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

In this episode, we discuss Expedia’s need to evaluate many ranking ideas quickly so that A/B testing would not become a bottleneck. We explore their three-stage experimentation funnel: backtesting to remove weak candidates, interleaving to rapidly compare promising ones, and A/B testing to validate final business impact. What made this design approach effective was using the right evaluation tool at the right step instead of forcing A/B testing to do everything.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/expedia-group-tech/interleaving-for-accelerated-testing-75adc644027b

In this episode, we look at how companies deal with large volumes of unstructured text and why traditional clustering methods often fall short at scale. We explore two LLM-powered approaches shared by data scientists from Microsoft: a bottom-up pipeline that builds structure from data using embeddings and clustering, and a top-down pipeline that starts with LLM-generated categories and refines them recursively into a hierarchy.
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/data-science-at-microsoft/from-chaos-to-clarity-building-taxonomies-from-unstructured-text-using-large-language-models-c1303db3adb1

In this episode, we discuss a classic scaling problem in fraud and risk operations: too much manual review, inconsistent judgments, and growing complexity. We explore the team’s solution, Bumblebee, a multi-agent AI architecture that separates planning, evidence gathering, and analysis into specialized roles, enabling a robust and scalable system to solve the problem.
For more details, you can refer to their published tech blog, linked here for your reference: https://engineering.razorpay.com/meet-bumblebee-the-multi-agent-ai-architecture-that-changed-fraud-detection-at-razorpay-c2b6d5704f51

In this episode, we explore how OLX improved discovery by combining keyword search and vector search instead of forcing a choice between the two. Keyword systems remain excellent for precision, while vector systems add semantic understanding. Together, they create a smarter and more user-friendly marketplace experience.
For more details, you can refer to their published tech blog, linked here for your reference: https://tech.olx.com/hybrid-search-where-keywords-meet-vectors-enabling-classifieds-discovery-b7c383fe4fc4

In this episode, we explore how Udemy built a multilingual AI platform to bring its generative AI features to learners around the world. The team approached localization across three levels: a translation-first approach for broad and fast coverage, a fully native multilingual system for markets where fluency and cultural precision are essential, and a hybrid solution in between that intelligently routes between the two depending on the situation
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/udemy-engineering/from-zero-to-hero-localization-led-generative-ai-at-udemy-a422e4f968d4