Blog · Topic
Fundamentals
16 articles
Sampling vs. census in AI-answer monitoring
How to decide how many runs per prompt are enough — the statistics of collecting AI answers without paying for noise.
Estimating your AI-answer monitoring bill before you spend a credit
A practical model for sizing prompt sets, engine mixes and cadence so you can predict monthly credit usage before your first run.
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.
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.
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.
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.
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.
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.
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.
Sync calls, async tasks or webhooks: how to call an answer API
When to wait for an answer, when to queue a task and poll, and when to let results come to you by webhook.
How to track what AI assistants say about your brand
A practical setup for measuring brand mentions and citations in ChatGPT answers, market by market.