We began with extensive stakeholder interviews across product teams, compliance officers, and customer service representatives to understand the full ecosystem. We analyzed 18 months of historical transaction data, application patterns, and customer journey analytics to identify key behavioral signals.

Rather than relying solely on deep learning, we developed a hybrid system combining: Collaborative filtering for pattern recognition across similar user segments, Content-based filtering using product features and customer profiles, Deep neural networks for complex feature interactions and non-linear relationships, and Multi-armed bandit algorithms for exploration-exploitation balance.

We implemented SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-agnostic Explanations) to provide human-readable explanations for every recommendation, showing customers exactly which factors influenced each suggestion.

We deployed AERO in phases—starting with a shadow mode where recommendations were generated but not shown, allowing us to validate accuracy before going live. This de-risked the implementation and built stakeholder confidence.

We established automated retraining pipelines with A/B testing frameworks, allowing the model to learn from real-world outcomes and continuously improve without manual intervention.

We built a scalable AI tech stack using modern machine learning frameworks, data pipelines, and cloud infrastructure. The stack includes model training, feature engineering, real-time inference APIs, and monitoring tools, ensuring high performance, reliability, and seamless integration with production systems.