Metadata becomes the pitch
Menus, schema, reviews, booking surfaces, and business descriptions are often what AI uses to decide whether you fit a prompt like date night, business lunch, or hidden gem.
AI recommendation intelligence
If diners ask AI where to book, make sure it says your name.
Rekkd shows how ChatGPT, Gemini, Claude, and Perplexity actually describe your restaurant, where your visibility breaks down, and which fixes will change the next answer.
4
Engines checked in every run
12
Core visibility signals scored
Live
Reports built from current prompts
Sample snapshot
What a winning report looks like
Pulled for a modern European bistro in Paris with visibility gaps across menus and platforms.
72
3 / 4
Engines actively recommending you
8 / 12
Signals scoring above benchmark
5
Priority fixes queued next
ChatGPT
Strong on neighborhood relevance, weaker on menu specificity.
Gemini
Confident tone, but missing booking context and current positioning.
Claude
Good vibe match, low certainty because review platforms are thin.
- Add menu schema Make price point, specialties, and dietary cues machine-readable.
- Refresh Google copy Align description with what AI already likes about the venue.
- Strengthen platform coverage Patch Tripadvisor and booking surfaces to remove confidence gaps.
ChatGPT recommendation prompts
Gemini local discovery checks
Claude positioning snapshots
Perplexity citation comparison
Why this matters
AI is compressing the shortlist before people ever open Maps.
Recommendation engines do not browse like diners. They synthesize what your restaurant seems to be, whether the data looks trustworthy, and how consistently the web repeats that story.
Missing signals lower confidence
If your website is vague, your listings disagree, or third-party coverage is thin, models become hesitant even when the venue is a strong real-world match.
Fixes can be deterministic
Visibility is not magic. The right audit turns guesswork into a concrete queue your team can ship across site content, structured data, 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 answers fast, without running a six-week SEO project first.
Restaurant input
Google Maps URL or manual details with business name, city, country, and website.
Live engine prompts
"Best bistro near Le Marais for date night" and similar recommendation scenarios.
Output
Visibility score, cross-engine quotes, platform audit, website findings, and prioritized fixes.
Pin the venue
Add the Google Maps listing or the core business details so Rekkd can anchor the scan to the exact restaurant you care about.
Interrogate four engines
Rekkd compares what leading models say with hard website, schema, and platform signals so the answer is grounded in evidence rather than vibes.
Ship the next improvements
The report surfaces what will matter most next, from menu markup to copy alignment and missing review or booking surfaces.
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, mood, audience, and price signals AI believes about you before a diner clicks anything.
Cross-engine quotes
Read the real recommendation language from ChatGPT, Gemini, Claude, and Perplexity side by side.
Scenario map
Test prompts for date night, brunch, business meals, tourists, locals, and cuisine-specific searches.
Platform audit
Spot where menus, booking links, review platforms, and discovery sites are missing or inconsistent.
Fix queue
Get an ordered action list with a reason for each fix so teams know where to spend the next hour.
Pricing
Start with a free scan. Upgrade when you need deeper coverage.
Built for single-location operators first, with room for agencies and multi-venue groups that need repeated visibility checks.
$0 / month
Perfect for a first visibility audit and a quick read on whether AI is already recommending you.
- 5 checks per month
- Visibility score and summary
- Core website and listing signals
$49 / month
For restaurants actively improving AI discovery and wanting repeat snapshots after each round of changes.
- 50 checks per month
- Full report with cross-engine quotes
- Scenario prompts and ordered fix queue
Custom for teams
For agencies, hospitality groups, and portfolio operators managing visibility across multiple venues.
- Multi-location workflows
- Higher monthly scan volume
- Priority support and rollout planning
FAQ
Questions teams usually ask before the first scan.
Rekkd is designed to be simple to run, but the underlying problem is new enough that a little context helps people see where it fits.
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 you when they do.
How is this different from SEO?
Search ranking helps users discover your links. AI visibility is about whether a model chooses your restaurant in the answer itself, based on signals it trusts across the web.
What evidence does the report use?
The scan combines direct recommendation responses, website quality, structured data, and listing presence so the recommendations are explainable instead of hand-wavy.
How long does a scan take?
Most checks complete in a couple of minutes, which makes it practical to use during normal operating or marketing cycles.
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 your next marketing sprint.