I built the infrastructure to make a health brand citable by AI search.
Search is splitting in two: the ten blue links, and the answer an AI gives directly. I structured Dr. Berg Nutritionals so ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, and Copilot can find, parse, trust, and cite it — llms.txt, A2A endpoints, MedicalWebPage-grade schema, verified author and medical-reviewer markup, and a citation-tracking method to measure whether it lands. This is answer-engine optimization built as real technical infrastructure, not a content afterthought.
AI answers health questions directly now.
Being absent from the answer — or cited without proper authorship signals — is both a visibility and a trust problem.
For a medical-adjacent brand, that’s not optional.
Traditional SEO optimizes for a crawler and a ranking. AI search optimizes for a model that has to decide which sources are authoritative enough to quote.
Dr. Berg’s site needed to be machine-legible to LLM crawlers, needed unambiguous entity and authorship signals — who Dr. Eric Berg is, who medically reviewed the content — and needed a way to measure citation presence across the major assistants so the work could be steered by data rather than assumption.
Three layers: legibility, authority, measurement.
I treated AEO/GEO as a technical-SEO discipline: make the site legible to AI crawlers, make its authority explicit through structured data, and measure citation presence across the major assistants.
Machine legibility. An llms.txt file published live via Rank Math describing the Dr. Eric Berg DC entity, key topics, and entity links — plus AI-friendly crawler directives, A2A endpoints, and NLWeb for machine access.
Authority signals. sameAs identity across all pages, a clean Organization / Person entity separation, movement toward MedicalWebPage-grade markup, and a medical-reviewer audit feeding author/reviewer signals — exactly the E-E-A-T cues AI systems weigh.
Measurement. AI-citation tracking across six assistants, using prompt auditing to check whether and how the brand surfaced in AI answers.
AI search is a technical-SEO discipline, not a content afterthought. The job is to make a health source legible enough to parse and authoritative enough to quote.
— Design principle · built to be citedThe AI-search stack.
A legibility layer exposes the site to AI crawlers. An authority layer makes the brand’s expertise explicit in structured data. A measurement layer watches whether the six major assistants actually cite it.
The legibility layer (llms.txt, A2A, NLWeb, AI-crawler directives) makes the site machine-readable. The authority layer (8 sameAs profiles, a clean Organization/Person entity split, movement toward MedicalWebPage schema, 98.7% FAQPage coverage, and staged medical-reviewer markup) makes its expertise explicit. The measurement layer tracks citations across all six assistants. Quantified citation figures are held back pending methodology review — the proven claim is the infrastructure and the measurement approach.
Machine Legibility
Entity / Structured Data
E-E-A-T / Authorship
Platform
Measurement
Positioning
Six assistants, one strategy.
The citation strategy and measurement spanned every major AI assistant a health question might reach.
The infrastructure that shipped.
Infrastructure and process · verified from vault verified-metrics + Slack record. Quantified citation outcomes deliberately excluded.
Three shifts toward being citable.
What shipped — and what stays honest.
The proven claim is the infrastructure and the measurement approach. Quantified citation counts are held back until the methodology is separately reviewed.
A health brand made materially more legible and trustworthy to AI search.
- llms.txt entity file live in production via Rank Math.
- A2A / NLWeb machine access and AI-crawler directives.
- Clean Organization / Person entity graph with 8 sameAs profiles.
- 98.7% FAQPage coverage; movement toward MedicalWebPage schema.
- Medical-reviewer audit feeding author / reviewer authority signals.
- Citation-tracking method spanning the six major assistants.
Late-stage reporting did track AI-citation signals alongside SEO KPIs.
- Reporting indicated the brand was surfacing in AI answers.
- The definition of “citation” and the measurement methodology are not yet reviewed.
Quantified citation figures are deliberately withheld from headline use until the measurement definition and methodology are separately reviewed. The proven claim is the infrastructure and the measurement approach — not a specific citation count.
The broader Dr. Berg engagement (May 2022 – Apr 2026).
- Organic traffic grew ~57K → ~181K monthly visits (+217%, Ahrefs full-period).
- $8.86M in GA4 organic revenue attributed (Jul 2023 – Mar 2026).
Engagement-wide context, not a result of the AI-search build. The AEO/GEO work was one workstream inside the four-year engagement.
I set the direction and designed the stack.
I set the direction that AI search is a technical-SEO discipline for this brand, and built the infrastructure to match.
I designed what the llms.txt should say, which entity and authorship signals to deploy, how to separate the Organization and Person schema, how to prepare the medical-reviewer data for review markup, and how to measure citation presence across the six assistants. Code changes to the WordPress VIP repo were routed through the client’s dev team under a read-only-repo policy; I owned the strategy, the schema design, the measurement approach, and the review of what shipped.