Test Kimi and Open Models on Real Analytics Work

Part of Build Your AI Product Analyst

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Aug 12, 2026

7:00 PM UTC (30 minutes)

Virtual (Zoom)

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Agentic Analytics: Build an AI Analyst You Can Trust
Shane Butler, Sravya Madipalli, and Hai Guan
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What you'll learn

Understand what open models are and why they matter

The open-model landscape in plain terms: the families, how you run them, and why the price gap changes the math.

See if open models can power analytics work reliably

Our scorecard from real analytics tasks, open models vs Claude, shows where the threshold sits today.

Swap an open model into your workflow live

Watch us swap Kimi into a working analyst setup in Claude Code and run a real analysis on the spot.

Know when an open model is the right call

The cost math, privacy, and the judgment call: when open models make sense for your analytics work and when they do not

Why this topic matters

The AI model behind your AI analyst is a decision most builders never make on purpose. They default to the biggest name and pay for it on every run. Open models now do analyst-grade work at a fraction of the price and needed privacy, but nobody shows you a fair test on real work. This session is that test: same tasks, head to head, graded on quality, cost, and speed, so you pick with evidence.

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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