Run by engineers who practice GEO on their own site
Most AI-visibility offerings are repackaged SEO with new vocabulary. Ours comes from engineers who treat it as a measurement and infrastructure problem. The site you're reading is itself a worked example — a structured entity graph, machine-readable definitions, an llms.txt manifest, and answer-shaped content built specifically so AI engines can parse, attribute, and cite it. We do for your brand what we've already done for ours.
That means no hand-waving. Every engagement starts from data: which engines cite you today, for which prompts, and where a competitor is winning the answer you should own. Then we change the things that move that number, and we show you the before-and-after.
What actually moves AI citations
Being visible in AI answers is not one lever, it's several. There's the difference between being mentioned across the web and being cited as a source — engines weigh both, and they're earned differently. There's the shape of your content: AI engines extract self-contained, answer-first passages, not buried prose. There's evidence — original statistics, quotable lines, and clear citations measurably raise the odds of being pulled into a generated answer. And there's machine legibility: structured data, a consistent entity across the web (E-E-A-T), and clean technical access for AI crawlers.
We'll also give you a straight answer on the tactics that are oversold. llms.txt, for instance, is worth publishing but is not a magic ranking switch and isn't yet honored by the major engines as a ranking factor — we'll tell you where it helps and where effort is better spent. The goal is durable visibility built on substance, not a checklist of tricks that ages out in a quarter.
Who it's for
- B2B and professional-services firms whose buyers research with AI before they ever make contact
- Brands that rank in Google but go unmentioned when the same question is asked of ChatGPT
- Marketing and growth teams adding GEO alongside an existing SEO program