US Meal Prep Brand — AI Visibility Audit
The situation
The client was a well-known US meal prep & healthy food subscription brand competing against HelloFresh, CookUnity, Sakara, and Trifecta. They ranked top-3 on Google for “meal delivery” and “chef-prepared meals” — but their AI search visibility was significantly below what their organic SEO performance would predict.
Pre-audit baseline scan across 12 representative queries × 4 AI platforms showed: 33% mention rate on ChatGPT, 25% on Claude, 42% on Gemini, 50% on Perplexity. Almost never cited as the #1 recommendation. HelloFresh was winning roughly 83% of all relevant AI queries — and the technical reason became obvious within the first 30 minutes of the audit.
What we did
1. 12-query battery × 4 AI platforms
Ran a representative cross-section of buyer queries — “best meal prep service,” “chef-prepared keto meals,” “GLP-1 friendly meal plans,” and 9 others — against ChatGPT (GPT-4.5), Claude 3.7 Sonnet, Gemini 3.5 Flash, Perplexity, and Google AI Overviews. Logged mention rate, citation position, and which competitors won.
2. Technical foundation scorecard (9 items)
Scored llms.txt presence, robots.txt AI-crawler config, JSON-LD coverage (LocalBusiness, Service, Offer, FAQPage, SpeakableSpecification), Open Graph completeness, canonical URLs, and Core Web Vitals. Client scored 47 / 100 — strong on classic SEO signals, weak on AI-specific signals.
3. Top 5 prioritized findings (HIGH / MEDIUM / LOW)
Missing llms.txt (HIGH), no FAQPage schema despite visible FAQ content (HIGH), incomplete Service/Offer schema per meal plan (HIGH), robots.txt blocking Google-Extended (MEDIUM — Gemini training data exclusion), content not written in AI-quotable 40-60 word format (MEDIUM).
4. Competitive scan vs HelloFresh + CookUnity
Spot-checked the leaders. HelloFresh had a complete llms.txt, FAQPage schema across 30+ pages, Service + Offer schema for every meal category, Recipe schema on every meal page, and explicit allow-listing of every major AI crawler. The reason they were winning was visible in their source code.
5. 90-day action plan with weekly deliverables
Week 1-2: llms.txt + robots.txt fix + FAQPage schema (+20-25 score points). Week 3-4: Service + Offer schema for all 8 meal plans + SpeakableSpecification sitewide (+15-19 points). Week 5-6: top-5 page content rewrites + Recipe schema on 50 meal pages (+18-22 points). Week 7-8: citation push. Week 9-12: monthly tracking + iteration. Projected end-state: 105-120 / 100.
The results
The audit itself was the deliverable — a 7-page PDF that gave the client a clear, prioritized path from 47/100 to a projected 105-120/100 within 90 days. Specifically:
- Mention-rate baseline locked in across 4 AI platforms — measurable starting line for future tracking.
- 5 highest-leverage gaps identified with severity ratings, observation, business impact, and exact fix per gap.
- Competitive teardown showed exactly which technical moves HelloFresh used to dominate AI citations.
- Weekly action plan with projected score impact per fix — so the client could plan resource allocation against expected lift.
- Realistic 90-day projection: 70-85% AI mention rate, with citation-as-#1 in roughly half of relevant queries.
What we learned
Strong traditional SEO does not translate to AI search visibility. The two systems use different signals. A brand can rank #1 on Google and still be effectively invisible to ChatGPT, Claude, Gemini, and Perplexity. We see this pattern in roughly 7 out of 10 audits we run on brands with established Google rankings — they assume their AI visibility is fine because their Google visibility is fine, and it's almost never true.
The fix is rarely “more content” or “more backlinks.” It's technical AI-discovery hygiene: llms.txt, schema completeness, AI-quotable content patterns, and explicit crawler allow-listing. Most agencies don't ship any of these because they're invisible in the “is my site indexed by Google?” mental model.
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