AI Visibility Audit for food delivery · AI Discovery Intelligence
When someone asks ChatGPT, Claude, Gemini, or Perplexity the best delivery app in their city, whether you deliver to their area, or who is cheapest, the engine names a platform. You cannot see if it is you or a competitor, and that choice is already moving installs and orders and pushing up your acquisition cost.
28 Labs measures your share of AI recommendations for the queries that drive orders, ties it to your traffic and order economics, 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 orders
scored
A delivery platform has tens of thousands of meaningful consumer and merchant queries. We score each by intent and track the set that drives orders and merchant sign-ups.
The shift
When someone asks an engine where to order, it names a platform off your property - you do not know if it was you or a competitor.
The demand the engines used to send you now lands on whoever they recommend; to hold orders flat you rebuy it as paid installs and promos.
We measure your share of AI recommendations across the high-intent order queries on all four engines, then tie it to traffic and order economics.
The board-level risk
A delivery platform's moat is being the place consumers and merchants go to discover, compare, and transact. Answer engines now sit in front of all three. The exposure is specific, and it is measurable.
"Best delivery app in {city}" gets composed off-platform, so the hungry user who used to open your app opens a chat window.
"Does {app} deliver to {area}" and merchant-side "best app to list my restaurant" get answered from whoever the engine trusts.
The cheapest-fees and fastest-delivery questions - the ones that decide the order - get answered with a competitor named.
"Most reliable delivery service" gets decided by reviews the engine leans on.
Every figure we put against these is anonymised and confidence-tagged. We show the pattern and the magnitude; we never expose another delivery platform's numbers to make the point.
The query universe
A delivery platform's customers 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 orders, mapped to where the customer is in the decision.
Comparison, coverage, and pricing queries carry the orders; explainer queries carry almost none. We weight what you track to where the money is - 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 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 traffic and order 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 a competitor surges, and a monthly read that tracks the trend and ties it to traffic and order economics. 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 traffic and order economics 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-order-economics loop comes when we wire into your systems.
What winning takes
A delivery platform 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 customers actually ask about will.
We diagnose where the engines already trust you, where they trust a 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-order-economics 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.