Nearby place comparison and trip planning workspace

For

Trip planners, concierge teams and event organisers who shortlist nearby venues for groups

Solves

Place research is scattered across map apps, review sites and writing tools, so shortlists, comparisons and the final itinerary never live in one reviewed record.

Delivers

Reviewed shortlist and editable map linked to source records

Built in

about 4 weeks of creation time, MVP in 4 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce venue shortlisting time while keeping every recommendation traceable to a source.

1. Detect the user's current location and search radius.
2. Collect stated interests, group size, budget band and accessibility needs.
3. Search aggregated venue data for nearby attractions and venues.
4. Filter results by niche interests such as ghost walks, secret societies or sunrise viewpoints.
5. Rank candidates against the stated brief and constraints.
6. Show addresses, opening hours, ratings and review excerpts with source links.
7. Compare two or more venues side by side on the stated criteria.
8. Generate a custom digital map of the shortlisted venues.
9. Produce an editable copy of the map for journey planning.
10. Draft venue notes, itinerary text and social posts from the shortlist.
11. Apply templates for blogs, emails and social posts.
12. Suggest grammar and style corrections on drafted text.
13. Support multiple named collaborators on one trip workspace.
14. Export or email the map and itinerary to the client.
15. Compare the reviewed result with the recorded baseline and value assumptions.
16. Capture corrections and named-owner approval before the itinerary is sent.

Everything these tools do, in one app

• Nearby place discovery Finds and explores attractions and venues near a user's location.Found in MapsGPT, Where to?
• Personalized recommendations Tailors suggestions based on the user's input and interests.Found in MapsGPT, Where to?
• Detailed place information Provides addresses, opening hours, ratings, and reviews for locations.Found in Where to?
• Side-by-side comparison Allows users to compare multiple venues side by side to make informed decisions.Found in Where to?
• Custom digital maps Generates a personalized map that visualizes locations matching the search criteria.Found in MapsGPT
• Editable map copies Gives users an editable copy of the generated map to modify details and plan journeys.Found in MapsGPT
• Email delivery Sends the customized map directly to the user's inbox.Found in MapsGPT
• Real-time location services Uses current location to provide accurate recommendations.Found in MapsGPT
• AI-driven search Leverages artificial intelligence to sift through aggregated data and present relevant options.Found in MapsGPT
• Niche interest support Caters to unusual interests such as secret societies, ghost encounters, and romantic sunrise viewing spots.Found in MapsGPT
• Versatile activity planning Supports a wide range of activities from kid-friendly outings to cocktail bars and first date ideas.Found in MapsGPT
• AI content generation Creates high-quality text with minimal input, adapting to different writing styles and tones.Found in Tailbox
• Customizable templates Provides templates to assist with various content types such as blogs, emails, and social media posts.Found in Tailbox
• Grammar and style suggestions Offers real-time corrections and suggestions to improve text quality and readability.Found in Tailbox
• Collaboration tools Enables multiple users to work together on documents.Found in Tailbox
• Platform integration Integrates with popular platforms for seamless workflow management.Found in Tailbox
• Intuitive interface Provides an easy-to-use design suitable for beginners and experienced users.Found in MapsGPT, Tailbox, Where to?
• Regular updates Adds new functionalities and improves performance over time.Found in Tailbox

What goes in, what comes out

The workflow

1. In
   Start with

   Current location, stated interests, venue data and group constraints

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect current location

4. 3

   Stated interests

5. 4

   Venue data and group constraints

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed shortlist and editable map linked to source records

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 city and one venue data source per pilot; opening hours, ratings and availability remain source-verified. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Trip brief and interests, Comparison and map workspace, Client itinerary and delivery. Use a thumbnail gallery for trips, a large central map and comparison canvas, and a right-hand panel for venue details, constraints and comments. Let users compare venues side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant venue or map pin. Make the task-specific outcome reviewed shortlist and editable map linked to source records visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, venue data 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

Authorized venue data sources, map providers and calendar or booking destinations. Cloud asset storage, document import/export and email delivery. 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

   4 days

   One buyer segment, one recurring use case; first modules: detect the user's current location and search radius; collect stated interests, group size, budget band and accessibility needs. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   9 days

   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 trip planners, concierge teams and event organisers who shortlist nearby venues for groups use it to solve "place research is scattered across map apps, review sites and writing tools, so shortlists, comparisons and the final itinerary never live in one reviewed record"?
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: Accepted shortlists per planning hour and corrections after the itinerary is shared.
4. Measure, then decide. Track accepted shortlists per planning hour and corrections after the itinerary is shared; 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 city and one venue data source per pilot; opening hours, ratings and availability remain source-verified. Implement one approved input format, a bounded representative case set and the first two task modules: detect the user's current location and search radius; collect stated interests, group size, budget band and accessibility needs. Support the third module with operator review: search aggregated venue data for nearby attractions and venues. 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 the reviewed shortlist and editable map linked to source records. Retain the explicit scope boundary: One city and one venue data source per pilot; opening hours, ratings and availability remain source-verified.

What the build depends on. Venue data upload and preview, asynchronous search jobs, editable version history, reviewer access and tested export formats. High-fidelity planning requires current opening hours and availability checks. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One city and one venue data source per pilot; opening hours, ratings and availability remain source-verified.

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 trip package. Offer a monthly planning allowance after repeat demand. Quote complex multi-city or large-group work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed shortlist and editable map linked to source records. 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 venue shortlisting time while keeping every recommendation traceable to a source. Demonstrate a concrete reviewed shortlist and editable map linked to source records using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Trip planners, concierge teams and event organisers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample shortlist and editable map linked to source records from a small authorized input set, with a transparent calculation of accepted shortlists per planning hour and corrections after the itinerary is shared and no promised savings.

The first 30 days

1. Week 1: interview five trip planners, concierge teams and event organisers who shortlist nearby venues for groups and inspect a recent example of place research scattered across map apps, review sites and writing tools.
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 accepted shortlists per planning hour and corrections after the itinerary is shared, 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: Accepted shortlists per planning hour and corrections after the itinerary is shared. 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

Accepted shortlists per planning hour and corrections after the itinerary is shared; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed shortlist and editable map linked to source records. 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 venue criteria, group constraints and review examples, together with reliable delivery for a narrow hospitality niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for trip planners, concierge teams and event organisers who shortlist nearby venues for groups. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

MapsGPT, Tailbox and Where to? as the tools buyers use today, plus manual map and review-site research. Compare this product with the buyer's present method on accepted shortlists per planning hour and corrections after the itinerary is shared. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Venue data access, map rendering, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the reviewed shortlist and editable map linked to source records. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, opening-hours accuracy and usage permissions. Named planners approve substantive changes and client delivery scope. One city and one venue data source per pilot; opening hours, ratings and availability remain source-verified. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.