Full-Stack Developer – AI Global Indian Restaurant Directory | Next.js, Google Places, Maps, PostGIS

Project We own FineDiningIndian.com, an established Indian food, chef, recipe and restaurant editorial website. We now want to transform the website into: Fine Dining Indian The Global Guide to Indian Restaurants The existing editorial content must remain, but we want to add a modern, scalable restaurant discovery platform that automatically discovers Indian restaurants by location and creates rich restaurant profiles using approved third-party APIs. This is not a basic WordPress directory/plugin project. We need a developer or small team experienced in: Next.js / React Node.js / TypeScript PostgreSQL WordPress integration/headless architecture Google Places API Google Maps Tripadvisor API/Terra AI/LLM integration Programmatic SEO Structured data/schema Authentication Stripe SaaS dashboards Geolocation/search The system must be designed to expand globally, although V1 will concentrate primarily on UK Indian restaurants. 1. CORE OBJECTIVE A visitor should be able to visit FineDiningIndian.com and search: Indian restaurants near me or search by: city postcode restaurant name neighbourhood cuisine dining style Example: Indian Restaurants in Leeds The system returns: restaurant list interactive map restaurant ratings Fine Dining Indian Score AI review summary cuisine price indication opening information booking information directions website individual restaurant profile 2. EXISTING WEBSITE FineDiningIndian.com already contains significant existing content including: recipes chefs restaurant reviews Indian restaurant articles guides editorial content We DO NOT want this content deleted. The new restaurant directory should be integrated with the existing domain and content. Preferred structure: finediningindian.com/restaurants/ rather than a separate domain. Existing URLs should be preserved wherever possible to protect existing SEO value. 3. HOMEPAGE Redesign/restructure the homepage around restaurant discovery. Main hero: Discover Exceptional Indian Restaurants Supporting text: Find outstanding Indian restaurants around the world using diner reviews, reputation and Fine Dining Indian insight. Large search: City, postcode, restaurant or location Buttons: Search Use My Location Homepage sections should include: Restaurants Near You FDI Recommended Explore by Cuisine Examples: South Indian Kerala Punjabi Gujarati Bengali Goan Hyderabadi Modern Indian Explore by Experience Fine Dining Casual Dining Date Night Family Business Lunch Tasting Menu Vegetarian Vegan Halal Popular Cities Fine Dining Indian Guides Featured Chefs Recipes For Restaurant Owners 4. RESTAURANT SEARCH Create: /restaurants/ User can enter: city postcode restaurant area Also support: Use My Location The system should obtain browser geolocation after user permission and return nearby Indian restaurants. Search results need: LIST VIEW and MAP VIEW Desktop can display list + map simultaneously. Mobile should allow switching between: List / Map 5. SEARCH FILTERS V1 filters should include where reliable data exists: distance FDI score Google rating Tripadvisor rating cuisine dining style price level open now reservable vegetarian vegan halal fine dining Architecture should allow additional filters later. 6. AUTOMATIC RESTAURANT DISCOVERY This is a critical requirement. We do NOT want to manually enter every restaurant. The system should use the official Google Places API to discover Indian restaurants geographically. Potential discovery terms include: Indian restaurant South Indian restaurant Kerala restaurant Punjabi restaurant Bengali restaurant Gujarati restaurant Goan restaurant Indian fine dining The developer should propose a sensible geographic discovery strategy. For example: Country → City → geographic search areas → Places API → deduplication → restaurant database Google Place ID should be used as a primary external identifier. The system must prevent duplicate restaurants. 7. DATA COMPLIANCE IMPORTANT: We want official APIs used wherever required. Do NOT build the platform around unauthorised scraping of: Google Maps Tripadvisor other protected platforms Developer must understand API caching, storage, attribution and display requirements. We expect the developer to explain how the proposed architecture complies with Google and Tripadvisor API/data policies. 