Interaction Patterns as Ground Truth

LLMs allow us to process public interaction signals at scale:

• What people engage with

• Who they follow

• Which conversations they participate in

• How they react to competitors

• What topics consistently draw their attention

These signals are far more revealing than titles because they show intent in motion. And with reasoning LLMs, we can interpret what these patterns mean, not just aggregate them.

This led us to build interaction clusters inside accounts - behavioral groupings that surface the people leaning into new functions long before org structures acknowledge it.

Finding the Real Operators Behind GEO and AEO

For GEO and AI-SEO customers, this changed everything.

When an account showed GEO activity, the system could immediately identify the individuals already operating in that motion — often months before their title caught up.

LLMs removed the dependency on job labels entirely.

We could now see:

• Who needed the solution

• Who cared about the domain

• Who was already shaping the function internally

This allowed us to map emerging operators across the ecosystem, revealing the early adopters pushing GEO/AEO inside larger organizations.

Operators Actively Shaping GEO and AEO

Reasoning over interaction clusters surfaced the following leaders who consistently drive thinking around GEO, AEO, and AI-driven discovery:

• Tim Manalo (ServiceNow) – Exploring AI discovery patterns and the evolution of AIO-driven SEO.

• Kyle Swinderman (AWS) – Engaging deeply with AEO and GEO as AWS evolves toward AI-native search.

• Ali Mercieca (Ramp) – Leaning into AI-first SEO and generative visibility models.

• Jeremy Liang (Rho) – Merging technical SEO with LLM-aware optimization patterns.

• Sarah Kay (JPMorgan Chase) – Active contributor in GEO and AI-search performance conversations.

• Lavall Chichester (BarkleyOKRP) – Applying GEO frameworks across SGE, ChatGPT, and Perplexity ecosystems.

• Tim Clarke (Later) – Driving AI-powered discovery and content workflows.

These individuals reflect how new functions form — not through titles, but through behavior, participation, and signal density.

LLMs Reveal Who Actually Runs Emerging Functions

LLMs made one thing clear:
The real operators show up in patterns, not job descriptions.

By reasoning over interactions instead of relying on outdated labels, GTM systems can now identify the people shaping new categories the moment those categories appear.

Reasoning LLMs surface the real operators inside accounts by analyzing behavior, not titles — enabling accurate ICP discovery in emerging functions like GEO and AEO.