Build a Semantic Layer So AI Defines Your Metrics

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Aug 12, 2026

5:00 PM UTC (1 hour)

Virtual (Zoom)

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Automate AI Evals with Claude Code
Shane Butler
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What you'll learn

Define joins, metrics, and terms once

Write down what "active user" and "retention" mean, plus how the tables join, in one place the AI reads every time.

Make every AI answer use the same logic

Point the agent at your semantic layer so it computes the same number the same way, no matter who asks.

Stop arguing about whose number is right

Kill the "my dashboard says 40, yours says 32" fights by giving every query one shared source of meaning.

Keep it current as the data changes

Update a definition in one file and every future AI answer picks it up, no rewriting prompts or dashboards.

Why this topic matters

Everyone on your team defines retention differently, and AI makes that worse. Ask three people, or three chat sessions, and you get three numbers, all confidently wrong. The fix is one place that says what each metric means and how tables connect: a semantic layer. Once it exists, AI stops guessing and computes your metrics your way, every time. This session builds a starter one live.

You'll learn from

Shane Butler

Co-founder, AI Analyst Lab

Shane Butler is a Co-Founder of the AI Analyst Lab. Previously he led evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Sravya Madipalli

Senior DS Leader (Ex-Microsoft)

Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.

Hai Guan

Head of Data at Ontra, Ex-LinkedIn

Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.

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