8. RESTAURANT DATABASE Minimum restaurant information should support: internal restaurant ID slug restaurant name Google Place ID Tripadvisor ID address postcode city region/state country latitude longitude telephone restaurant website menu URL booking URL Google Maps/directions link cuisine regional cuisine dining style price level Google rating Google review count Tripadvisor rating Tripadvisor review count opening hours reservable status vegetarian options vegan options halal status where verified/reliable popular dishes AI review summary FDI score claimed/unclaimed verified status data last updated publication/index status Use separate relational tables where appropriate rather than putting everything into one oversized table. 9. TRIPADVISOR INTEGRATION Integrate the current official Tripadvisor developer platform/API where access and licensing allow. The system should attempt to match each restaurant with the appropriate Tripadvisor location. Store the Tripadvisor identifier. Where permitted, retrieve/display relevant information such as: rating review count recent review information restaurant information Matching must be carefully handled because restaurant names may differ between Google and Tripadvisor. Suggested matching factors: restaurant name address postcode coordinates telephone website Provide an admin facility to correct incorrect matches. 10. AI RESTAURANT CLASSIFICATION Use AI to classify restaurant information. Possible inputs: restaurant name available description website information menu information where legally/technically accessible review signals/content where API terms permit Output structured classifications such as: Cuisine Kerala Tamil Punjabi Gujarati Bengali Goan Hyderabadi Modern Indian etc. Experience Fine Dining Casual Family Date Night Business Tasting Menu etc. Popular Dishes AI should identify frequently praised or mentioned dishes when sufficient data exists. AI-generated classifications must include confidence values where appropriate. Low-confidence classifications should be sent for admin review rather than treated as facts. 11. AI REVIEW INTELLIGENCE We want our own useful restaurant summary. Example: What Diners Say “Guests consistently praise the restaurant's regional cooking, friendly service and lively atmosphere. Lamb dishes and biryani receive particularly strong feedback. Some diners mention longer waits during peak weekend periods.” Also classify sentiment: Food Service Atmosphere Value Waiting Time Example: Food: 94/100 Service: 88/100 Atmosphere: 91/100 Value: 84/100 AI must NOT invent information. Where insufficient review/data exists, display: Not enough data yet rather than generating unsupported claims. 12. FINE DINING INDIAN SCORE Create a proprietary: FDI SCORE /100 The scoring engine should be configurable from the admin dashboard. Initial factors may include: Google rating Tripadvisor rating review volume review recency food sentiment service sentiment atmosphere sentiment consistency Fine Dining Indian editorial input Do NOT hard-code the weighting permanently. Admin should be able to change weighting later. The algorithm should account for review volume. For example: 4.9 from 15 reviews should not necessarily rank above 4.7 from 3,000 reviews. Full calculation should occur server-side. 13. INDIVIDUAL RESTAURANT PAGE Example: /restaurant/example-indian-restaurant-leeds/ Each page should contain: Restaurant name Location Cuisine Dining style FDI Score Example: 92/100 – Exceptional Google Rating + review volume Tripadvisor Rating + review volume What Diners Say AI summary Sentiment Food Service Atmosphere Value Popular Dishes Good For Date Night Family Business Celebration etc. Restaurant Information Address Map Telephone Opening hours Price level Cuisine Dietary information Booking BOOK A TABLE Website VISIT WEBSITE Menu VIEW MENU Directions GET DIRECTIONS Nearby Indian Restaurants Similar Restaurants Fine Dining Indian Editorial Content Where an existing FDI article/review relates to the restaurant, connect it to the new restaurant profile. 14. WALK-IN / BOOKING Where reliable data exists, show: Booking recommended Reservations available or similar. Do NOT have AI claim that walk-ins are accepted unless reliable data supports it. Restaurant owners should later be able to update this information after claiming their listing. 15. CITY PAGES Create scalable city landing pages. Examples: /restaurants/uk/london/ /restaurants/uk/leeds/ /restaurants/uk/manchester/ Each page should contain: unique title/H1 useful city introduction restaurant count map restaurant results FDI rankings cuisine categories relevant editorial content internal links Example title: Best Indian Restaurants in Leeds – Ratings, Reviews & Booking 16. COUNTRY PAGES Examples: /restaurants/uk/ Later: /restaurants/india/ /restaurants/uae/ /restaurants/usa/ Architecture must therefore support international: currencies address formats timezones geographic regions localisation V1 can initially display English. 