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.
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.
Pulled for a modern European bistro in Paris with visibility gaps across menus and platforms.
Strong on neighborhood relevance, weaker on menu specificity.
Confident tone, but missing booking context and current positioning.
Good vibe match, low certainty because review platforms are thin.
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.
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.
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.
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.
The workflow is built for operators, marketers, and owners who need answers fast, without running a six-week SEO project first.
Google Maps URL or manual details with business name, city, country, and website.
"Best bistro near Le Marais for date night" and similar recommendation scenarios.
Visibility score, cross-engine quotes, platform audit, website findings, and prioritized fixes.
Add the Google Maps listing or the core business details so Rekkd can anchor the scan to the exact restaurant you care about.
Rekkd compares what leading models say with hard website, schema, and platform signals so the answer is grounded in evidence rather than vibes.
The report surfaces what will matter most next, from menu markup to copy alignment and missing review or booking surfaces.
Each scan turns recommendation behavior into a readable operating picture of how your restaurant is being interpreted across the open web.
A stable benchmark that weighs website quality, structured data, platform coverage, and engine output together.
See the cuisine, mood, audience, and price signals AI believes about you before a diner clicks anything.
Read the real recommendation language from ChatGPT, Gemini, Claude, and Perplexity side by side.
Test prompts for date night, brunch, business meals, tourists, locals, and cuisine-specific searches.
Spot where menus, booking links, review platforms, and discovery sites are missing or inconsistent.
Get an ordered action list with a reason for each fix so teams know where to spend the next hour.
Built for single-location operators first, with room for agencies and multi-venue groups that need repeated visibility checks.
Perfect for a first visibility audit and a quick read on whether AI is already recommending you.
For restaurants actively improving AI discovery and wanting repeat snapshots after each round of changes.
For agencies, hospitality groups, and portfolio operators managing visibility across multiple venues.
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.
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.
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.
The scan combines direct recommendation responses, website quality, structured data, and listing presence so the recommendations are explainable instead of hand-wavy.
Most checks complete in a couple of minutes, which makes it practical to use during normal operating or marketing cycles.
Start free, audit one location, and get a concrete list of improvements before your next marketing sprint.