“Vibe analysis”: How Faire’s data team uses AI to investigate conversion drops, analyze experiment results, and convert raw data into executive-ready insights

How I AI

Tim Trueman and Alexa Cerf from Faire’s data team demonstrate how AI tools are revolutionizing data analysis workflows. They show how data teams, product managers, and engineers can use tools like Cursor, ChatGPT, and custom agents to investigate business metrics, analyze experiment results, and extract insights from user surveys—all while dramatically reducing the time and technical expertise required.

What you’ll learn:

1. How to use AI to investigate sudden drops in business metrics by searching documentation and codebases

2. Techniques for creating a semantic layer that helps AI understand your business data

3. How to build end-to-end analytics workflows using Cursor and Model Context Protocols (MCPs)

4. Ways to automate experiment analysis and create standardized reports

5. How AI can help design and analyze customer surveys

6. Strategies for creating executive-ready documents from raw data analysis

7. Why every team member should have access to code repositories—not just engineers

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Brought to you by:

Zapier—The most connected AI orchestration platform

Brex—The intelligent finance platform built for founders

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Where to find Tim Trueman:

LinkedIn: https://www.linkedin.com/in/tim-trueman-99788592/

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Where to find Alexa Cerf:

LinkedIn: https://www.linkedin.com/in/alexandra-cerf/

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Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

LinkedIn: https://www.linkedin.com/in/clairevo/

X: https://x.com/clairevo

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In this episode, we cover:

(00:00) Introduction to Tim and Alexa from Faire

(02:53) The challenge of analyzing product quality and usage

(04:14) Breaking down what analytics actually involves beyond data manipulation

(05:46) Demo: Investigating a conversion rate drop using enterprise AI search

(09:05) Using ChatGPT Deep Research to analyze code changes

(12:40) Leveraging Cursor as the ultimate context engine for code analysis

(18:55) Analyzing a new product feature’s performance with Cursor

(26:27) How semantic layers make AI tools more effective for data analysis

(30:00) Using Model Context Protocols (MCPs) to connect AI with data tools

(34:17) Creating visualizations and dashboards with Mode integration

(37:04) Generating structured analysis documents with Notion integration

(44:39) Building custom agents to automate experiment result documentation

(53:10) Designing and analyzing customer surveys

(59:40) Lightning round and final thoughts

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Tools referenced:

• Cursor: https://cursor.com/

• ChatGPT: https://chat.openai.com/

• Notion: https://www.notion.so/

• Snowflake: https://www.snowflake.com/

• Mode: https://mode.com

• Qualtrics: https://www.qualtrics.com/

• GitHub: https://github.com/

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Other references:

• Model Context Protocol (MCP): https://www.anthropic.com/news/model-context-protocol

• Faire Careers: https://www.faire.com/careers

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