17. PROGRAMMATIC SEO This is extremely important. We want scalable SEO but DO NOT want millions of low-quality autogenerated pages. Only selected valuable combinations should become indexable URLs. Examples: /restaurants/uk/london/ /restaurants/uk/leeds/ /cuisine/kerala/london/ or an agreed SEO-safe structure. Search/filter parameter combinations should NOT automatically become indexable pages. Developer should implement: canonical tags index/noindex rules pagination strategy XML sitemaps robots rules breadcrumbs internal linking server-rendered SEO content metadata Open Graph structured data 18. PUBLICATION QUALITY THRESHOLD Automatically discovered restaurants should NOT automatically create poor-quality indexable pages. Create a publication/indexing quality system. Example requirements: valid restaurant name valid location Google Place ID currently operating sufficient confidence it is an Indian restaurant minimum useful restaurant information unique profile information no duplicate restaurant Restaurant can exist in the database/search before its page qualifies for Google indexing. Admin should be able to control: Published Unpublished Index Noindex 19. STRUCTURED DATA Implement appropriate Schema.org structured data. Restaurant profiles should support relevant: Restaurant / LocalBusiness schema. Also consider where appropriate: BreadcrumbList ItemList Article Person Recipe Structured data must follow current Google guidelines. Do not falsely represent third-party ratings as Fine Dining Indian's own reviews. 20. EXISTING CONTENT INTEGRATION FineDiningIndian.com already has years of: restaurant reviews chef profiles recipes articles We want relationships between these. Example: Restaurant Gymkhana ↓ Related chef ↓ Related Fine Dining Indian articles ↓ Related cuisine ↓ Related city This should create strong internal linking. Existing URLs should not be changed unnecessarily. 21. CLAIM THIS RESTAURANT Every unclaimed restaurant page should show: Own or manage this restaurant? CLAIM THIS LISTING Restaurant owner creates an account. Collect: name email restaurant role telephone verification information Admin must approve claims in V1. Do not over-engineer automatic verification initially. 22. OWNER DASHBOARD After an approved claim, owner should be able to manage permitted information such as: description cuisine menu URL booking URL website dietary information booking guidance restaurant features Changes to sensitive/core imported information may require admin approval. Owner dashboard should also show basic analytics. Example: Profile Views Website Clicks Booking Clicks Direction Clicks 23. LISTING LEVELS Architecture should support: FREE Basic listing. VERIFIED Enhanced claimed profile. PRO Premium restaurant profile and analytics. Exact prices may change. Do not hard-code subscription amounts throughout the application. Admin should be able to configure plans. 24. STRIPE Integrate Stripe for restaurant subscriptions. Requirements: monthly subscription annual subscription capability trial capability upgrade/downgrade cancellation billing portal webhook handling payment status failed payment handling System architecture should support multiple currencies later. Initial primary currency: GBP 25. RESTAURANT STATUS Support: Unclaimed Claimed Verified FDI Recommended Potentially later: FDI 90+ Badges should be configurable. 26. CLICK TRACKING Track important conversion events: restaurant profile view Book Table click Website click Menu click Directions click telephone click Claim Listing click Store: restaurant event timestamp referral/source where appropriate anonymous session information where privacy compliant This data will become important for proving value to restaurants. 27. ADMIN DASHBOARD Super-admin should be able to: Restaurants Search Edit Publish/unpublish Index/noindex Merge duplicates Delete/archive Mark closed Override classifications Correct Tripadvisor match Restaurant Claims Approve Reject Review FDI Score Change scoring weights. AI Regenerate summary Approve/edit summary Review low-confidence classifications Cities Create/edit city Set index/noindex Add editorial introduction Cuisines Create/edit cuisine categories. Users Manage restaurant owners. Subscriptions View subscription status. Analytics View: restaurant views booking clicks website clicks direction clicks top restaurants top cities popular searches 28. SEARCH ANALYTICS Store anonymised site search information. We want to understand queries such as: “Indian restaurant Leeds” “Kerala restaurant London” “best dosa Manchester” This will eventually provide valuable market intelligence. Admin dashboard should show: Top Searches Zero Result Searches Top Locations Popular Cuisine Searches 29. SEARCH TECHNOLOGY Developer should recommend the appropriate search engine. For V1 possibilities include: PostgreSQL/PostGIS search Typesense Meilisearch Algolia Search should eventually handle: geographic radius typo tolerance restaurant names cuisine city neighbourhood dishes ranking filters Please explain your recommendation. 