The AI platform Food and Beverage Retail uses to get recommended on
Shoppers ask an AI where to buy, what is in stock and what is safe to eat. LBOX measures what the engines say about your stores, finds where a delivery platform or review site is answering in your place, and builds the evidence that changes it.
Measured across
Perplexity
Gemini
Google AI
Claude
Copilot
Meta
DeepSeekGrok... and moreEvery one of them is competing for the same answer. One name comes back when a buyer asks.
What makes Food and Beverage Retail different
Not general marketing advice. These are the constraints your category actually operates under.
Delivery platforms answer, and they take the margin.
Ask where to get something and the engines surface Instacart, DoorDash and Uber Eats. Those platforms already take a cut of the order, and they are now also the source that decides whether your own ordering page is mentioned at all.
Stock and price move faster than any answer.
What you carry changes weekly and what it costs changes daily. Engines quote from cached pages and third-party feeds, so a shopper is told you stock something you dropped a season ago.
Allergen and dietary questions are answered from other people data.
Gluten free, nut free, halal, kosher, vegan. These are the highest-intent questions in your category and the ones where being wrong matters most, and the engines answer them from aggregator listings rather than from you.
Local intent is decided by maps and reviews.
Near me is the whole game in retail, and the engines resolve it through Google listings, Yelp and review counts. Your store estate is described by profiles you may not have touched in years.
What you get back
The console, measuring a company in Food and Beverage Retail against the questions its buyers ask. Every brand named, every domain cited, every engine separately.
A worked example on an illustrative company, shown to display the console. Your own numbers come from measuring your questions through the engines’ own APIs.
See where the engines send shoppers
Visibility and position on the unbranded where-to-buy questions shoppers ask in your area, measured per engine.
- Ranked against the retailers the engines actually named
- See where a model reads your pages without naming you
You against the field, engine by engine
unbranded questions| Brand | Gemini | AI Overviews | OpenAI | Perplexity | Claude | ChatGPT |
|---|---|---|---|---|---|---|
| Fernhill Market | 33% | 30% | 27% | 23% | 15% | 31% |
| Cobblestone Foods | 27% | 25% | 22% | 19% | 12% | 26% |
| Greenmarket Grocersyou | 26% | 17% | 22% | 17% | 10% | 3% |
| Willowbrook Provisions | 15% | 12% | 10% | 13% | 6% | 12% |
Turn what the answers get wrong into work that ships
Findings arrive as a worklist sorted by who can act: what LBOX writes, what needs your decision, what needs your store data team.
- Scored against your category, not a generic SEO checklist
- Nothing publishes without your sign off
The worklist
Prove the change, week after week
Every measurement is stored, so the line lengthens each cycle and the movement is on the record rather than asserted.
- The same questions, asked again and checked
- Sources broken out per model, per page
Visibility over time
every measured runKnow when the models come to read your store pages
Crawler activity captured at the edge, so you can see which models fetch your range, location and dietary pages.
- Every model, every page, logged over time
- Robots and content signals kept current
Crawler activity
hits by model, last 30 daysWhere you disappear as the shopper gets closer to the basket
Presence falls away as intent rises. The where-can-I-buy-it questions are the ones that convert, and the ones delivery platforms win.
- Visibility split by where the question sits in the journey
- Names the stage that costs you the order
Visibility by buying stage
where you show up in the journeyThe sources the engines trust in food retail
Maps, review platforms and delivery apps decide what the engines repeat about your stores. Several of them already take a cut of the order.
- Owned versus earned, so you know what to build and what to earn
- Every citation traced to the answer it supported
Which domains the engines cite
637 citationsThe program for Food and Beverage Retail
Everything below is included. Measured through the engines’ own APIs, so the same questions can be asked again and the result checked.
- Measure what every major AI engine says about your stores and range today, through the engines own APIs
- The shopper questions you are actually asked, mapped by category, occasion, dietary need and location
- Where a delivery platform or review site is answering in your place
- Whether the engines describe your range, hours, locations and dietary information correctly
- Which sources the engines cite for your category, and which of those you control
- Store, range and dietary pages built so the engines can read them accurately
- Re-measurement on the same questions, so the change is provable rather than asserted
What this does not show: what any individual user saw. LBOX measures what the engines answer through their own APIs, which is repeatable and checkable. It is not a record of any one person’s session, and it does not predict revenue.