Turning Data into Velocity: Caper’s Edge and Cloud Data Flywheel with Capsight
Key Contributors: Youming Luo, Andrew Tanner, Matas Sriubiskis, Sylvia Lin, Sikun Zhu, Lei Li, Xiao Zhou Introduction Caper is Instacart’s AI-powered smart cart that provides customers with a fast, seamless, and intuitive shopping experience. We achieve this through computer vision and multi-sensor fusion to power accurate product recognition and effortless checkout. Delivering this experience requires Caper’s AI models to understand what truly happens in stores — the movement, intention, and decisions unfolding across every grocery aisle. Historically, our ability to learn from production environments was limited. Even though the carts were deployed in stores, we lacked a scalable way to collect real‑world data that would allow us to rapidly iterate and improve our models. This resulted in three core challenges: Scalable Onboard Observability : We had little visibility into what was happening on the cart, in the stores. When something went wrong, it was hard to understand or reproduce the scenario. At the same time, each cart generates gigabytes of multimodal data, from sources such as cameras, weight sensors, and localization sensors. We needed a centralized way to capture key moments so the team could clearly understand what the cart experiences, how users interact with it, and where to improve — all while maintaining a magical user experience and minimal impact on the network. Data Quality and Diversity: Our models were primarily trained on manually-collected data that d…
