This retail grocer's data science team sits on a goldmine: 10 petabytes of customer data spanning purchase history, browsing behavior, seasonal patterns, and more. The challenge wasn't having data—it was making it useful in the split second between a customer opening the app and deciding whether to engage.

Real personalization at scale is a latency problem. You have thousands of customer attributes, millions of possible offers, and about 200 milliseconds before the customer loses interest. Most companies solve this by pre-computing everything, but that means your recommendations are always slightly stale.

We helped optimize the ML pipeline for real-time inference—making predictions on the fly rather than relying on batch processing. The result: genuinely personalized offers that reflect what a customer did five minutes ago, not five days ago.