Five restaurants, one kitchen. Count them as five venues and your per-outlet analysis is counting the same stove five times.
ScrapeIt runs the collection as a managed service. You name the brands, cities and localities; we build the pipeline, keep brand and locality as separate fields, derive a kitchen grouping where the data supports it and label it as inferred, and hand back CSV, JSON, Excel or a push into your warehouse.
Enumeration runs from the published sitemap index rather than from guessed paths, which makes coverage checkable rather than hopeful.
We collect public catalogue data only and honour the crawl rules the site publishes - checkout, login, tracking and profile paths are disallowed and we do not go near them. No customer data, no order tracking, no account access. Terms restrict commercial reuse, so the output is for analysis rather than republication, and your counsel should see the use case before the project starts.
Records carry the brand, city, locality, the outlet address where published, and the menu keyed to that combination: section, item, description, price, currency and option groups with surcharges.
A kitchen identifier is derived where the data supports it, grouping brands that share a location. It is an inference and it is labelled as one, with a confidence value, because the platform is built around brands rather than kitchens and pretending otherwise would be presenting a guess as a fact.
Brand and locality are separate first class fields rather than being parsed back out of a URL later, since every useful question here is asked along one of those two axes.
Price sits with its currency and the locality it applies to. The same item under the same brand is priced differently between localities, and dropping the locality makes the price meaningless.
Availability and the collection timestamp complete the row, with the source page address recorded so any figure can be traced back.
Portfolio comparison is the strongest output. One operator, several brands, consistent structure, same localities - that is a controlled comparison of positioning and pricing that is hard to assemble from any marketplace where confounds multiply.
Expansion tracking follows from repeat collection of the brand and locality lists: which brands entered which localities and when. Since brands are launched and retired deliberately here, that list is a readable record of strategy, and it only exists from the point collection starts.
Cross platform comparison is worth adding where a client needs it. These brands also list on the large delivery marketplaces, frequently at different prices, and the gap between an operator's own platform and a marketplace is itself the analysis - it is where commission economics becomes visible.
The limit to state plainly is scope. This is one company's portfolio. For market level questions it belongs alongside the marketplaces rather than standing in for them, and we scope it that way rather than letting a deep dataset be mistaken for a broad one.
EatSure is the ordering platform of Rebel Foods, the cloud kitchen company behind brands including Faasos, Behrouz Biryani, Ovenstory and others. The brands are real, the menus are real, and the restaurants in the usual sense are not: several brands are cooked in the same kitchen and delivered from the same address.
The addressing reflects it. Pages are organised brand first, then city, then locality, so a brand exists across many localities and a locality hosts several brands. A collector that assumes a listing equals a venue will produce a dataset that counts one kitchen as five restaurants.
That matters for anyone doing outlet level analysis. Density, coverage and per-outlet averages all break if virtual brands are counted as separate physical sites, and the error looks plausible because every row is individually correct.
Brand, city and locality pages respond directly with substantial content, crawl rules are published, and a sitemap index is referenced from robots with separate brand, location and product maps - which makes enumeration straightforward rather than a discovery problem.
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Cloud kitchens break the assumption every restaurant dataset is built on, which is that a listing corresponds to a place. Here it corresponds to a brand operating from a place that several other brands also operate from.
If you are studying menu strategy, that does not matter and brand level data is exactly right. If you are studying outlet density, delivery coverage or per-site economics, it matters enormously, and counting brands as outlets inflates every figure in a way that is invisible on inspection because each individual row is correct.
The second thing this source is unusually good for is menu strategy itself. Rebel Foods runs multiple brands deliberately positioned against different cuisines and price points, and having them all on one platform with consistent structure makes portfolio comparison straightforward - what the same operator charges for biryani versus pizza versus wraps, in the same locality, at the same moment.
The third is locality pricing. The same brand prices differently across localities, and with brand, city and locality as separate fields that variation is directly measurable rather than something to be inferred.
The fourth is that this is a single operator. It is a deep look at one company's portfolio, not a picture of the Indian restaurant market, and we describe it that way rather than letting it be read as a market dataset.
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, watches it as brands launch and retire, and repairs it before your expansion timeline loses the months that mattered.
You see a sample first, in your format, over the brands and localities you actually track, with the kitchen grouping and its confidence visible so you can judge the inference rather than take it on trust.
They are real brands with real menus, cooked in shared kitchens. Several brands operate from one address, so a listing is not a venue. For menu strategy that is irrelevant; for outlet density or per-site economics it changes every figure, which is why we model it rather than counting listings.
Where the data supports it, as a derived field with a confidence value and labelled as inferred. The platform is organised around brands rather than kitchens, so presenting the grouping as a fact would be presenting a guess as one.
No, and that is one of the more useful things here. The same brand prices differently across localities, so price travels with its locality and currency on the row. Dropping the locality makes the price meaningless.
No. It is one operator's portfolio, in depth. For market level questions it belongs alongside the large marketplaces rather than standing in for them, and we scope it that way rather than letting a deep dataset read as a broad one.
Yes, and it is usually worth doing. The same brands list on the big delivery platforms at frequently different prices, and the gap between an operator's own platform and a marketplace is where commission economics becomes visible.
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.
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