Most valuable data for LLM context: usage and calls

Adrian states product usage data and customer calls are the two richest context sources for LLM-driven GTM work.

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Adrian Rosenkranz, CRO at Webflow, joins Kyle Norton to break down exactly how to rebuild your GTM motion for AI bot traffic and scale internal AI agents.

As AI bots reshape enterprise pipeline generation, revenue teams must master AEO (Agentic Engine Optimization) and GEO (Generative Engine Optimization) to capture high-intent search traffic. Adrian shares how to optimize your technical SEO for agent crawls, collapse legacy RevOps silos, and build custom internal AI workflows that drive real sales outcomes.

A lil' sneak peek into some key topics we discuss:

Optimize for AEO and GEO: AI bot crawls now make up nearly 20% of Webflow's site traffic. Companies must provide markdown-friendly metadata to control their brand narrative inside LLMs and secure citations over standard referrals.
Pioneer Go-To-Market Engineering: Fragmented operations teams create workflow bottlenecks. Merging Sales, Marketing, and Post-Sales Ops into a single go-to-market engineering (GTME) unit is required to deploy effective cross-functional AI systems.
Deploy Agents with Claude Code: Stop using AI just to write faster emails. Centralize your AI development to build autonomous workflows—like using Claude Code and MCP to draft hyper-personalized customer emails based on live Slack and Salesforce context.
Build Dynamic Knowledge Bases: Static documentation is dead. Feed call data into AI to automate your Ideal Customer Profile (ICP) and "...run this every week... and you can see the drift from the previous one" to continuously fuel outbound scripts.

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Chapters:

00:00 Intro and AI bot traffic surge

05:12 AI citations vs website referrals

08:28 Optimizing websites for AEO & GEO

13:14 Controlling brand narratives in LLMs

15:05 Enable sales for rapid product updates