AI app for hospitality and events · no coding needed
Transparent dining and local place shortlist platform
Reduce the time to a shortlist that matches stated taste and occasion while keeping the reasons visible.
Made for: Travellers and locals choosing where to eat and what to visit in a city
What it does for you
The problem
Dining choices are spread across review sites, social feeds and map apps, so matching a personal taste and occasion to a place takes repeated manual browsing.
What it gives you
Transparent shortlist of dining and local places with source-linked reasons
What you give it
Stated preferencesoccasionbudgetlocationpermitted reviewsocial sources
Build your own version of FindAMeal, Wanderboat 2.0 and more
One app with what these 3 AI tools do, yours to keep and change: FindAMeal, Wanderboat 2.0, TravelMind.
Everything these tools do, in one app
- AI personalized recommendations Uses AI to suggest places that match your personal preferences.Found in FindAMeal, Wanderboat 2.0, TravelMind
- Restaurant discovery Helps you find dining options and places to eat.Found in FindAMeal, Wanderboat 2.0
- Local place discovery Surfaces nearby spots beyond just restaurants, such as bars and attractions.Found in Wanderboat 2.0, TravelMind
- Aggregates review platforms Consolidates restaurant data from multiple review sites so you don't browse them separately.Found in FindAMeal
- Social media content Uses real-time social media videos and photos to ground recommendations in real experiences.Found in Wanderboat 2.0
- Swipe-based preference learning Learns your taste quickly through swiping instead of ratings.Found in TravelMind
- Occasion-based suggestions Offers recommendations for specific dining occasions like casual hangouts, romantic dates, or special events.Found in FindAMeal
- Natural language queries Interprets queries like finding the best date spot or a fancy steakhouse for a birthday.Found in FindAMeal
- Customizable filters Lets you refine searches by cuisine type, price range, and atmosphere.Found in FindAMeal
- Real-time availability updates Provides current information on restaurant availability and user ratings.Found in FindAMeal
- Integrated map interface Shows curated places on an interactive map for easy exploration.Found in Wanderboat 2.0
- Chat-based planning Offers interactive chat feeds for planning and getting personalized suggestions.Found in Wanderboat 2.0
- Portable taste profile Carries your preference profile across cities so past choices influence new recommendations.Found in TravelMind
- User-contributed listings Allows locals and creators to add lesser-known spots to the app.Found in TravelMind
- Mobile-first availability Available on iOS and Android for on-the-ground use.Found in TravelMind
- Multi-city coverage Works in multiple major cities, enhancing local discovery.Found in FindAMeal, TravelMind
How it works, step by step
- Capture stated taste, occasion, budget and location
- Learn preferences through swipe or quick-choice cards
- Aggregate permitted review and listing sources into one place record
- Surface real-time social videos and photos as grounding evidence
- Interpret natural language queries such as a date spot or a birthday steakhouse
- Apply cuisine, price and atmosphere filters
- Rank places by fit to the stated preference and occasion
- Show current availability and recent user ratings where permitted
- Plot the shortlist on an interactive map
- Support chat-based planning for a trip or an evening
- Carry the taste profile across cities
- Accept user-contributed listings with source attribution
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned transparent shortlist of dining and local places with source-linked reasons with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Transparent dining and local place shortlist platform with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Transparent dining and local place shortlist platform with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data194 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce the time to a shortlist that matches stated taste and occasion while keeping the reasons visible. For travellers and locals choosing where to eat and what to visit in a city, convert stated preferences, occasion, budget, location and permitted review and social sources into a transparent shortlist of dining and local places with source-linked reasons. The benefit is a testable hypothesis, measured through shortlist acceptance rate and time to a booked or visited place; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect stated preferences, occasion, budget, location and permitted review and social sources, then follow this sequence: 1. Capture stated taste, occasion, budget and location. 2. Learn preferences through swipe or quick-choice cards. 3. Aggregate permitted review and listing sources into one place record. 4. Surface real-time social videos and photos as grounding evidence. 5. Interpret natural language queries such as a date spot or a birthday steakhouse. 6. Apply cuisine, price and atmosphere filters. 7. Rank places by fit to the stated preference and occasion. 8. Show current availability and recent user ratings where permitted. 9. Plot the shortlist on an interactive map. 10. Support chat-based planning for a trip or an evening. 11. Carry the taste profile across cities. 12. Accept user-contributed listings with source attribution. Resolve uncertain cases with qualified reviewers, approve transparent shortlist of dining and local places with source-linked reasons, and measure shortlist acceptance rate and time to a booked or visited place against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed city set and permitted source set; final booking and visit decisions remain with the user. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve author voice, source attribution, quotation accuracy and usage permissions. Authors approve substantive changes and publication scope. One fixed city set and permitted source set; final booking and visit decisions remain with the user. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed city set and permitted source set; final booking and visit decisions remain with the user. Implement one approved input format, a bounded representative case set and the first two task modules: capture stated taste, occasion, budget and location; learn preferences through swipe or quick-choice cards. Support the third module with operator review: aggregate permitted review and listing sources into one place record. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
User-owned preference profiles, permitted review and listing sources and authorized social content. Cloud asset storage, map and booking destinations. Start with file exchange and validate destination specifications before promising direct booking. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Preference and occasion intake, Shortlist and map, Place detail and booking handoff. Use a swipe or quick-choice card stack for taste learning, a central map with a ranked list beside it, and a detail panel showing source-linked reasons, hours, price band and availability. Let users compare two or three places side by side. Display draft, shortlisted and visited states. Provide a shareable shortlist link for a group. Make the task-specific outcome transparent shortlist of dining and local places with source-linked reasons visible beside its evidence, review state and value baseline.
