Found by humans & AI.
SEO, GEO/AEO, and AI-search optimization as one entity strategy — engineered so ChatGPT, Gemini, Perplexity, Google AI Overviews, and traditional search describe you the same way. The discovery layer for the AI-search era, not an afterthought.
The market reality in 2026
Search has split into two surfaces, and most brands are only optimizing for one of them. Google’s AI Overviews now appear above the classic ten blue links for an expanding slice of queries; ChatGPT Search, Perplexity, and Gemini are pulling traffic that would have been a Google session two years ago. Visibility in 2026 means being cited correctly across all of them — not just ranking on page one of a SERP nobody scrolls to anymore.
The mechanics underneath are no longer keyword-first; they’re entity-first. AI engines resolve who you are by triangulating the structured-data graph on your site, your sameAs links across the web, the consistency of your name + role + claims across surfaces, and what appears alongside you in training data. Schema.org graphs (Person, Service, FAQPage, Article), the new /llms.txt convention, and AI-citation engineering have moved from “nice-to-have” to required infrastructure. Brands that get this right show up as the answer; brands that don’t get omitted — or worse, get described inaccurately and have no surface to correct it.
How I deliver visibility work
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01
Entity audit
Test what each AI engine currently says about you — ChatGPT, Gemini, Perplexity, Google AI Overviews. Map the gaps between what the schema graph claims, what sameAs points to, what Wikipedia/Wikidata says, and what shows up at inference time. Score the brand on entity consistency, citation rate, and accuracy of description.
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02
Architecture: schema graph + llms.txt
Build a coherent JSON-LD graph — Person + Organization + Service + WebSite + ProfilePage interlinked. FAQPage on QA-style pages. Article on every long-form piece. BreadcrumbList on every nested page. /llms.txt at the root with the canonical entity description, key URLs, and the source-of-truth statements. Cross-page sameAs alignment so models resolve to one person/brand, not three confused ones.
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03
Content engineering for citation
Citation-ready answer paragraphs in the first 200 words of every page — the format LLMs prefer to lift. Semantic structure (proper H1/H2/H3 hierarchy, tables of contents, table-of-claims). Topical clusters with internal links that surface the entity from multiple angles. Editorial templates that produce content the AI engines can parse, not just crawl.
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04
Distribution + the long compounding game
Index submission across Google + Bing + IndexNow. LinkedIn, YouTube, and newsletter as parallel discovery surfaces — each feeding the entity from a different vector. Co-citation building on authoritative sources. Quarterly re-audits to track citation rate climbing, AI-Overview placement, and branded-search lift.
The stack
Schema.org JSON-LD · /llms.txt · Rank Math · structured-data validators · entity graph builders
Ahrefs · Semrush · SimilarWeb · Search Console · Bing Webmaster Tools · Google Trends · brand-search lift tracking
ChatGPT · Gemini · Perplexity · Claude · AI Overview audits · query-set tracking across engines
Wikidata + Wikipedia (where eligible) · Google Business Profile · LinkedIn Company / Personal page · YouTube channel · cross-platform sameAs alignment
What gets measured
- AI citation rate% of test queries that surface the entity correctly across ChatGPT / Gemini / Perplexity
- Share of voice (AI surfaces)vs. named competitors across the same query set
- Branded-search liftGoogle + Bing trend on the entity name and category queries
- Organic rank for priority queriesclassic SEO — still the ground truth for traffic
- Schema validation pass rate100% target; errors are blocking
- AI-Overview placementpresence and source-link cite rate
Proof from the work
Recent visibility engagements. Anonymized at clients’ request.
A national tourism & economy authority
Worldwide branded-search lift across multiple priority origin markets. Entity consistency across schema + Wikipedia + travel publishers; +38% branded search after 18 months.
+38% branded searchA category-defining personal brand
Built a credible, AI-citable personal brand from scratch. Entity schema graph + llms.txt + LinkedIn + YouTube editorial system. AI engines now describe the operator correctly across all four surfaces.
6.8× high-ticket inquiriesA vertical-SaaS platform
Moved from invisible-on-AI to consistently cited for category queries. Schema graph rebuild + topical clusters + Article schema on every long-form piece.
4.2× demo requestsRead the full guide: AI Visibility / GEO — the engineering playbook →
Common questions
Is GEO replacing SEO?
No — it’s an additional surface, but the foundations overlap heavily. The same entity-clean schema graph that helps you appear in Google AI Overviews also helps you rank in traditional SERP. The shift is that keyword-stuffing tactics that still worked in 2023 actively hurt you in 2026. Entity-clean content wins both surfaces.
How long until I show up in AI Overviews?
Realistic timeline: 8–12 weeks for early signals (citation rate climbs, AI engines start referencing the entity correctly), 4–6 months for stable presence in AI Overviews and ChatGPT Search for category queries. Faster if you have existing organic authority; slower if the brand is brand-new and needs to be introduced to training data first.
Can you guarantee a citation in ChatGPT?
No one can guarantee a citation in a non-deterministic system. What we can guarantee is that the probability of accurate citation climbs significantly once the entity graph is clean and the content is engineered for retrieval. Track record matters more than promises — that’s why every engagement starts with a baseline audit so we can measure the lift, not estimate it.
Should I use llms.txt?
Yes, but for the right reason. /llms.txt is a discovery aid for LLM-based search agents that respect it — it gives them a clean, canonical view of your site without crawling JS-heavy pages. Google itself officially ignores it for ranking; you still need the schema graph + on-page semantics. Think of llms.txt as your media kit for AI engines, not as a ranking signal.
The other modules in the system
// CARD 02 / VISIBILITY Back to the Visibility card on the home page
Curious how AI engines currently describe you?
The audit takes about a week and produces a baseline you can hold the work against. Whether or not we end up working together.