30. GEOSPATIAL DATABASE We strongly prefer proper geographic search capability. PostgreSQL + PostGIS is preferred unless the developer recommends a stronger alternative. Need: Restaurants within X miles/km and: Restaurants near coordinates 31. PERFORMANCE Restaurant and city pages need to be fast. Use: server-side rendering/static generation where appropriate sensible caching image optimisation CDN lazy loading API request optimisation We do NOT want every website visit triggering expensive Google/Tripadvisor API requests. Developer should propose an API refresh/caching strategy that complies with applicable API terms. 32. API COST CONTROL This is essential. We do not want uncontrolled API bills. Implement: request logging usage monitoring scheduled refresh field selection caching where permitted API limits retry handling error handling Admin should be able to understand approximately how much third-party API activity the system is generating. 33. BACKGROUND JOBS System should support scheduled/background jobs for: restaurant discovery data refresh AI classification AI summaries score recalculation duplicate detection sitemap generation/update closed restaurant checking Jobs should have logging and failure/retry handling. 34. SECURITY Implement: secure authentication role-based permissions protected admin routes secure API keys/secrets server-side validation rate limiting secure Stripe webhooks audit/logging where appropriate Roles: Visitor Restaurant Owner Admin Super Admin 35. GDPR / PRIVACY Because FineDiningIndian.com operates from the UK, development should support appropriate GDPR/privacy practices. Avoid collecting unnecessary personal information. Analytics/search tracking should be designed with privacy in mind. 36. PREFERRED TECHNOLOGY We are open to recommendations, but preferred architecture is approximately: Frontend Next.js React TypeScript Backend Node.js / TypeScript Database PostgreSQL + PostGIS Existing editorial WordPress retained/integrated where sensible. Authentication Developer recommendation. Payments Stripe Maps/Data Google Maps Platform / Places API Reputation Tripadvisor's current official API platform, subject to access/terms. AI OpenAI or appropriate LLM provider. Hosting Vercel + suitable managed database/backend infrastructure, or developer recommendation. Do not propose a cheap collection of WordPress plugins unless you can demonstrate that it will support the scale and functionality above. 37. V1 LAUNCH MARKET Build database/application architecture globally. Initial restaurant population: United Kingdom Priority cities: London Birmingham Manchester Leeds Leicester Liverpool Bristol Nottingham Edinburgh Glasgow Once the system works correctly, we will expand internationally. 38. UX REQUIREMENT The product should feel closer to: modern restaurant/travel discovery software rather than: traditional business directory We want: premium clean mobile-first visual fast simple Restaurant discovery should be the main focus. 39. V1 DELIVERABLES The completed V1 should include: Existing FineDiningIndian.com integration New restaurant-focused homepage Restaurant search Near Me/geolocation List + map results Search filters Restaurant database Google Places integration Tripadvisor integration subject to API approval/access Automatic restaurant discovery Duplicate prevention AI restaurant classification AI review intelligence FDI Score Restaurant profile pages City pages Country architecture Programmatic SEO system Schema/structured data Sitemap/indexing system Existing editorial integration Claim Restaurant Restaurant owner accounts Owner dashboard Basic analytics Stripe subscriptions Admin dashboard Search analytics Background processing API usage/cost controls Responsive mobile/desktop UX Production deployment Documentation 40. NOT REQUIRED FOR V1 Please do NOT inflate the quote by adding: native iOS application native Android application our own complete restaurant reservation engine POS integration loyalty programme delivery ordering social network restaurant review collection platform Architecture should allow future expansion, but these are outside V1. 