A client needing a complete, structured snapshot of restaurant menus listed on the Toters app in Lebanon.

Use Case

Menu catalogue analysis

Region

Lebanon

Sources

Toters delivery app

Number of sources

Restaurant & Item Counts

Area by Area

Modifier Groups & Options

Item by Item

Contact Sales

Key Takeaways

Actowiz Solutions ran a once-off Toters menu data scraping project for an existing client, a Lebanon-based quick-commerce and delivery company. We collected restaurants, menu categories, items, prices, modifier groups and options, and menu image URLs from the Toters app, and delivered one linked dataset the client could load straight into its catalogue analysis.

What did the client need?

The client wanted to understand how restaurant menus are structured on Toters, a Lebanese delivery app. They needed every listed restaurant, its full menu, each item's add-ons and choice groups, and the image URL shown for each item.

This was a single snapshot, not ongoing monitoring. The inquiry arrived on 24 August 2026 and work started on 1 September 2026. Volume: [Add restaurant / item count from delivery team].

What made this hard?

App-first platform: Toters is used mainly through its mobile app, so data had to be collected from app-rendered storefronts rather than simple web pages.
Nested modifiers: one item can carry several choice groups (size, sauce, extras), each with priced options. Flattening them without losing the parent–child link is a typical challenge.
Currency display: Lebanese menus can show prices in LBP, USD or both. Reporting has noted that some Lebanese restaurant prices became dollarised, so currency had to be captured per price, not assumed.
Area-dependent listings: which restaurants appear can depend on the delivery address, so coverage needed several area anchor points.

How did Actowiz solve it?

We built a one-time app data pipeline that captured the full menu tree for each restaurant and kept every level linked.

Approach

Area-based discovery: we set delivery locations across the client's target areas and recorded every restaurant returned, deduplicating by restaurant_id.
Full menu tree capture: for each restaurant we collected categories, items, descriptions and prices, using our mobile app data extraction workflow.
Modifier normalisation: each modifier group and option became its own row, keyed to the item, with its price add-on.
Currency tagging: every price carries a currency code exactly as displayed, with no conversion applied.
Image URLs only: we stored each item's displayed image URL for internal catalogue analysis; no images were licensed or redistributed.
QA pass: automated checks for orphan modifiers, empty prices and duplicates, plus manual spot checks.

Data Attributes / Fields

Field	Description
restaurant_id / restaurant_name	Platform restaurant ID and display name
area / cuisine_tags	Delivery area and cuisine labels shown on the app
menu_category	Section of the menu, e.g. Burgers or Drinks
item_name / item_description	Menu item title and description
item_price / currency	Displayed base price and currency code (LBP or USD)
modifier_group	Choice group name, e.g. "Choose your sauce"
modifier_option / modifier_price	Option name and its price add-on
item_image_url	URL of the item image as displayed in the app
scrape_date	Date the record was collected

What were the results?

One linked catalogue: restaurants, menu items and modifiers were organised into related tables with consistent IDs, enabling easier joins and structured catalogue analysis.
Modifier visibility: structured modifier groups and options allow the client to compare how restaurants organise add-ons, customisations and customer choices.
Clean currency data: prices are tagged by currency, allowing LBP and USD listings to be analysed separately and reducing currency-related inconsistencies.
Image coverage: item image URLs are included where available, supporting visual menu reviews and product catalogue validation.
Fast turnaround: the structured dataset supports a streamlined handover for analysis and integration, with the final delivery timeline dependent on the agreed project scope.

Project at a Glance

Metric	Value
Use Case	Menu catalogue intelligence for a quick-commerce company
Region	Lebanon
Sources	Toters delivery app
Metrics	Restaurants, items, prices, modifier groups and options, image URLs
Reconciliation / Method	Area-based discovery, dedupe by restaurant_id, item-to-modifier keys
Cadence	One-time snapshot (kickoff 1 Sep 2026)
Output	[Confirm delivery format, e.g. CSV / JSON / Excel]

Client Feedback

“Actowiz Solutions delivered a well-structured dataset of restaurant menus from the Toters app, including restaurant details, menu items, nested modifiers, and menu image URLs. The organized output made it easier for our team to analyze restaurant offerings, understand menu customization options, and support our quick-commerce market research in Lebanon.”

— Product Manager, Quick-Commerce Company

Frequently Asked Questions

Q: What data can Toters menu data scraping provide?

A: Publicly displayed restaurant details, menu categories, items, descriptions, prices, modifier groups and options, and item image URLs. See our Toters food data scraping service.

Q: Can modifiers and add-ons be captured with their prices?

A: Yes. Each choice group and option is stored with its price add-on and a link to the parent item.

Q: Are menu images included?

A: We deliver the image URLs shown in the app for internal analysis. We do not supply licensed image files.

Q: Can this run on a schedule instead of once?

A: Yes. The same pipeline can refresh weekly or monthly, similar to our food delivery deal and coupon tracking work.

Q: Is the data collection compliant?

A: We collect only publicly available business data, respect site terms and recommend a legal review for each use case. No personal data is collected.

Need Toters Menu Catalogue Data?

Explore our food data scraping services or our guide to scraping food mobile apps. Visit actowizsolutions.com to discuss your data requirement.

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