How to Build an App Like Zomato: Food Discovery and Delivery Platform Guide

Zomato started as a restaurant discovery platform -- essentially a digital restaurant guide with photos, menus, and reviews. It added delivery later. The discovery side attracts users through content quality. The delivery side generates revenue through transaction commissions.

If you are building a food tech platform, you are choosing which side to build first. Both have different requirements, different operational models, and different tech stacks. This guide covers the full picture.

The two sides of a Zomato-style platform

Discovery side (content-heavy):

• Restaurant profiles: photos, menus, hours, location

• Reviews and ratings from diners

• Search and filter by cuisine, location, price range, rating

• Editorial content (best restaurants lists, new openings, seasonal guides)

Discovery attracts users organically (SEO-friendly, shareable content). Revenue comes from restaurant advertising and subscription plans.

Delivery side (commerce-heavy):

• Delivery ordering from listed restaurants

• Real-time order tracking

• Rider dispatch and management

• Split payments (customer, restaurant, rider, platform)

Delivery generates direct commission revenue but requires operational infrastructure (riders, zone coverage) that discovery does not.

Core product architecture

Restaurant listing and discovery

Restaurant profiles need: name, cuisine type, location and map, contact, hours of operation, photos (interior, food, menu), full menu with prices, and aggregate rating.

The menu is the most data-intensive piece. Restaurants change menus constantly. Your restaurant-facing management tool needs to make menu updates easy enough that restaurants do it themselves -- without relying on your operations team for every change.

Search and filter: by cuisine, location proximity, rating threshold, price range, and delivery availability. Geographic search (restaurants within 5km of the user's location) requires PostGIS or equivalent spatial indexing.

Reviews and ratings

Post-order ratings (1-5 stars, required) and optional written reviews (moderated). Ratings aggregate to an overall restaurant score. The score is the primary quality signal users use for decisions -- protect its accuracy aggressively.

Anti-fraud measures: only verified orders generate ratings. Rate-limit reviews per user. Flag suspicious patterns (sudden surge in positive reviews from new accounts).

Ordering and checkout

Restaurant menu display, cart, item customization (extras, notes), delivery address selection, fee calculation (delivery fee, platform fee, taxes), and payment. Coupon codes and loyalty credits in v2.

Delivery dispatch

When an order is placed: notify the restaurant, dispatch a rider when food is estimated to be ready, rider picks up, delivers, confirms delivery. Real-time tracking throughout.

The dispatch algorithm needs: rider location and availability, estimated restaurant prep time (confirmed or estimated from historical data), delivery distance and estimated time.

Rider app

Separate app for delivery riders: accept/decline assignments, navigate to restaurant and customer, confirm pickup and delivery, see earnings. Same core requirements as DoorDash's Dasher app.

Restaurant management dashboard

Restaurants need to: receive and confirm orders, update prep times, mark items unavailable, manage their menu, view order history, and see payout reports.

This dashboard runs on a tablet in a busy kitchen environment. It must be fast, simple, and reliable. Complicated UX leads to missed orders, which leads to customer complaints.

The restaurant onboarding problem

Without quality restaurant data, the platform has nothing to offer users. Restaurant onboarding is a business development problem before it is a technology problem.

What you need for each restaurant:

• Basic info (name, address, hours, contact)

• Menu (items, descriptions, prices, photos)

• High-quality food photos

Getting this data requires either: outbound sales and onboarding teams (expensive, doesn't scale), self-service onboarding with guidance (slower adoption, lower data quality), or scraping and enriching from public sources (legal risks, data quality issues).

For launch: target restaurants within a specific geography and cuisine category. Onboard 50-100 restaurants manually before opening self-service. Quality over quantity in the launch phase.

Search and discovery mechanics

Geographic clustering is the primary discovery mechanic: "restaurants near you." The quality of the search experience determines how many users convert from browsing to ordering.

Search requires:

• Full-text search across restaurant names, cuisines, and dish names

• Location-based radius filtering (show only restaurants that deliver to the user's address)

• Filtering by cuisine, dietary requirements (vegetarian, vegan, halal), price range, rating

• Sorting by relevance, rating, delivery time, or distance

Elasticsearch handles the full-text and structured filter combination well. PostGIS handles the geographic radius queries.

Editorial and content layer

Zomato's media side -- curated lists, reviews by food critics, seasonal guides, new restaurant features -- drives organic traffic and brand credibility. This content is search-engine-indexed, shared on social media, and positions the platform as an authority rather than just a marketplace.

For v1: automated restaurant pages optimized for "restaurants in [city] [cuisine]" searches. For v2: editorial content produced by your team or partnered food writers. For scale: user-generated content programs.

Revenue model

Delivery commission: 15-25% of delivery order value. The primary revenue driver. Collect via the checkout flow and pay out the restaurant's share net of commission.

Restaurant advertising: Promoted placement in search results and category pages. Restaurants pay per click or per impression. Requires significant traffic to generate meaningful advertiser interest.

Subscription plans: Restaurant monthly subscription for featured placement, analytics, and additional tools. Predictable revenue; easier to sell than per-performance advertising.

Tech stack

Cost to build

How RaftLabs approaches this

Food tech platforms have a supply-side problem (restaurants) and a demand-side problem (users) that need to be solved in parallel. Technology does not solve either -- it enables the business model once supply and demand are established.

We help define the geography, the restaurant category, and the delivery zone radius before writing a line of code. A tight geographic launch (one neighborhood, one cuisine category) with high restaurant coverage performs better than a broad launch with thin coverage everywhere.

If you are building a food platform and want to understand the right scope for your market, let's talk.