A working prototype and phased build plan for transforming FineDiningIndian.com from an editorial site into an AI-powered restaurant directory, without losing a single article, chef profile, or ounce of SEO equity you've already earned.
FineDiningIndian.com is already a real, established editorial property, with years of chef profiles, recipes, restaurant reviews and guides, built on WordPress. That content, and the SEO equity behind it, is not something to route around; it's the foundation the new product has to sit on top of.
The ask isn't a WordPress directory plugin. It's a scalable, geospatial restaurant discovery engine, automatically populated via the Google Places API, cross-referenced with Tripadvisor, classified and summarised with AI, and scored with a proprietary Fine Dining Indian Score, that lives at finediningindian.com/restaurants/ alongside the existing editorial content, not on a separate domain.
V1 focuses on the UK market across ten priority cities, but the data model, URL structure and search architecture need to support country and city expansion from day one, because retrofitting internationalisation later is far more expensive than designing for it now.
Underneath the consumer-facing product sits a second product: a SaaS dashboard for restaurant owners to claim, verify, and manage their listings on FREE / VERIFIED / PRO tiers via Stripe. That's the part that eventually pays for the platform.
Restaurants are found automatically via Google Places geographic search, never manually entered, with strict deduplication using Place ID as the primary key.
AI review summaries and classifications must be grounded in real signal. Where data is thin, the product says "not enough data yet." It never fabricates a claim.
Programmatic scale without doorway pages: a publication-quality gate decides what's indexable, with canonical, noindex and sitemap logic built in from V1.
Google/Tripadvisor calls are cached, scheduled and logged, not fired on every page view, so API spend stays predictable as the directory scales.
We pulled the real Fine Dining Indian mark, palette and typography, the interlocking orange/green rings, Merriweather headlines, Catamaran body text, the black-and-gold accent from your subscribe bar, directly from finediningindian.com. Nothing here is a generic template.
Diner reviews, reputation & Fine Dining Indian insight, worldwide.
This is a real, navigable front-end prototype, not a static mockup. Switch tabs to move between the redesigned homepage, restaurant search with map + list view, and an individual restaurant profile with live FDI Score and AI review intelligence.
Find outstanding Indian restaurants around the world using diner reviews, reputation and Fine Dining Indian insight.
Twelve regional cuisines mapped from Kerala to Kashmir, each one a scalable landing page.
Ranked by the Fine Dining Indian Score: Google + Tripadvisor reputation, weighted by review volume and AI sentiment.
Guests consistently praise the kitchen's confident regional cooking and lively room. Expect strong feedback on the wild muntjac biryani and attentive, unhurried service, with a handful of notes about wait times on peak weekend evenings.
Wild muntjac biryani · Kid goat methi · Chilli lamb chop · Godhuma dosa
Each phase produces something reviewable before the next begins. This sequencing exists so risk is retired early: the data engine and SEO-critical architecture come before the polish.
Audit the existing FineDiningIndian.com, design the database and URL/SEO migration plan, assess API access (Google Places, Tripadvisor), and produce a final technical specification and wireframes before any production code is written.
Build the PostgreSQL + PostGIS restaurant database, the Google Places discovery pipeline (country → city → search area → dedupe), and the background jobs that keep it fresh.
Ship the search experience: geolocation, filters, list + map views, plus restaurant profile pages and city pages, fully responsive across desktop and mobile.
Layer in AI cuisine/experience classification, AI-generated review summaries with sentiment scoring, and the configurable Fine Dining Indian scoring engine, with low-confidence results routed to admin review, never published blind.
Implement the publication-quality gate, canonical/index rules, sitemaps and structured data, and connect the new restaurant profiles back to existing FDI articles, chefs and recipes for strong internal linking.
Build the claim-listing flow, owner authentication, the owner dashboard with basic analytics, and Stripe subscriptions across FREE / VERIFIED / PRO tiers with configurable pricing.
Complete the super-admin dashboard, run security/performance/mobile/SEO QA, deploy to production, and hand over full documentation and source-code ownership.
This prototype is a starting point, not a finished product. We'd rather show you direction than describe it. If the approach resonates, we're ready to walk through the architecture, the API cost-control strategy, and the milestone plan in detail on a call.