AEO Architecture
Your prospects are asking AI before they ever search Google.
ChatGPT, Claude, Perplexity, Google AI Overviews — a growing share of prospect research now runs through answer engines that cite a handful of sources and ignore the rest. AEO is the work of being the source that gets cited. The methodology is an extension of the same discipline I’ve run for eleven years: structure, authority, and position — applied to a new surface.
The problem most AEO offerings don’t solve.
Most of what’s sold as AEO today operates at the prompt-optimization register: testing how a brand appears in AI answers and tweaking copy until the output sounds better. The strategic problem lives a layer down, at the structural-citation register — whether the entity is unambiguous to the systems doing the answering, whether the infrastructure exists for them to extract from, and whether the authority signals that govern citation point where they should. Answer engines don’t cite the best-written page. They cite the most extractable, most corroborated source. That’s an architecture problem, and architecture is buildable.
The math, sized for AEO.
The structure: take the share of your prospects whose research now starts in an AI interface — a share that moves in one direction. Within that share, citation visibility is close to binary: the engines present a few sources as the answer, and everything else is absent rather than ranked lower. Multiply presence-or-absence by your client lifetime value, and the math behaves like the early years of search all over again — the positions are being assigned now, and they’re cheaper to take while most of your competitors haven’t noticed the surface exists. The inputs are yours; what’s distinct about this surface is how few sources share the answer.
Medical and surgical practices
Patients research procedures, risks, and recovery conversationally — the exact query shape answer engines serve best. The practice the AI names at the end of that research holds the position that matters.
Legal practices
“Do I have a case” and “what kind of lawyer do I need” are answer-engine questions before they’re Google searches. Citation at the research stage shapes the shortlist.
Financial advisors
Prospects ask AI to explain, compare, and shortlist before any human conversation. Being absent from the answer is invisible — to you and to them.
What’s included.
- Entity and citation infrastructure — making the practice unambiguous to the systems that answer.
- Structured data across the surfaces engines extract from.
- Content architecture built for AI extraction: the formats, schemas, and corroboration patterns that get cited.
- Monitoring across LLM platforms — what the engines say about the practice, tracked over time.
- Reporting on presence, accuracy, and movement.
What’s not.
- Paid placement in AI platforms — separate surface; this practice is organic citation.
- Chatbot deployment — building AI for your site is product work, not search work.
- LLM fine-tuning — out of scope; the work optimizes what existing engines cite, not the engines themselves.
How the engagement runs.
The engagement opens with a project phase: the entity audit, the baseline of what each platform currently says, the infrastructure build — defined scope, defined timeline. Maintenance follows month-to-month. Weekly reporting on citation presence and accuracy across platforms; quarterly strategy review as the platforms themselves evolve — and they evolve faster than Google ever did. Month-to-month renewal, the authority to end it yours at every boundary.
Adjacent disciplines.
AEO and SEO share a foundation — the citation infrastructure that answer engines trust is built on the same authority signals that rankings are. If commercial keyword visibility is also strategic, that’s SEO Architecture. And when engines answer questions about you by name, what they say is a reputation surface — that’s Reputation Architecture.
Questions prospects ask.
Is AEO stable enough to invest in?
The platforms change quickly; the fundamentals they cite against — entity clarity, corroborated authority, extractable structure — have been stable across every iteration so far. The work targets the fundamentals, not the quirks of any one model.
Which platforms does the work cover?
ChatGPT, Claude, Perplexity, and Google AI Overviews as the core set, with monitoring extended as new surfaces earn prospect traffic.
How is success measured?
Presence and accuracy: whether the platforms cite the practice for the queries that matter, and whether what they say is correct. Both are baselined at the start and reported against throughout.
Do we need SEO first?
They share infrastructure, and authority built for one serves the other. Sequence depends on where your prospects actually are — that’s a fit conversation, not a default.
Browse the full FAQ →Engagements at AEO Architecture scope are structured around the strategic situation specific to your practice — the state of the entity infrastructure, the platforms your prospects use, the queries that matter. Budget context is captured in the consultation form so we can discuss fit and scope on the call.