➜ ./webnuvexa --geo
Probing generative engines▊
Querying ChatGPT + Perplexity + AI Overviews + Gemini...
✓ 240 brand prompts benchmarked
✓ Citation gap analysis complete
✓ Structured data validated
Ready to be cited▊
// AI Search Optimization / GEO
Generative Engine Optimization for ChatGPT, Perplexity, Gemini, and AI Overviews. Entity authority, machine-readable structure, and answer-shaped content built to be quoted.
The technical and editorial layer that makes AI engines cite you instead of your competitors.
We query 200+ buyer prompts across ChatGPT, Perplexity, Gemini, and Copilot to measure where you are mentioned, where you are absent, and who gets cited instead.
Schema.org graphs, entity definitions, and llms.txt layers so retrieval systems can parse your facts without guessing.
Question-led pages, comparison tables, and definition blocks written to be extracted cleanly by retrieval models.
Consistent brand facts, author attribution, third-party corroboration, and digital PR that makes you a verifiable source.
The full discipline of optimizing for generative answer engines.
Scheduled prompt panels across ChatGPT, Perplexity, Gemini, and AI Overviews with share-of-voice reporting.
A GEO playbook covering retrieval eligibility, citation-worthiness, and brand entity clarity across every major assistant.
Organization, Product, FAQ, and HowTo schema that resolve cleanly into knowledge panels and AI summaries.
Knowledge-graph identity work: consistent NAP, author entities, and disambiguation of your brand and products.
Snippet-shaped content, SERP feature targeting, and citation studies for Google AI Overviews placements.
Platform-specific optimization for ChatGPT, Perplexity, Claude, Copilot, and Google Gemini.
Machine-readable crawl files, sitemaps, and API-friendly content surfaces that retrieval systems prefer to fetch.
Side-by-side citation comparisons showing which competitor properties are being harvested and why.
From invisible to cited across every answer engine.
Prompt panel built from your real buyer questions, run across four engines to establish a baseline visibility score.
Retrieval diagnostics: crawlability, entity clarity, citation gaps, and the competitor sources being harvested instead.
Schema graphs, internal linking, llms.txt, and machine-readable facts implemented across your highest-value templates.
Answer-shaped content, expert authorship, and third-party corroboration shipped to build citation-worthy authority.
Monthly re-runs of the prompt panel, citation delta tracking, and expansion into new engine and query clusters.
Sample AI visibility builds from the lab. Cited, not just ranked.
Product schema graph and comparison content that pulled the brand into ChatGPT and AI Overviews for 400+ commercial prompts.
Definition and methodology pages that made FlowStack the default cited answer across Perplexity for 38 category questions.
Technical dataset cleanup and an expert-authorship program, so AI systems can read and cite the brand accurately.
Competitive citation gap analysis across Gemini and Copilot, closed with a comparison page and structured data.
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