AI recommendation intelligence for restaurants
See how AI perceives your restaurant, then fix what is costing you recommendations.
Rekkd runs one scan across ChatGPT, Gemini, Claude, and Perplexity, then turns the result into a visibility score, cross-engine quotes, platform findings, and a prioritized fix queue your team can actually ship.
- 4 AI engines in one scan
- Website, schema, and platform checks
- 3 free checks every month
Sample report
Le Jardin Social
Modern European bistro in Paris with strong demand, uneven machine-readable signals, and a clear next move list.
74
visibility
ChatGPT
Gemini
Claude
Perplexity
3 / 4
Engines currently recommending the restaurant
8 / 12
Signals already scoring above the benchmark
4
Fixes with the highest expected lift next
What the models are saying
-
ChatGPT
Feels like a fit for date night, but menu specifics are too thin to recommend with confidence.
-
Gemini
Strong on atmosphere and neighborhood context, weaker on booking and price-point clarity.
-
Perplexity
Cites credible sources, but the story changes between review sites and the official website.
Fixes with the highest lift
-
Publish readable menu details
Expose pricing, specialties, and dietary cues instead of leaving them buried in imagery.
-
Align Google and site positioning
Repeat the same cuisine, vibe, and occasion language where the models already show interest.
-
Patch missing platform signals
Strengthen booking and review coverage so recommendation confidence stops dropping across engines.
Product proof
Why this matters
AI is shaping the shortlist before people ever open Maps.
Recommendation engines do not browse the way diners do. They synthesize what your restaurant seems to be, whether the data looks trustworthy, and how consistently the web repeats the same story.
Metadata becomes the pitch
Menus, schema, booking surfaces, reviews, and business descriptions are often what decide whether you fit prompts like date night, business lunch, hidden gem, or best local spot.
Missing signals lower confidence
If the website is vague, listings disagree, or third-party coverage is thin, models become hesitant even when the venue is a strong real-world match.
Fixes can be concrete
Visibility is not magic. The right audit turns guesswork into a specific queue across content, structured data, listings, and platform presence.
How it works
Start with the restaurant. Finish with a clear fix queue.
The workflow is built for operators, marketers, and owners who need a fast answer without turning this into a months-long SEO project first.
Anchor the exact venue
Start with the Google Maps listing or the core business details so the scan stays tied to the exact restaurant you care about.
Interrogate the recommendation layer
Rekkd compares four AI engines against hard website, schema, and platform signals so the report is grounded in evidence rather than vibes.
Ship the next improvements
The result is an ordered action queue across copy, markup, menus, discovery surfaces, and confidence gaps.
Restaurant input
Google Maps URL or manual details with business name, city, country, and optional website.
Live recommendation scan
Four engines, scenario prompts, and deterministic checks across the website, schema, reviews, and listings.
Output your team can use
Visibility score, cross-engine quotes, shadow profile, platform audit, and prioritized fixes.
The point is not just to know whether AI likes the restaurant. It is to know what to change before the next answer.
Inside the report
The output is built to help teams act, not just observe.
Each scan turns recommendation behavior into a readable operating picture of how your restaurant is being interpreted across the open web.
Visibility score
A stable benchmark that weighs website quality, structured data, platform coverage, and engine output together.
Shadow profile
See the cuisine, vibe, audience, and price signals AI already believes about the restaurant.
Cross-engine quotes
Read the recommendation language from all engines side by side instead of treating AI as one monolith.
Scenario map
Test prompts for date night, brunch, business meals, tourists, locals, and cuisine-specific discovery.
Platform audit
Spot where menus, booking links, review platforms, and discovery surfaces are missing or inconsistent.
Fix queue
Get an ordered action list with the reasoning attached, so the next hour of work is obvious.
Pricing
Start with one location. Scale when the workflow proves itself.
The free tier is enough to validate the motion. Upgrade when you want deeper reporting, ongoing tracking, or an agency-ready setup.
Free
$0 forever
3 checks/month
Enough for a first visibility audit and a clean read on whether AI is already recommending the restaurant.
- AI visibility score and grade
- 2 AI engines: ChatGPT and Perplexity
- AI quotes from 2 engines
- Basic scenario map
- Top 3 competitors and "Do today" action items
- Follow 1 place
Pro
$39 /month
50 checks/month
For restaurants actively improving AI discovery and wanting repeat snapshots after each round of changes.
- Everything in Free
- All 4 AI engines and the synthesis report
- Full shadow profile and what-if analysis
- Full action plan, marketer brief, and voice search analysis
- Tourist vs local lens plus score tracking and weekly dynamics
- Monthly Rekkd Insights, PDF report download, and follow up to 10 places
Agency
Custom contact us
Unlimited checks
For agencies, hospitality groups, and multi-location operators managing visibility across a portfolio.
- Everything in Pro
- Unlimited followed places
- API access
- White-label PDF export
- Multi-location dashboard and bulk competitor analysis
- Weekly Rekkd Insights and dedicated support
FAQ
Questions teams usually ask before the first scan.
Rekkd is designed to be simple to run, but the category is new enough that a little context helps people see where it fits in their marketing and operations workflow.
What is AI visibility for restaurants?
It is whether assistants like ChatGPT, Gemini, Claude, and Perplexity include your restaurant when someone asks where to eat, and how confidently they describe it when they do.
How is this different from classic SEO?
Search ranking helps people discover your links. AI visibility is about whether a model chooses your restaurant in the answer itself, based on the signals it trusts across the web.
What evidence does the report use?
The scan combines direct recommendation responses with website quality, structured data, listing presence, and platform coverage so the findings are explainable instead of hand-wavy.
How long does a scan usually take?
Most checks complete in a couple of minutes, which makes it practical to use during normal marketing, operations, and menu-update cycles.
Who is the Pro plan for?
Pro is for teams who want the full four-engine picture, deeper reporting, and ongoing score tracking after they start making changes.
Run your first report
See what AI is already saying about your restaurant.
Start free, audit one location, and get a concrete list of improvements before the next marketing sprint.