Frequently Asked Questions

SEO optimizes for link-based search results and getting your pages to rank near the top of a list. GEO (Generative Engine Optimization) optimizes for how your brand is represented in the synthesized narrative that AI engines produce. When someone asks ChatGPT or Perplexity about your category, GEO determines whether you appear in that paragraph, what is said, and whether it is accurate. The two disciplines complement each other but require different signals and strategies.

AEO (Answer Engine Optimization) focuses specifically on getting AI systems to extract and present your brand's information as a direct, accurate answer to specific questions. GEO is about your brand's overall presence in AI-generated narratives. AEO is about whether AI can reliably answer "What does [Brand] do?" or "How does [Brand] compare to X?" with accurate, on-brand detail. Both matter. GEO gets you into the conversation, and AEO ensures you are represented accurately when you are there.

AI engines use a fundamentally different process from traditional search. Instead of ranking a list of links, they synthesize a response from their training data and live retrieval systems. A brand can rank on page one of Google and still be invisible in AI-generated responses, or worse, be represented inaccurately. AI visibility depends on source authority, content structure, and entity clarity, not keyword placement or backlink volume.

SurfaceGX observes three validated engines at launch: ChatGPT/GPT, Claude, and Perplexity/Sonar. Results from each engine are recorded separately and dated, so you can see where the answer differs across systems. The baseline is capped at 30 observations and the recheck repeats the same scope, which is what makes the comparison honest.

SurfaceGX measures around change rather than on a subscription cadence. Each engagement records a dated baseline before repairs and one recheck of the same scope after you report implementation. AI engine behavior does shift as training data and retrieval systems update, which is exactly why the recheck is bounded to the original scope: it isolates what your repairs changed from what the engines changed on their own.

Repair artifacts include revised copy, content briefs, schema, and configuration guidance, drafted by SurfaceGX and editorially reviewed by a founder before delivery. Your team approves and publishes; nothing ships automatically.

llms.txt is an emerging standard, analogous to robots.txt, that helps AI language models understand which of your pages are most important and how they should be used. Instead of blocking crawlers, it actively guides AI systems toward your best content. One important surface distinction: Google has confirmed that llms.txt is not required for Google AI Overviews or AI Mode — Google's generative features run on standard search fundamentals. Discovery files apply to independent AI surfaces such as ChatGPT, Perplexity, Claude, and AI-powered agents that crawl on demand. Where a discovery file is the right repair, it arrives as one of the five artifacts in the engagement, scoped to the surfaces where it applies, with a directive hint for each URL that tells AI systems what that page is for.

A page is ready when it can be read clearly, communicates meaningful brand information, and gives AI systems enough confidence to treat it as useful source material. In an engagement, page readiness gaps become Fix Cards with an owner, acceptance criteria, and a validation method.

Competitor-citation analysis is part of the Authority + Citation Repair add-on, which examines the sources AI engines cite in your category and what is giving competitors authority you could pursue. The standard Sprint stays focused on one change to your own domain.

Every engagement ends with a concise proof report, 5 to 7 pages plus evidence links, alongside the five Fix Cards and their attached artifacts. Those files are yours to circulate; there is no login for your stakeholders to manage.

No, and that is deliberate. SurfaceGX uses its platform internally to run and document each engagement. You buy a completed repair-and-verification outcome, delivered as founder-reviewed files: Fix Cards with attached artifacts, dated evidence, and the final proof report.

Scope and truth intake come first, then the baseline, diagnosis, and the five repairs. The recheck begins only after you report deployment, so the total elapsed time depends on how quickly your team ships. The bounded scope is what keeps the engagement from drifting while that happens.

Engagements fit teams with a live company change, someone who can approve the facts, and someone who can deploy the fixes. Agencies come first: they own the client project and can reuse the offer across a roster. Direct B2B technology companies with a material change are the second fit.