Price per pack is not price per tablet, and on a market where packs come in fifteens and tens, comparing packs is comparing nothing.
ScrapeIt runs the collector as a managed service. You name the molecules, brands or categories; we build the pipeline, compute unit normalised prices, deliver substitutes as rows and hand back CSV, JSON, Excel or a push into your warehouse with both the listed and the discounted price on every row.
Cadence is set against how fast discounts actually move, which is usually weekly or fortnightly rather than daily. Requests are paced within the crawl rules the site publishes.
We collect published catalogue and price data only. No customer data, no order information, no reviews tied to identities. Prescription status is recorded, and if your product could appear to facilitate purchase without a prescription, that is a regulatory question to settle with your counsel before the project starts.
The product record covers the product name, composition with strengths, manufacturer, pack size and unit count, form, the maximum retail price, the discounted price, the discount percentage and the currency.
Unit normalised price is computed rather than left to the client: price per tablet, per millilitre or per gram as appropriate. Pack sizes vary between ten, fifteen and thirty in this market, so comparing pack prices compares packaging rather than medicine, and every price comparison we deliver carries the normalised figure alongside the raw one.
Substitute relationships are delivered as rows: this product, that product, and the shared composition. That is the field that turns a price list into a competitive map of the generic market.
Prescription status is recorded where published, because it changes both what may be sold and what a dataset may reasonably be used for.
Availability and the collection timestamp sit on every row, since price and stock both move and a price without a time is not a fact about anything.
Substitution mapping is the most valuable output. Once substitutes are rows keyed on composition, a client can ask which molecules have the most brand competition, where price dispersion is widest, and which of their products face the most alternatives at a lower price.
Price series need repeat collection at a sensible cadence. Discounts move, so weekly or fortnightly collection produces a usable series while daily collection mostly records noise. We set it against how fast the prices actually move rather than by default.
Prescription medicines need care in how the dataset is used rather than in how it is collected. The listings are public, but a product built on them that appears to facilitate purchase without a prescription is a regulatory problem in any market. We collect the prescription flag and raise the question at scoping.
What we avoid is anything touching customers: no order data, no reviews tied to identities, no attempt to infer who bought what. A pharmacy dataset that reaches into customer behaviour is a health data problem, and the price and catalogue analysis needs none of it.
PharmEasy is one of India's largest online pharmacies. Product pages carry the medicine, its composition, the manufacturer, the pack size, the maximum retail price and the discounted price actually offered, along with suggested substitutes containing the same active ingredients.
Published price is the reason this source matters. In most pharmaceutical markets retail pricing is opaque or fragmented; here the listed price and the discount sit on the page, which makes real price analysis possible without a survey.
Substitutes are the second structural feature and the one that is genuinely unusual. Indian pharmacy listings routinely suggest alternative brands with the same composition, which exposes the generic substitution landscape directly: which brands compete on the same molecule and at what price gap.
Product pages respond directly with substantial content and crawl rules are published. Category surfaces exist for enumerating the catalogue, and the health care section carries non medicine products with different rules again.
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The first chart anybody builds from pharmacy data compares product prices and shows a scatter that means nothing, because a strip of ten and a bottle of sixty are on the same axis.
Unit normalisation fixes it and has to happen at collection time, when the pack size and the form are on the same row as the price. Retrofitting it later from a price column and a product name is guesswork, and it is guesswork about medicines.
The second reason is the substitute graph. Indian pharmacy competition happens largely between brands of the same molecule, and the platform publishes those relationships directly. Collected as rows, they show which brands compete, how wide the price gap is between the branded original and its alternatives, and how that gap moves - which is the analysis a pharmaceutical company operating in this market actually wants.
The third is the discount, which is close to permanent. The maximum retail price is a regulated ceiling rather than a selling price, and treating it as what people pay produces a market picture nobody would recognise. Both numbers, on the same row, with a timestamp.
The fourth is scope honesty: this is one platform, not the Indian pharmaceutical market. Offline retail dominates and prices differ. A dataset from here is online pharmacy pricing, 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 the catalogue and templates change, and repairs it before your price series develops a hole.
You see a sample first, in your format, over the molecules you actually track, with unit prices computed and substitutes as rows so you can judge the comparison on real products.
Because pack sizes in this market vary between ten, fifteen and thirty, so comparing pack prices compares packaging rather than medicine. Normalisation has to happen at collection time when pack size and form are on the same row as the price - retrofitting it later from a product name is guesswork, and guesswork about medicines.
They are the competitive map of the generic market. The platform publishes alternative brands with the same composition, so collected as rows they show which brands compete on a molecule, how wide the price gap is and how it moves. For a pharmaceutical company operating in India that is usually the point of the whole dataset.
Because the maximum retail price is a regulated ceiling rather than a selling price, and discounting here is close to permanent. Treating the ceiling as what people pay produces a market picture nobody would recognise. Both numbers sit on the row with a timestamp.
No, and we describe it accurately rather than letting it stand in. It is online pharmacy pricing from one platform. Offline retail dominates the market and prices differ. For a fuller picture we collect several platforms, and even then it remains online retail rather than the whole market.
No. No order data, no reviews tied to identities, no inference about who bought what. A pharmacy dataset that reaches into customer behaviour is a health data problem, and price and catalogue analysis needs none of it.
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
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