AI Visibility Audit for travel · AI Discovery Intelligence
When someone asks ChatGPT, Claude, Gemini, or Perplexity where to stay, the cheapest time to fly, or the best way to plan a trip, the engine recommends a brand - and you cannot see whether it is you or an OTA reselling your own inventory. That invisible choice is already moving your direct traffic and pushing up your cost per booking.
28 Labs measures your share of AI recommendations for the queries that drive bookings, ties it to your direct traffic and CPL, and hands your teams the moves to win it back.
4 engines · intent-scored query universe · every read confidence-tagged
Measured from the outside. No SDK in your rendering path. We do not measure training-data exposure - and we say so.
The queries that move bookings
scored
A travel brand has tens of thousands of meaningful trip queries. We score each by commercial intent and track the set that drives bookings, not the inspiration long tail.
The shift
When a traveler asks an engine where to book or stay, it names a brand off your property, where your analytics never see it. You do not know if your name came back, or an OTA's.
Demand the engine used to send you direct now lands on whoever it recommends - often an intermediary reselling your rooms or seats at a margin. The gap shows up as softer direct traffic and a higher cost per booking.
We measure your share of AI recommendations across the high-intent booking queries on all four engines, then tie movement in that share to movement in your direct traffic and CPL.
The board-level risk
A travel brand's moat is being the place travelers go to discover, compare, and book direct. Answer engines now sit in front of all three. The exposure is specific, and it is measurable.
Travelers ask the engine what is available and what it costs. It answers from whatever it trusts - which may not be your rates and inventory - so the trip planning that used to start on your site now starts inside a chat window.
The engine frequently names the OTA over the brand - so even when your inventory wins, the booking, the margin, and the customer relationship route through a reseller instead of through you.
The highest-margin moment - book direct - is exactly where a competitor or intermediary gets named ahead of you, off-platform, where your loyalty incentives never surface. That is the part of the funnel you least want decided elsewhere.
Organic trip demand the engines used to send you now lands on whoever they recommend. To hold bookings flat you rebuy it as paid - so a share loss you cannot see surfaces as a CPL rise you can.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another travel brand's numbers to make the point.
The query universe
A travel brand's travelers do not ask four questions. They ask thousands, across a journey. The work is not to track 50,000 random queries. It is to score every query by commercial intent and monitor the high-intent ones that actually drive bookings, mapped to where the traveler is in the decision.
High-intent pricing, availability, and book-direct queries carry the revenue; open-ended inspiration carries almost none. We weight what you track to where the bookings are - so the share number you read is the one that matters to the business, not a vanity average across the long tail.
The decision system
Most tools stop at "you were cited less." That is a metric, not a decision. We frame it as market share - the share of high-intent recommendations you hold against the OTA or competitor taking them - and we connect that share to the numbers your business already runs on.
How we keep it honest
No one can prove a single AI recommendation caused a single lead. Anyone who claims they can is selling certainty that does not exist. We treat it the way marketing-mix modeling treats a channel: we estimate the contribution from converging signals and we label the confidence of every read, so your analysts can audit any claim back to the evidence behind it.
When a read is directly observed in your own data, we say so. When it is inferred, we say so. When it rests on industry intelligence, we say that too. You never get a confident-sounding number with nothing under it.
Directly observed. The signal is present in your own logs, CDP, or AI-engine output. We saw it happen.
Inferred. Multiple independent signals converge on the same read, but no single source confirms it outright.
Industry intelligence. The read rests on category benchmarks and external patterns, not your own data. Treated as directional.
In your stack
This is not a deck that ages the day it ships. It is a live read wired into the systems you already run, so the share number and its business impact stay current as the engines re-crawl and your competitors move.
The integrated engagement connects to your logs, CDP, and paid feeds, so the AI-share read sits next to the direct-booking and CPL data your teams already trust. No SDK in your rendering path.
You get a flag when your share on a high-intent query set moves sharply or an OTA surges, and a monthly read that tracks the trend and ties it to direct traffic and CPL. Not once a quarter, on a slide.
HIGH, MED, or LOW on every finding, so the team acting on it knows exactly how much weight it carries before committing engineering or budget against it.
Honest about the tiers. The live read on how much of your direct traffic and CPL movement is AI needs your first-party data - your logs, CDP, and paid feeds. That is the integrated, enterprise engagement, not a free snapshot. A standalone audit measures your share of AI recommendations across the four engines from the outside and shows where you are losing ground and to whom. The traffic-and-CPL loop comes when we wire into your systems.
What winning takes
A travel brand wins when it becomes the source the engines trust to answer the category. That is structural. More blog posts will not do it. Being the authoritative, reachable source for the data travelers actually ask about will.
We diagnose where the engines already trust you, where they trust an OTA or competitor instead, and the specific moves that shift that trust toward you. The output is a build plan your teams can act on, not a list of keywords.
Start here
A 60-minute working session. We walk a sample audit, show you how the share read and the traffic-and-CPL loop would shape up for your category, and scope a live run.
No obligation. We do not measure training-data exposure, and we tell you what we can and cannot see before you commit.