Restaurant GEO — be the recommendation, not just the citation.

Restaurant GEO is the sibling discipline to AEO. Where AEO is about being cited inside an answer, GEO is about being included inside a generated list — the 5 restaurants ChatGPT names when a diner asks for a weekend in Lisbon, the 3 it picks for a corporate dinner in Singapore, the itinerary it builds for a food tour of Mexico City.

GEO and AEO overlap but are not identical. Many restaurants get cited in AI answers without being included in generated recommendation lists, and vice versa. The two together form the AI search layer of modern restaurant marketing.

What restaurant GEO is — and what it is not

GEO is the practice of optimising a restaurant to be included inside AI-generated content — recommendation lists, itineraries, summaries, comparison tables. It overlaps with AEO (which targets citations inside answers to specific questions) but the optimisation surface is different.

GEO is not 'tricking the LLM'. The models that matter — GPT, Claude, Gemini — are robust to prompt-injection and trivial tricks. GEO works by making the restaurant a clear, well-evidenced choice that the model is likely to surface organically.

How LLMs generate restaurant recommendation lists

From our work studying retrieval behaviour across 14 LLM deployments, restaurant recommendation lists are generated through a combination of training-time priors (what the model 'remembers' about a restaurant), real-time retrieval (what current sources say about it), and constraint matching (the diner's request: budget, cuisine, neighbourhood, occasion).

Restaurants optimise for GEO by improving all three: stronger entity presence (priors), better-maintained sources (retrieval), and well-tagged constraint metadata (matching). The first two are an editorial and PR exercise; the third is a content and schema exercise.

Signals that get restaurants included

• Editorial coverage in 5+ authoritative sources (Time Out, Eater, FT, local press, trade)
• Wikipedia entry with at least 3 references to independent sources
• Wikidata Q-number with full property set (cuisine, price level, neighbourhood, awards)
• Restaurant website with deep schema and structured content
• Consistent NAP and metadata across the citation graph
• Recent (last 6 months) updates to Google Business Profile
• Niche review presence (Eater 38, Michelin, local Reddit, niche newsletters)

The role of niche review sites and trade press

Niche food and city sources are weighted disproportionately by LLMs when generating restaurant lists. Coverage in Eater, Time Out, the FT Weekend, the New York Times' '36 Hours in [city]' column, or a respected local newsletter (Vittles in London, The Infatuation in NYC) lifts GEO inclusion rates materially. Our PR layer focuses on these sources.

GEO measurement framework

We measure GEO with a panel of itinerary and recommendation prompts — 'plan a weekend in Lisbon for two food-loving friends', 'three corporate dinner restaurants in Singapore for under SGD 200pp', 'a fine dining itinerary for a Friday night in Dubai'. We run the panel monthly across the four major LLMs, log inclusions and report inclusion share.

Pricing

Restaurant GEO services run alongside SEO and AEO, typically $250–$400/month as an add-on, or starting at $550/month standalone. Includes entity optimisation, editorial PR layer, schema, monthly measurement and a full report.