Validate Claude Code Analytics Output

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, May 13, 2026

7:00 PM UTC (1 hour)

Virtual (Zoom)

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320 students

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Build AI Analysts in Claude Code
Shane Butler, Sravya Madipalli, and Hai Guan
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What you'll learn

Run a 4-layer validation on any AI analysis

Check structure, logic, business rules, and segment-level reversals before trusting the output.

Define evidence that supports a ship decision

For each step, specify what evidence would justify ship, iterate, or stop, and what “evidence” is misleading.

Catch errors AI analysis gets wrong

Learn the specific failure modes where AI confidently produces wrong answers and how to spot them.

Re-derive findings to confirm correctness

Use Claude Code to approach the same question a different way to measure both capability and reliability

Why this topic matters

AI analysis tools are confident even when they are wrong. Most people eyeball the output and move on. This lesson teaches a 4-layer validation stack so you can catch structural errors, logic mistakes, violated business rules, and hidden segment reversals before the wrong number hits a stakeholder deck.

You'll learn from

Shane Butler

Principal Data Scientist at Ontra

Shane Butler is a Principal Data Scientist at Ontra, where he leads 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.

Previously at Stripe, Nextdoor, PwC

Stripe
Nextdoor
Ontra
PwC India
AppFolio

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