The same chain is spelled four ways across eight countries. Matching it back together is most of the work, and nobody quotes for it.
ScrapeIt runs the collection as a managed service. You name the countries, cities and categories; we build the pipeline, resolve brands across scripts with a confidence value, preserve the original merchant names, keep currency per row, and hand back CSV, JSON, Excel or a push into your warehouse.
Where a brief spans several countries we deliver them as separate markets with the resolved brand key linking them, so regional aggregation is something you choose rather than something the pipeline assumed.
We collect public catalogue data only and honour the crawl rules published on both domains, pacing requests. No customer data, no driver data, no account access. Terms restrict commercial reuse of the content, so the output is for analysis rather than republication, and your counsel should see the use case before the project starts.
Merchant records carry the merchant name as published, the name transliterated, category, address, city, country, coordinates where available, rating, review count, delivery fee, estimated delivery time and open state.
A brand identifier sits alongside the merchant name, resolved across countries and scripts, with a confidence value. That field is the reason a regional analysis is possible at all, and publishing it with a confidence value rather than as a certainty is the honest way to deliver a match that was inferred.
Menu records are keyed to the merchant: section, item, description, price, currency and option groups with their surcharges.
Currency and country are their own fields on every row. Eight countries mean eight currencies, and a regional price comparison without them is arithmetic on unlike things.
The original script is preserved alongside any transliteration. Overwriting a Thai merchant name with its Latin form destroys the only value that matches the platform, and a client who later needs to reconcile against Grab's own data is left with nothing to join on.
Coverage by city is the most requested extension. Which merchants exist in which cities, how deep each category goes, and where a brand is present in one country and absent in another are questions the merchant list answers cheaply, before any menu collection is needed.
Promotion tracking needs a cadence matched to promotions rather than to convenience, as everywhere in this category. A discount that ran through a lunch service is invisible to a nightly crawl.
Menu localisation analysis is a genuinely interesting output once brands are resolved: which items are common to a chain across the region, which are country specific, and how prices for the identical item differ once currency is handled. That analysis is impossible without the resolution step and trivial with it.
What we avoid is people. Driver identities and locations, customer accounts and order histories are out of scope. Ratings and review counts are collected as merchant attributes; review text is collected only where a client has a use for it and never tied to named individuals.
Grab is Southeast Asia's largest superapp, and food delivery is one vertical inside it alongside rides, payments and financial services. The food surface operates across roughly eight countries: Singapore, Malaysia, Indonesia, Thailand, Vietnam, the Philippines, Cambodia and Myanmar.
Each country runs its own catalogue, currency and language. The merchant surface for Singapore responds directly with substantial content, and crawl rules are published both on the main domain and on the food subdomain.
What makes this source distinctive as a data problem is script. Merchant names appear in Thai, Vietnamese with diacritics, Bahasa and Latin transliteration, and a regional chain appears under all of them. Any analysis that spans more than one country runs into this immediately, and it is not solved by string matching.
Beneath that the structure is familiar: merchant records with category, address, rating, delivery fee and hours, and menu records with sections, items, prices and option groups per merchant.
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Clients ask for menus and prices. What decides whether the dataset works is whether the same chain in Bangkok, Ho Chi Minh City and Jakarta ends up as one brand or three.
String matching does not do it. The same brand is written in a different script per country, transliterated inconsistently, and often suffixed with the outlet location. Resolution has to use several signals together and it has to record how confident it was, because a silent wrong match corrupts a regional average in a way nobody notices until a decision has been made on it.
The second reason is that country differences are real, not noise. Menus are localised, portion sizes differ, and prices reflect local markets, so the same brand genuinely sells different things in different countries. Resolution lets you see that deliberately instead of losing it.
The third is the superapp context. A merchant on Grab exists inside a platform that also handles rides and payments, and merchant records are shared across those surfaces. For a client studying market structure the merchant list is a picture of organised commerce in these cities that goes beyond restaurants.
The fourth is the familiar one on any delivery platform: fees and availability move during the day, and a daily snapshot is one arbitrary moment of that.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, maintains the brand resolution as merchants are renamed and added, and repairs the collector before your regional comparison starts double counting a chain.
You see a sample first, in your format, over the countries you actually trade in, with brands resolved and confidence values visible so you can judge the matching on real merchants rather than on a promise.
With several signals together, not string matching - the same brand is written in a different script per country and transliterated inconsistently. Every match carries a confidence value, because a silent wrong match corrupts a regional average in a way nobody notices until a decision rests on it.
Always, alongside any transliteration. Overwriting a Thai name with its Latin form destroys the only value that matches the platform, and a client who later needs to reconcile against Grab's own data would have nothing left to join on.
Yes, once brands are resolved and currency is on the row. It is one of the more useful outputs here: the identical item priced across markets, plus which menu items are regional and which are country specific. Without the resolution step that comparison cannot be built.
No. Menus are localised, portion sizes differ and prices reflect local markets, so the same brand genuinely sells different things in different countries. We collect countries separately so those differences stay visible rather than being averaged away.
No. Driver identities and locations, customer accounts and order histories are out of scope. Ratings and review counts come through as merchant attributes; review text only where you have a use for it, never tied to named individuals.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
