Our eCommerce client of Australia had strong organic rankings but no foothold in AI-generated responses. Competitors were cited in AI Overviews. Their content wasn’t. That gap was showing up in pipeline conversations, and the team wanted it fixed before it widened further.
Generative Engine Optimization services (GEO) is what closes that gap. We combined structured content signals, authority-building across citations, and platform-specific optimization to put the client in front of users whether they’re on Google, an AI assistant, or a discovery tool like Perplexity.
Retail
Generative Engine Optimization
We audited every page on the client’s site and rewrote content to match how AI systems retrieve and cite information, clear statements of fact, structured headings, and direct answers to the questions their audience actually asks. No keyword-stuffing, no filler.
AI Overviews and ChatGPT pull heavily from sources that have earned third-party mentions. We built a citation programme that placed the client’s commentary and data in industry publications, earning the references that translate into AI visibility over time.
Perplexity, Copilot, Gemini, and Grok each retrieve content differently. We tracked performance per platform and adjusted structured data, schema markup, and content formats to improve citation rates across all six channels in the client’s dashboard.
We established a baseline before any work began, recording where the client appeared (or didn’t) across every major AI platform. Every decision after that was driven by data, not guesswork, with clear month-on-month reporting against those benchmarks.
To secure consistent visibility for the client across AI-generated search responses, not just traditional rankings by restructuring their content and building the third-party authority that AI platforms rely on when deciding what to cite.
Most pages were optimised for keyword density and structured around what ranked well in traditional search. None of it was formatted the way AI systems prefer to read, summarise, and cite. The site was invisible to a growing share of the search experience.
The client had almost no external citations, no mentions in trade publications, and no presence on the platforms that AI models treat as trusted sources. AI assistants couldn’t reference them because nobody else had done so either.
The team was tracking traditional rankings and page traffic but had no way of knowing whether they were appearing in AI-generated answers at all. There was no baseline, which meant no way to know if anything was working or not.
Two direct competitors in the Australian market were already being cited regularly in Google’s AI Overviews. The gap wasn’t theoretical, it was active, and it was getting wider each month.
Six months in, the picture looked noticeably different post OM started working for them. Not just in AI visibility, the traditional search side moved too, because good structure is good for everyone.
Six months in, the client had 517 AI Overview appearances and 262 pages surfacing in Google’s AI results, up from near-zero with month-on-month AIO response growth hitting +76 in the most recent period alone.
Across six platforms ,Google AI Mode, Gemini, Perplexity, Copilot, Grok, and ChatGPT the client built a tracked, reportable presence that simply didn’t exist before. Grok’s crawl alone picked up 1.9K pages, while ChatGPT began citing the client in 131 responses, a channel with no prior baseline.
The same well-structured content that drove AI visibility also widened the client’s reach across conventional rankings, because good structure works for algorithms and AI systems alike.
