This is the work in progress: GEO (Generative Engine Optimization) targets presence inside AI answers rather than search rankings — making brand information easy for AI systems like ChatGPT and Perplexity to understand, cite, and recommend.
One platform supports ongoing operations; one tool provides a fast diagnostic.
Break GEO into falsifiable improvement hypotheses — each bound to a project, a monitored question, an AI platform, and a verification criterion.
Visit productScans a website and assesses GEO readiness across content citability, structured data, content depth, brand authority signals, and technical accessibility.
Visit productGEO is not a one-off content project. It is ongoing work from question to answer, answer to action, and back to verification.
From customer decisions to research on how GEO works, connecting questions, evidence, content, publishing, and verification in one workflow.
The next stage of GEO is connecting questions, facts, observations, actions, and retests with stable context, clear boundaries, and verifiable outcomes.
GEO changed my view of brand growth from exposure to reducing misunderstanding through user questions, facts, publishing, monitoring, and retesting.
Rebuilding a brand diagnosis: eight natural questions and three brand questions were scored as one batch of eleven, so correct calculations answered different questions — data ownership was redrawn and report evidence frozen.
A GEO product must define what results mean, preserve evidence, and protect responsibility boundaries so failures can be recovered.
When AI gets a brand wrong, preserve the original answer, establish publishable facts, record human corrections, and observe comparable retests.
Why I removed summary scores, evidence-free generation, premature actions, and automatic attribution from the default path of a GEO product.
A falsifiable GEO opportunity connects monitoring to action by preserving evidence, defining the gap, setting priorities, and agreeing on success first.
AI visibility is not one score. It requires separate evidence for mentions, brand presentation, cited sources, collection context, and human review.
How a browser extension can use an operator's existing login to assist publishing while verifying the account, state, public URL, and uncertain outcomes.
How a GEO article moves through four stages — content brief, human review, publishing, and same-question retesting — from a user question and brand evidence into an ongoing, verifiable workflow.
A knowledge base is not a document dump: uploaded files must be parsed and indexed — processing, searchable, failed, and index-not-ready are four different states — turning brand files into traceable evidence within permission boundaries.
Why GEO should not begin with article generation: questions split into awareness, comparison, and concrete-condition types, and a question library anchors monitoring, content, publishing, and retesting as one workflow.
What "be seen by AI" means, and how GEO distinguishes accuracy, relevance, sources, and user choice.
Turning “earn a place in AI answers” into a first product definition: monitoring, generation, knowledge base, publishing, and reports all sound right, but the product must decide what not to do and test the boundary.
A first GEO experiment you can finish in a day: pick 1 real user question, record first answers on 2–3 AI platforms, classify them as absent, inaccurate, relevant, or sourced, change exactly one variable, and retest — about 1–2 hours end to end.
Why I started building GEO: as users hand “which options are worth considering” straight to AI, being seen is only the first step toward being understood, cited, and recommended correctly.
GEO and SEO differ by one letter, but SEO targets rankings while GEO targets presence inside AI answers. The real problem is brand understanding and feedback: being seen is the start; described correctly, cited, and recommended closes the loop.