Blog · Topic
GEO
21 articles
AI answers in every language your customers speak
The same prompt in French, German and Japanese returns different answers citing different sources. How to monitor AI visibility beyond English markets.
Run your own AI citation study — the honest methodology
Most AI-search statistics online are borrowed or stale. How to measure citation rates, trigger rates and source mixes yourself, with a defensible method.
Does structured data help AI engines cite you?
Schema.org markup, clean HTML and machine-readable pages — what actually helps an AI engine read and cite your content.
AI answers resample — why one check is an anecdote
The same prompt can name you today and a competitor tomorrow. Answer variance is the core measurement problem in AI visibility — and the fix is statistical.
An AI visibility report clients and executives actually read
The structure, metrics and chart set for a monthly AI-visibility report — built from monitor API data, readable by non-SEOs.
Extending an SEO program to AI answers — the migration map
Your keyword lists, rank-tracking cadence and reporting stack all port to answer monitoring. What carries over, what changes shape, what to drop.
GEO metrics vs SEO metrics: what maps and what doesn't
Position, CTR and impressions don't survive the move to AI answers. The metric translation table and what's genuinely new.
Design a prompt set worth monitoring
The prompts you track define the metric. How to build a prompt set that reflects real buyers — clustering, phrasing, and how many you need.
AI answer tracking as an agency deliverable
Clients are asking "do we show up in ChatGPT?" — how agencies can answer that as a service line without building infrastructure.
Monitor what AI answers say about your competitors
Competitive intelligence from answer data — who gets recommended, on which prompts, in which markets, and when it changes.
Why AI engines cite Reddit so much — and what to do about it
Reddit threads appear disproportionately in AI citations. The structural reasons, how to measure it for your prompts, and how to earn the citation anyway.
Mentions vs citations: the two numbers that matter in AI answers
Being named in an answer and being cited as a source are different events with different fixes. How to measure and improve each.
How often do AI answers change? Setting a monitoring cadence
AI answers drift on their own schedule — model updates, fresh sources, prompt phrasing. How to pick a sampling cadence per use case without wasting credits.
AI answers differ by location — here's how to see it
The same ChatGPT prompt returns different answers in Texas and California. Geo-targeted monitoring at country and US-state level, and why local brands need it.
White-label AI visibility data for agencies and platforms
Sell AI-visibility features without building capture infrastructure — one API behind your own dashboards, reports and client portals.
What is generative engine optimization (GEO)?
A working definition of GEO — the discipline of making your brand appear inside AI-generated answers — and how it differs from SEO.
Measure the traffic AI engines actually send you
AI referral traffic is real but noisy — partial attribution, dark social, no query data. How to measure it properly and connect it to answer monitoring.
Build vs buy: AI visibility tracking infrastructure
The real cost of collecting ChatGPT, Perplexity and Google AI answers yourself — sessions, parsing drift, geo coverage — vs paying per call.
AI share of voice: a measurement framework
Concrete metrics for AI visibility — mention rate, citation share, and coverage — with formulas you can compute from monitor API responses.
Query fan-out: the searches ChatGPT runs before it answers
Capture the web searches behind ChatGPT's answers and turn them, across a prompt set, into a list of queries worth winning.
How AI engines choose which sources to cite
What actually drives a citation inside ChatGPT, Perplexity or Gemini answers — the retrieval layer, fan-out queries, and the page traits that get picked.