Romania is not one delivery market. Bucharest prices and Cluj prices diverge enough that a national average describes neither city.
ScrapeIt runs the collection as a managed service. You name the cities and verticals; we build the pipeline with option surcharges as their own rows, city and vertical as fields, fees on the venue record, and hand back CSV, JSON, Excel or a push into your warehouse.
Cadence is set against what you are watching - menu structure moves slowly, prices and fees daily, promotions within the day - rather than one schedule that suits none of them.
We collect public catalogue data only, honour the crawl rules and pace requests. No courier data, no customer data, no account access. Terms restrict commercial reuse, so the dataset is for analysis rather than republication, and your counsel should see the use case before the project starts.
Venue records carry the venue name, category, city, address, rating, delivery fee, minimum order, estimated delivery time and open state.
Menu records are keyed to the venue: section, item name, description, price in lei, and the option groups with their own surcharges. Options are separate rows rather than folded into the price, because an item with a mandatory size choice is not the price its base row shows.
City is a first class field rather than something inferred later. It is the axis the platform is built on and the axis on which Romanian delivery pricing actually varies.
Vertical is recorded per venue - restaurant, grocery, pharmacy, retail - so a client analysing restaurant pricing does not silently include a supermarket's catalogue in the average.
Every row carries the collection timestamp and the currency, since prices are in lei and a series spanning any length of time needs both.
City comparison is the most immediately useful output: the same chain priced across Romanian cities, with delivery fees on the row, which for a brand operating nationally is a direct read on where its franchisees or partners are pricing differently.
Promotion tracking needs a cadence matched to promotions rather than to convenience, as on any delivery platform. A discount that ran over a lunch service is invisible to a nightly crawl.
Venue coverage mapping answers which categories are thin in which cities, which is an expansion question and runs off the venue list alone without any menu collection.
What stays out of scope is people: no courier data, no customer accounts, no order histories. The crawl rules already disallow the account paths and we would not go near them regardless.
Tazz is one of Romania's largest delivery platforms, carrying restaurants, groceries, pharmacies and retail across the country's major cities. It is part of the eMAG group, which is the dominant e-commerce operator in the region.
The catalogue is organised by city, and city pages respond directly with a great deal of content. That structure matters because Romanian delivery pricing varies substantially between the capital and the regional cities, and the venue base differs even more.
Like most platforms of this kind it carries several verticals side by side, and they are different data - a restaurant menu has sections and options, a grocery listing has brands and pack sizes, a pharmacy listing has its own rules. Typing them separately is the first design decision.
Crawl rules are published and disallow the cart, account and password reset paths - none of which a catalogue project has any business touching.
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Romanian delivery data gets treated as a simpler version of a Western European brief, and it is not - it has the same structural traps with less published analysis to catch mistakes.
City variation is the first. Bucharest has a venue base and a price level that the regional cities do not, and a national aggregate is dominated by the capital while describing nowhere. Collecting city as a field costs nothing and is the difference between a usable dataset and an average.
Option surcharges are the second, and they behave here as everywhere: a base price with a mandatory choice attached is not the price a customer pays. Flattening them produces a price index that is wrong by a consistent margin in whichever direction the flattening chose.
The third is vertical mixing. A platform carrying restaurants and supermarkets in one interface will, if collected naively, put a kilo of potatoes and a restaurant main course in the same price column. Typing per vertical is what keeps aggregates honest.
The fourth is that this is one platform in a market with others. It is a large slice of Romanian online delivery and it is not the Romanian food market, most of which is still offline, and we describe it that way.
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 city coverage and menu templates change, and repairs it before a price series develops a gap.
You see a sample first, in your format, over the cities you actually trade in, with options expanded and fees on the row so you can judge the comparison on real venues.
You can, and it will be dominated by Bucharest while describing nowhere in particular. City is a field on every row precisely so you can aggregate deliberately rather than inherit the capital's weight by accident.
As separate rows with their surcharges. An item with a mandatory size choice is not the price its base row shows, and flattening them gives a price index that is wrong by a consistent margin in whichever direction the flattening went.
Not in our output - vertical is recorded per venue. Collected naively, a kilo of potatoes and a restaurant main course end up in the same price column, which quietly ruins every aggregate built on it.
No. It is a large slice of Romanian online delivery. Most of the market is still offline with different pricing, and we describe the dataset as what it is rather than letting one platform stand in for a country.
No. The crawl rules disallow the account paths and we would not go near them regardless. Venue, menu, price, fee and availability data is what these projects need and it contains none of that.
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
