The site splits its catalogue by prescription status in the URL itself. That is a regulatory boundary, and it should survive into the dataset.
ScrapeIt runs the collector as a managed service. You name the categories, molecules or brands; we build the pipeline with the prescription split preserved, compute unit normalised prices and hand back CSV, JSON, Excel or a push into your warehouse with category type and source surface on every row.
Where the brief covers several pharmacies we collect them on one schema with consistent classification, and surface the places where platforms disagree rather than resolving them silently.
We collect published catalogue and price data only. No customer data, no order information, no reviews tied to identities. If your product could appear to facilitate obtaining prescription medicines without a prescription, settle that with your counsel before the project starts rather than after.
The product record covers the product name, composition where it is a medicine, manufacturer, pack size and unit count, form, listed price, discounted price, discount percentage and currency.
Prescription classification is a first class field taken from the catalogue surface the product was found on rather than inferred from its name. Inference here is unreliable in exactly the cases where being right matters.
Unit normalised price is computed at collection, for the same reason as on any pharmacy source: pack sizes differ and comparing packs compares packaging. Price per tablet or per millilitre travels alongside the raw price.
Category type separates medicines from wellness, personal care and diagnostics, because a vitamin supplement and a prescription antibiotic have nothing in common analytically and mixing them distorts every aggregate.
Availability, manufacturer and the collection timestamp complete the row, with the source surface recorded so the classification is auditable.
Cross platform comparison is the usual brief and it needs consistent classification more than it needs more rows. Two pharmacies compared on unit normalised prices, within matching regulatory classes, is a real analysis; the same two compared on raw pack prices across mixed catalogues is noise with decimal places.
Classification audit is worth running on any multi platform project. Platforms occasionally classify the same product differently, and those disagreements are informative: they flag products where the regulatory status is ambiguous or where one platform is wrong. We surface the disagreements rather than picking a winner silently.
Scope conversations happen at the start. If a product built on this data could appear to facilitate obtaining prescription medicines without a prescription, that is a regulatory problem regardless of how the data was collected, and it is better raised before the pipeline exists than after.
Customer data is out of scope entirely: no orders, no reviews tied to identities, no inference about purchasers. Catalogue and price analysis needs none of it and collecting it would turn a retail dataset into a health data problem.
Netmeds is a large Indian online pharmacy, and it does something structurally useful: it separates its catalogue by prescription status at the URL level, with distinct surfaces for prescription medicines and for products sold without one.
That is a regulatory boundary made visible, and it is worth preserving in any dataset built here. Prescription and over the counter products differ in how they may be sold, how they may be advertised, and what a dataset built on them may reasonably be used for. A collector that merges the two loses a distinction the site itself considered important enough to encode in its addressing.
Beyond medicines the catalogue extends into wellness, personal care and diagnostics, which behave like ordinary retail and should be typed separately again rather than mixed with pharmaceuticals.
Both catalogue surfaces respond directly and crawl rules are published. The prescription surface carries more content, which is consistent with it being the core of the business.
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It would be easy to collect both catalogues, concatenate them and hand over a price list. The result would be technically complete and quietly unusable for anything with a regulatory dimension.
Prescription and over the counter products are governed differently. What may be advertised, to whom, and in what terms differs, and a client building a comparison tool, a price index or a content product needs to know which side of that line each row sits on. The site encodes it in the URL; throwing it away is a decision, not a default.
The second reason is analytical. Price dynamics differ sharply between the two. Over the counter products behave like retail, with promotional cycles and basket effects; prescription medicines are shaped by regulated ceilings and generic competition. Averaging them produces a series that describes neither.
The third is the non medicine catalogue, which is large. Wellness and personal care products will dominate any aggregate drawn across the whole site, and a client analysing pharmaceutical pricing needs them separable rather than silently included.
The fourth, as on any pharmacy source, is that this is one platform and not the market. Online pharmacy pricing is a meaningful slice of Indian retail and it is not offline retail, where most of the volume still sits.
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 the catalogue structure changes, and repairs it before your classification starts drifting.
You see a sample first, in your format, over the categories you actually track, with the prescription split preserved and unit prices computed so you can judge the classification on real products.
Because they are governed differently - what may be advertised, to whom and in what terms - and because their price dynamics differ sharply. The site encodes the distinction in its own addressing; discarding it is a decision rather than a default, and it makes the dataset unusable for anything with a regulatory dimension.
From the catalogue surface the product was found on, not from inference on the name. Inference is unreliable in exactly the cases where being right matters, and the source surface is recorded on every row so the classification can be audited rather than trusted.
They are collected and typed separately. That catalogue is large and will dominate any aggregate drawn across the whole site, so a client analysing pharmaceutical pricing needs it separable rather than silently mixed in with medicines.
Yes, and consistent classification matters more than extra rows. Two pharmacies compared on unit normalised prices within matching regulatory classes is a real analysis. We also surface the places where platforms classify the same product differently, because those disagreements are informative rather than noise.
Not in collecting published catalogue and price information. There can be in what a product built on it appears to do: anything that looks like facilitating access to prescription medicines without a prescription is a regulatory problem regardless of how the data was obtained. We raise it at scoping rather than after the pipeline exists.
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
