The proprietary Layer 0–4 framework for making businesses discoverable, parseable, and recommendable by AI search systems. Tested across nine industries. Documented in three published books. Available as a working engagement, not just a body of writing.
When a buyer asks ChatGPT, Claude, Perplexity, or Google AI Overviews for a recommendation, the AI doesn't return ten blue links. It returns an answer. One answer. Sometimes three.
Your business is either in that answer or it isn't. Being in it isn't a function of how good your business is — it's a function of whether your digital infrastructure tells the AI what it needs in the format it needs.
That's a solvable problem. We solved it methodically, layer by layer, and documented the solution. The work is now an engagement, not a discovery process.
Sequential. Each layer depends on the one below it. Most failed AEO attempts skip layers or run them in the wrong order. We don't.
robots.txt, llms.txt, and the access permissions that determine whether AI systems can read your site at all. The most common failure point — about 40% of businesses we audit are accidentally blocking the very systems they want to be cited by.
JSON-LD schema across every page type. Organization, Person, Service, Product, FAQ, Article, LocalBusiness — whatever the AI systems need to parse you correctly and trust the parsing. Sitemap, canonicals, OG and Twitter cards. The format layer.
Content structured for both human readers and AI extractors. Direct-answer formatting. FAQ pages with proper schema. Comparison content. Citation-ready references. The substance layer — most "AEO content programs" stop here and call it done. That's why they fail.
Who you are, what you do, why you can be trusted — established across Wikipedia, Wikidata, industry directories, and the corroboration network that AI systems use to verify a business is real. The trust layer. Without this, perfect content gets parsed but not cited.
Systematic testing of relevant queries across ChatGPT, Claude, Perplexity, Google AI Overviews, Bing Copilot. Tracking which competitors are cited, why, and what content shifts move the needle. The optimization layer. AEO without this is shipping in the dark.
Most clients enter at the audit and graduate to the full implementation. Some take the implementation in-house with our framework and stay on monitoring. All three work.
A complete diagnostic against the Layer 0–4 framework. Where you stand, what's broken, what's missing, what to fix first. The starting point for most engagements.
We do the work. Across all five layers, on your live site, with your team or independently. The engagement most clients ultimately want — the methodology applied, not just diagnosed.
Available only after audit or implementation. Ongoing citation monitoring, quarterly adjustment, AI-system-change advisory. For companies that want the work done but the system maintained.
Same discipline we apply to everything else. No improvisation, no surprises.
The forensic diagnostic. Where you stand on each layer, what's specifically broken, what fixes will move the needle. Written report with evidence for every conclusion.
The fix plan, sequenced by dependency and impact. What gets touched, in what order, by whom. Approved in writing before implementation begins. No moving targets.
The work — schema, content, entity, monitoring infrastructure. Done correctly the first time. Versioned, documented, reversible if needed.
Citation tracking infrastructure left running. Monthly review of what's being cited and why. Adjustment as AI systems evolve. The work doesn't end at deploy.
We've published extensively on AEO across our research lab, the educational explainers, and the books. None of it is behind a form. Read whatever helps you decide.
Or skip the audit if you've already made up your mind.
ptcollins@collinstechflorida.com