Transparent dining and local place shortlist platform

For

Travellers and locals choosing where to eat and what to visit in a city

Solves

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.

Delivers

Transparent shortlist of dining and local places with source-linked reasons

Built in

about 5 weeks of creation time, MVP in 5 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce the time to a shortlist that matches stated taste and occasion while keeping the reasons visible.

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.
13. Compare the reviewed result with the recorded baseline and value assumptions.
14. Capture corrections and named-owner approval before consequential use.
15. Export a versioned transparent shortlist of dining and local places with source-linked reasons with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Stated preferences, occasion, budget, location and permitted review and social sources

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect stated preferences

4. 3

   Occasion

5. 4

   Budget

6. 5

   Location and permitted review and social sources

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Transparent shortlist of dining and local places with source-linked reasons

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: capture stated taste, occasion, budget and location; learn preferences through swipe or quick-choice cards. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will travellers and locals choosing where to eat and what to visit in a city use it to solve "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"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Shortlist acceptance rate and time to a booked or visited place.
4. Measure, then decide. Track shortlist acceptance rate and time to a booked or visited place; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around transparent shortlist of dining and local places with source-linked reasons. Retain the explicit scope boundary: One fixed city set and permitted source set; final booking and visit decisions remain with the user.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed city set and permitted source set; final booking and visit decisions remain with the user.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined asset package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist design separately. These are test prices, not market benchmarks. Package the initial sale as one bounded transparent shortlist of dining and local places with source-linked reasons. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce the time to a shortlist that matches stated taste and occasion while keeping the reasons visible. Demonstrate a concrete transparent shortlist of dining and local places with source-linked reasons using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Travellers and locals choosing where to eat and what to visit in a city professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample transparent shortlist of dining and local places with source-linked reasons from a small authorized input set, with a transparent calculation of shortlist acceptance rate and time to a booked or visited place and no promised savings.

The first 30 days

1. Week 1: interview five travellers and locals choosing where to eat and what to visit in a city and inspect a recent example of dining choices spread across review sites, social feeds and map apps, so matching a personal taste and occasion to a place takes repeated manual browsing.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure shortlist acceptance rate and time to a booked or visited place, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Shortlist acceptance rate and time to a booked or visited place. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Shortlist acceptance rate and time to a booked or visited place; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs transparent shortlist of dining and local places with source-linked reasons. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved styles, production constraints and review examples, together with reliable delivery for a narrow creative niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for travellers and locals choosing where to eat and what to visit in a city. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

FindAMeal, Wanderboat 2.0 and TravelMind, plus review sites, social feeds and map apps. Compare this product with the buyer's present method on shortlist acceptance rate and time to a booked or visited place. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, video or image processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of transparent shortlist of dining and local places with source-linked reasons. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.