Generative engines like ChatGPT and Perplexity don't rank links anymore — they synthesise an answer. Two disciplines decide whether your property makes that answer: GEO for the brand narrative, AEO for the hard facts.
They sound similar and often share tooling, but GEO and AEO solve different problems for an AI model. Most properties need a strategy for both.
The answer to: “when an AI describes this hotel, how does it describe it?” Built from press, reviews, social mentions, partner content and editorial coverage — the unstructured layer.
the model associates your entity with the right adjectives — exclusive, design-forward, family-run, eco-led — and cites you when those qualities are asked for.
The facts a model can extract and answer directly — Schema.org markup, FAQ pages, amenity tags, policy data — content that maps cleanly to a question's literal terms.
when a traveler asks a yes-or-no or numeric question, the model gives the correct answer with confidence — and cites you as the source.
The same query runs a different gauntlet depending on what it asks for. Here is where your property is won — or quietly dropped.
The model is asking: “who is this property, in their own words?”
“Best boutique hotel in Lisbon under €250”
Find candidate properties from the model’s knowledge graph
Editorial reviews, press, social — what tone wins?
Match the property’s narrative against the query’s intent words
Up to 3 properties recommended — the rest are invisible
the model spends most of its compute on step 03 — pulling unstructured narrative from across the web. If your property’s narrative is thin, generic, or contradictory, you don’t survive the score.
The model is asking: “do they meet these specific constraints?”
“Pet-friendly · free parking · under $200 · near JFK”
Translate the query into structured field filters
Schema.org, FAQ pairs, amenity tags from indexed pages
Multiple sources must agree — contradictions disqualify
A short, confident answer with your name as the source
step 04 is where most properties die. The model finds your hotel says “pets allowed” on one page and “no pets” on a Booking listing — it can’t reconcile, so it picks a competitor whose facts agree with themselves.
None of this is impossible. The list ticks itself off as you read — these are the operational items you'd assign to a marketing lead, a content team, or an external agency. Per property. Per month.
Audit every public mention of the property — every source the model can ingest.
~ 200–2,000 mentions per established hotel
Define the target narrative — three to five adjectives the brand must own.
requires a GM & brand alignment workshop
Commission digital PR in the publications models actually weight.
~ 4–8 placements / quarter, $3–15k each
Update partner copy — DMOs, OTA descriptions, GDS content — to match the target adjectives.
15+ partners per property, cyclical
Surface guest stories that reinforce the narrative — long-form reviews, video.
Test how each engine describes the property — weekly, by hand, via prompt.
6 engines × ~12 query types = 72 prompts / wk
Track the sentiment gap — what the model says vs. what you want it to say.
no consumer tool does this; a spreadsheet today
Implement Hotel + LodgingBusiness schema across every public page.
~ 60–120 schema fields per property
Build FAQ pairs for the top 50 traveler intents per market.
refresh quarterly as intents drift
Reconcile facts across every channel — your site, OTA listings, GDS, partner pages.
~ 12–20 surfaces per property
Update the moment a fact changes — pet policy, parking, hours, kid age limits.
a 24h lag is enough to lose the citation
Verify each engine’s answer to a constraint query — by hand, with prompts.
6 engines × ~80 query types = 480 prompts / wk
Catch drifting facts before a model trains on them — before they harden.
window: roughly the model’s training cadence
Track the answer-accuracy gap — when a model is wrong, document and dispute it.
no native dispute channel exists for most engines
A single hotel needs roughly 138,240 lookups a day to keep GEO and AEO healthy across the engines that matter. Now multiply by your portfolio. This is what we automate.
One tells you where you stand today. The other tells you where the budget should go next quarter.
See your property's actual visibility score, mention velocity, and the sentiment gap between what the AI says and what you want it to say.
Run a free analysis →Three sliders — ADR, star rating, location intensity — and a recommended channel mix. The starting allocation you take to your CFO.
Open the calculator →Sigtrip runs the GEO and AEO loop continuously — across AI engines, every persona, every market — and tells you exactly what to fix, in priority order.