Product
GEO Monitoring & Strategy
Building a Fortune 500 semiconductor brand's first generative engine optimization system — from zero to a full monitoring and strategy framework.
Role
GEO Consultant
Timeline
Feb 2026 – Present
Company
Fortune 500 Semiconductor · via Teleworker
Platforms
ChatGPT · Perplexity · Google AIO
TL;DR
0→1
Defined what GEO means for a Fortune 500 semiconductor brand and built the measurement system from scratch
3
AI platforms monitored — ChatGPT, Perplexity, Google AI Overviews
New
No established playbook. Every framework, metric, and prioritization rule had to be invented.
Context
A new discipline with no established rules
As AI-powered search reshapes how brands get discovered, a new question emerged: when someone asks ChatGPT "which DDR6 memory brand is good?" or "what are the key semiconductor investments in Japan?" — what answer do they get, and is the brand in it?
GEO (Generative Engine Optimization) is the discipline of ensuring your brand is accurately represented, cited, and contextually positioned in LLM-generated answers. Unlike SEO, there's no established standard, no universal metric, no agreed-upon playbook.
I joined as a GEO consultant at Teleworker, embedded within a Fortune 500 semiconductor company's communications team to build their GEO strategy from the ground up.
2027
Semrush predicts LLM traffic will surpass Google
25%↓
Traditional search volume projected to decline (Gartner)
60%
Of search behaviors have evolved into Zero-Click journeys
GEO vs SEO
Old SEO
- Keywords
- Blue Link Ranking
- Traffic
- Backlinks
New GEO
- Entities & Intent
- Citations & Mentions
- Share of Voice
- Info Gain & E-E-A-T
The Core Problem
What does "being visible in AI" even mean?
Before I could build anything, I had to answer a harder question: what does "being visible in AI" mean for a semiconductor brand with multiple audiences, products, and regional markets?
The client isn't one brand story. It's a technology company (AI Memory, HBM, DDR6), an employer (hiring across Asia-Pacific), a policy actor (local investments, government engagement), and an ecosystem partner (supply chain). Each surfaces in AI-generated answers through completely different prompts, asked by completely different people.
The problem wasn't just visibility.
It was that visibility meant different things depending on who was asking — and there was no framework to think about it systematically.
My Approach
Three decisions that shaped the work
Structure the prompt landscape before optimizing anything
Signal
When I mapped out the types of questions people ask in AI search, a clear pattern emerged — some prompts are inherently global (technology and product comparisons), while others are deeply local (workplace culture, government investments, hiring expectations).
Bet
Treating all prompts the same would be a mistake. A globally consistent message about the company's technology is right. A globally consistent message about it as an employer is wrong — what a hiring candidate in Taiwan cares about is fundamentally different from what a policy audience in Japan cares about.
Decision
Build a two-tier prompt taxonomy: Technology (Global) for product and competitive prompts requiring consistent, authoritative responses — and Brand Perception (Localized by Region) for workplace, policy, and brand awareness prompts requiring market-specific narratives.
Fix absence before optimizing presence
Signal
In a new field with no baseline, the instinct is to start building — create content, run experiments, ship. But the first question should be: does the brand even show up? And when it does, is the information accurate?
Bet
Misinformation and brand absence are more damaging than low ranking. If AI generates a wrong answer about the company's technology or misrepresents its workplace culture, that's actively harmful — not just a missed opportunity.
Decision
Establish a prioritization rule: Fix misinformation and missing brand presence first → then improve citation coverage → then build differentiation and authority. This sequencing prevented the team from optimizing the wrong things at the wrong time.
Design the measurement system before picking the tools
Signal
GEO metrics aren't standardized. Different platforms define 'visibility' differently. The temptation is to pick a tool and use whatever metrics it provides.
Bet
If you design your measurement system around tool capabilities, you end up measuring what's easy to track, not what actually matters. The measurement framework should come from the strategy, not from the dashboard.
Decision
Define a two-tier metric structure — Primary metrics (Brand Mentions, Citations, Cited Pages, LLM Traffic) tracked consistently every month, and Secondary metrics (Share of Voice, Answer Rank, Sentiment, Distribution by LLMs) used diagnostically to explain primary metric changes.
Primary Metrics
- —Brand Mentions
- —Citations
- —Cited Pages
- —LLM Traffic
Secondary Metrics
- —Share of Voice
- —Answer Rank
- —Sentiment
- —Distribution by LLMs
- —Mentions by Countries
- —AI Visibility Score
What I Delivered
From strategy to execution infrastructure
GEO Strategy Roadmap
Full strategy framework covering AI search landscape, GEO vs SEO positioning, prompt taxonomy, measurement system, and 3-month execution plan
Prompt Framework
Structured two-tier taxonomy with sample prompts by category and market — Technology (Global) and Brand Perception (Localized by Region)
Monitoring Infrastructure
Core Prompt Set (fixed baseline for trend comparison) + Exploratory Prompt Set (20% monthly rolling update to track emerging topics) across ChatGPT, Perplexity, and Google AI Overviews
Content Execution Plan
Localized content strategy targeting key audience segments: supply chain partners, hiring candidates, and policy ecosystem audiences
Monthly Reporting Framework
Standardized report structure enabling Before / Action / After comparison across platforms
Learnings
What building in a nascent field taught me
GEO is a strategy problem before it's a content problem
The temptation in a new field is to start doing — create content, run experiments, ship. But without a clear framework for what you're optimizing and for whom, execution becomes noise. The most valuable thing I did wasn't any single deliverable. It was defining the prompt taxonomy and measurement structure that made all subsequent decisions coherent.
'Localize' means more than translate
A globally consistent brand message breaks down at the prompt level. The questions a hiring candidate in Taiwan asks are structurally different from the questions a policy audience in Japan asks. Localization in GEO isn't about language — it's about understanding what each audience is actually trying to find out, and making sure the right answer exists where AI will find it.
In nascent fields, the measurement framework is the strategy
Unlike SEO, GEO metrics aren't standardized. There's no Google Analytics for LLM citations. Building a meaningful monitoring system required deciding what matters before knowing what's measurable — and being comfortable operating on judgment when dashboards don't yet exist. That discomfort is the job.
Next Case
Kado+ Subscription →