Large-Scale Menu & Pricing Extraction Across 16 Markets for a Food Delivery Intelligence Platform

Industry: Food Delivery Intelligence / Market Research

Region: 16 markets across Europe, Middle East, and APAC

Platforms covered: Uber Eats, Deliveroo, FoodPanda, GrabFood, Wolt, Talabat, Zomato, Swiggy, and leading local apps per market

Services used: Food Delivery Data Scraping, Menu Intelligence, Item Matching, API Delivery

The Client

A European company building a customer-facing food delivery intelligence service — comparing restaurants, menus, prices, and fees across delivery platforms so consumers and restaurateurs can see the true cost of ordering on each app.

The Challenge

The product required something no single team could build quickly: complete, current menu and pricing data across 16 markets, where every market has a different platform mix, language, currency, and menu culture.

Specifics that made it hard:

• Scale. 400,000+ restaurant listings and 25 million+ menu items across all markets, with platforms restructuring menus, modifiers, and bundles constantly.
• The same restaurant ≠ the same data. One restaurant on three platforms shows different item names, portion descriptions, prices, and fees. The product's core value — "this exact burger costs €1.40 more on Platform A" — depends on matching items across platforms, in 12+ languages.
• Fees are buried. Delivery fee, service fee, small-order fee, and surge components appear at different stages of each platform's checkout flow and vary by location and time.
• Freshness expectations. Restaurant availability and pricing change daily; a comparison built on month-old menus is worthless.
• Delivery format. The client needed a production API their engineers could build on — not file dumps.

The Solution

Actowiz Solutions designed a multi-market extraction and matching pipeline delivered as a managed data API.

1. Market-by-market platform coverage.

We mapped the dominant platforms in each of the 16 markets and deployed dedicated extraction infrastructure per platform, with location simulation across 300+ city zones so prices and fees reflect what a customer in that area actually sees.

2. Full menu-depth capture.

For each restaurant: name, cuisine tags, ratings, opening status, full menu tree (categories, items, descriptions, prices, modifiers/options, images), plus platform fees captured through checkout-stage simulation — delivery fee, service fee, and minimum order values per zone.

3. Cross-platform restaurant & item matching.

A two-layer matching engine:

• Restaurant layer: geo-coordinates, name normalization, address parsing, and phone/brand signals link the same outlet across platforms.
• Item layer: multilingual title normalization, price-band signals, description similarity, and image hashing match individual dishes — with confidence scores exposed in the API and low-confidence pairs routed to a human QA queue.

4. Refresh & change detection.

Full menu refresh per market every 24–72 hours depending on tier, with daily delta detection so the client's API consumers receive only changes — new items, price moves, availability flips — rather than full re-crawls.

5. Production API delivery.

REST + bulk endpoints in JSON with per-market schemas unified into one global standard, 99.5% uptime SLA, and a staging sandbox for the client's developers.

The Results

After full rollout across all 16 markets:

• 400,000+ restaurants and 25M+ menu items maintained in continuous refresh — delivered through a single API contract instead of 16 separate scraping projects.
• 92% automated item-match rate across platforms (rising to 97% with QA review), enabling the client's headline "same dish, different price" comparisons.
• Fee transparency at zone level: checkout-simulated fee capture revealed effective price gaps of 8–22% between platforms for identical orders — the insight the client's entire marketing launch was built on.
• Time to market: first 4 markets live in 6 weeks; all 16 within 5 months. The client's internal estimate for self-building was 18+ months.
• Zero scraping headcount on the client side; platform changes across 16 markets are absorbed by our maintenance team under SLA.

"We asked for menu data; what we got was a matching engine across 16 markets that became the backbone of our product." — Co-founder, Client

Why It Worked

• Item matching is the product. Anyone can crawl a menu; linking the same dish across platforms and languages is where the value (and difficulty) lives.
• Checkout-stage fee capture. Listed prices are half the story — true comparison requires the fees platforms reveal only at checkout.
• One schema, sixteen markets. Unifying wildly different platforms into a single API contract is what made the data buildable-upon.

FAQs

Which food delivery platforms can Actowiz extract data from?

Uber Eats, Deliveroo, DoorDash, Grubhub, FoodPanda, GrabFood, GoFood, Wolt, Talabat, Careem, Zomato, Swiggy, Didi Food, and leading local platforms per market.

Can you match the same restaurant and dish across platforms?

Yes — multi-signal restaurant matching plus multilingual item matching with confidence scoring and human QA for low-confidence pairs.

How fresh is the menu data?

Tiered refresh from every 24 hours to every 72 hours, with daily change-detection deltas available via API or webhook.

Do you cover APAC markets like Thailand, Singapore, Japan, and Indonesia?

Yes — including FoodPanda, GrabFood, GoFood, Demae-can, LINE MAN, and other regional platforms, with sample datasets available per market.