch:

3.5x increase in user retention — Weekly active users grew from 12% to 42% within 90 days of the relaunch.
68% improvement in engagement — Average session duration increased from 2.1 minutes to 3.5 minutes, with users viewing 2.4x more products per session.
45% boost in conversion rate — AI-driven recommendations led to a significant increase in purchase completions, with the recommendation engine contributing to 35% of all sales.
95% reduction in crash rate — App stability improved dramatically, resulting in the store rating climbing from 3.2 to 4.7 stars.
40% faster development cycles — The unified Flutter codebase enabled the team to ship new features simultaneously on both platforms, cutting release cycles nearly in half.
Real-time decision making — The analytics dashboard empowered the product team to identify and fix drop-off points within hours, not weeks.

Technical Architecture

The ChooseApp platform was built on a modern, scalable microservices architecture designed to handle rapid user growth while maintaining sub-second response times. Here's a closer look at the technology stack and how each component fits together.

Frontend — Flutter & Dart

The mobile application was built using Flutter, leveraging its widget-based architecture for pixel-perfect UI across iOS and Android from a single codebase. We implemented custom animations and transitions using Flutter's Skia rendering engine to ensure a smooth, native-like feel. Key frontend patterns included BLoC (Business Logic Component) for state management and a modular feature-first folder structure to support rapid iteration.

Backend — Node.js & Python

The API layer was built with Node.js using Express, optimized for high-throughput I/O operations such as real-time feed generation and push notifications. Python-based microservices powered the machine learning pipeline — handling data preprocessing, model training, and inference for the recommendation engine. Communication between services was managed via message queues using AWS SQS for asynchronous task processing.

AI/ML Pipeline — TensorFlow

The recommendation engine uses a hybrid collaborative filtering and content-based approach. User interaction data (views, clicks, purchases, time spent) is fed into TensorFlow models that generate personalized product rankings in real time. The ML pipeline runs on AWS SageMaker, with models retrained weekly on the latest user behavior data to continuously improve accuracy.

Data Layer — MongoDB & Redis

MongoDB serves as the primary data store, chosen for its flexible schema that accommodates evolving product catalogs and user profiles. Redis handles session management, caching of frequently accessed data (trending products, user preferences), and real-time leaderboards. This dual-database approach reduced average API response times to under 80ms.

Cloud Infrastructure — AWS

The entire platform runs on AWS, using ECS (Elastic Container Service) for container orchestration, CloudFront as a CDN for static assets and images, and RDS for transactional data. Auto-scaling groups ensure the platform handles traffic spikes — including a 10x surge during a promotional campaign — without degradation.

Implementation Approach

AaiNova followed an agile delivery model with 2-week sprints and continuous deployment. The project was executed in three phases:

Phase 1: Foundation (Weeks 1–6) — Core app rebuild with Flutter, backend API setup, database migration from the legacy system, and CI/CD pipeline configuration using GitHub Actions and AWS CodePipeline.

Phase 2: Intelligence (Weeks 7–12) — AI recommendation engine development, Firebase Analytics integration, A/B testing framework setup, and real-time analytics dashboard build using custom admin tools.

Phase 3: Optimization (Weeks 13–16) — Performance tuning, load testing (simulating 100K concurrent users), security audit, UX refinements based on beta feedback, and staged rollout to production.

Key Takeaways

This project reinforced several principles that drive our approach at AaiNova:

Personalization drives retention. Users who received AI-tailored recommendations were 3.2x more likely to return within 7 days compared to those who didn't.
Performance is a feature. Reducing load time from 4.2s to 1.5s had a direct, measurable impact on conversion — every 100ms improvement correlated with a 1.2% increase in completed purchases.
Unified codebases accelerate delivery. Moving from separate native apps to Flutter eliminated an entire class of bugs related to platform inconsistencies and halved the QA effort.
Data-informed decisions compound. With the real-time analytics dashboard, the ChooseApp team identified and fixed a checkout flow issue within 48 hours of launch — a problem that would have gone undetected for weeks under the old system.

"AaiNova didn't just build us an app — they transformed how we think about our product. The AI recommendations and real-time analytics have fundamentally changed our ability to serve customers. Our retention numbers speak for themselves."

— Product Lead, ChooseApp Inc.