Pressure-test any AI analysis

Part of The AI Evaluation Handbook

Hosted by Shane Butler

Wed, Jun 24, 2026

3:00 PM UTC (1 hour)

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Build Agentic Analytics 101 in Claude Code
Shane Butler, Sravya Madipalli, and Hai Guan
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What you'll learn

Find or forge a reference to check an AI analysis against

The spectrum, and which fits your company

Handle the novel question that has no answer key

With checks you can run on any output

Know what each check actually catches

And where it falsely passes

Join AI Builders Slack Community. Link: bit.ly/ai-connect

Learn Agentic Analytics workflows and AI Eval design. Get feedback on what you’re building from other AI builders.

Decide when to act, when to dig,

And when to abstain

Why this topic matters

AI analytics tools are confident even when they're wrong, and "is this right?" is the question they all raise. Knowing means checking against ground truth. Sometimes you have a reference (there's a spectrum of ways to get one, and which fits depends on your company), and sometimes you genuinely don't (the novel question, which is exactly where AI is most useful). This lesson covers both.

You'll learn from

Shane Butler

Principal Data Scientist, AI Evaluations at Ontra

Shane Butler is a Co-Founder of AI Analyst Lab. He previously 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. His current work focuses on practical, end-to-end methods for evaluating AI features in production. 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.

Previously at Stripe, Nextdoor, PwC

Stripe
Nextdoor
PwC India
Ontra
AppFolio
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