41. DEVELOPMENT MILESTONES Please quote using milestones rather than one large payment. Suggested structure: Milestone 1 – Technical Discovery & Architecture audit existing FineDiningIndian.com database design architecture API assessment URL/SEO migration plan wireframes final technical specification Milestone 2 – Restaurant Data Engine database Google Places geographic discovery deduplication data enrichment background jobs Milestone 3 – Consumer Directory search geolocation filters map restaurant pages city pages responsive UI Milestone 4 – AI + FDI Score restaurant classification review intelligence sentiment popular dishes scoring engine Milestone 5 – SEO & Existing Content Integration programmatic SEO schema sitemaps canonical/index controls WordPress/editorial integration internal linking Milestone 6 – Restaurant Owner SaaS claim flow authentication owner dashboard listing management analytics Stripe Milestone 7 – Admin, QA & Launch admin dashboard testing security performance mobile QA SEO QA production deployment documentation source-code handover 42. OWNERSHIP This is important. Upon payment, we require ownership of: complete source code database schema UI created specifically for the project custom algorithms prompts/workflows deployment configuration documentation Code must be maintained in a Git repository accessible to us throughout development. Developer should disclose all third-party paid libraries/services before using them. No proprietary dependency controlled solely by the developer. 43. DOCUMENTATION & HANDOVER At completion provide: installation/deployment documentation architecture documentation database documentation environment variable list API setup instructions background-job documentation admin guide restaurant-owner guide backup/restore procedure We must be able to hire another competent developer later without being dependent on the original developer. 44. WHAT TO INCLUDE IN YOUR UPWORK PROPOSAL Please DO NOT send a generic AI-generated proposal. Start your application with: FDI DIRECTORY Then answer these questions: 1. Show us the closest directory, marketplace, local search, travel, restaurant or SaaS platform you have personally built. Provide a live URL if possible. 2. Have you worked with Google Places API? Explain exactly what you built. 3. Have you worked with geospatial/PostGIS search? Give an example. 4. How would you integrate our existing WordPress content with a Next.js restaurant directory while keeping everything under FineDiningIndian.com? 5. How would you automatically discover Indian restaurants across the UK without creating duplicates? 6. How would you prevent Google/third-party API costs from becoming excessive? 7. How would you match the same restaurant between Google and Tripadvisor? 8. How would you prevent thousands of thin automatically generated pages damaging SEO? 9. What search technology would you recommend and why? 10. What architecture would you use for background restaurant discovery and updates? 11. What parts of this project do you believe are technically risky? 12. Provide your proposed: Timeline Milestones Fixed-price estimate Ongoing monthly infrastructure estimate 45. DEVELOPER PROFILE WE WANT Ideal developer/team has demonstrated experience in several of: marketplace/directory products restaurant/travel products Google Places Maps geospatial search Next.js PostgreSQL/PostGIS large datasets AI/LLMs programmatic SEO SaaS Stripe WordPress/headless WordPress A beautiful portfolio alone is not sufficient. We need someone who understands: data architecture + SEO + APIs + SaaS + scalable product development. 46. FUTURE ROADMAP V1 should be designed so later phases can introduce: worldwide restaurant discovery direct reservation availability booking commissions FDI awards FDI Recommended badges restaurant review/reputation dashboard competitor intelligence review response AI restaurant marketing tools consumer accounts/favourites personalised restaurant recommendations mobile applications multilingual pages additional countries/currencies These are NOT part of the V1 quote unless specifically agreed. FINAL PROJECT VISION We are not trying to build another generic restaurant directory. Our goal is to transform FineDiningIndian.com into: The Global Guide to Indian Restaurants A customer should eventually be able to search anywhere in the world and discover Indian restaurants using: location + maps + Google reputation + Tripadvisor reputation + Fine Dining Indian scoring + AI diner intelligence + cuisine expertise + booking links. The public directory will remain useful to consumers while restaurant owners can claim and enhance their profiles and access paid business tools. If you have experience building scalable location/search products and understand both SEO and API-driven applications, we would like to hear from you.