In this episode, we explore how Meta addressed a fundamental limitation of applying LLMs to enterprise analytics. While modern models are highly capable of generating code and SQL, they still fall short when it comes to organizational context and deep domain understanding — both of which are essential for reliable, real-world analytical work. Meta’s approach focuses on closing this gap through shared memory systems, iterative reasoning loops, transparent execution, and a layered organizational knowledge framework built around Cookbooks, Recipes, and Ingredients.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/@AnalyticsAtMeta/inside-metas-home-grown-ai-analytics-agent-4ea6779acfb3

In this episode, we explore how CVS Health tackled a classic large-scale engineering operations problem: keeping application runbooks accurate, complete, and continuously compliant across hundreds of internal systems. To solve this, the team built an LLM-based automated auditing pipeline. The result is a lightweight but effective system that turns documentation compliance from a periodic manual effort into a continuous and scalable operational workflow.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/cvs-health-tech-blog/automated-documentation-auditing-leveraging-llms-for-compliance-verification-13fa80b90912

In this episode, we explore how Uber tackled one of the most operationally challenging parts of its marketplace: airport pickups. Unlike normal city rides, airport demand is highly bursty, queue-driven, and heavily influenced by flight schedules, delays, and driver positioning decisions. To solve this, Uber built a coordinated forecasting system composed of three specialized models: Estimated Time to Request (ETR) to predict queue wait times, Earnings Per Hour (EPH) to estimate airport profitability versus city driving, and Driver Deficit Forecasting to proactively reposition supply before shortages occur. This allows the platform to reduce uncertainty, improve driver behavior, and stabilize airport marketplace dynamics in real time.

For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/forecasting-models-to-improve-availability-at-airports