We extract footwear specifications, apparel sizing grids, dynamic discount pricing, and stock availability from Redtape. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Footwear Listings objects from redtape.com. All fields typed and schema-versioned.
"sku": "RTE3491", "title": "RedTape Men's Black Walking Shoes", "mrp": 5999.0, "selling_price": 1499.0, "discount_pct": 75, "upper_material": "Mesh", "sole_material": "EVA", "sizes_available": "['UK 6', 'UK 7', 'UK 8', 'UK 9', 'UK 10']"
| # | sku | title | category | sub_category | mrp | selling_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Apparel Data objects from redtape.com. All fields typed and schema-versioned.
"sku": "RTA1204", "title": "RedTape Men's Solid Casual Shirt", "fabric": "100% Cotton", "fit_type": "Regular Fit", "pattern": "Solid", "sleeve_length": "Full Sleeves", "mrp": 2499.0, "selling_price": 749.0
| # | sku | title | fabric | fit_type | pattern | sleeve_length |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Discounts objects from redtape.com. All fields typed and schema-versioned.
"sku": "RTE3491", "mrp": 5999.0, "selling_price": 1499.0, "discount_pct": 75, "discount_abs": 4500.0, "flash_sale_badge": true, "price_timestamp": "2026-05-12T10:14:00Z", "currency": "INR"
| # | sku | mrp | selling_price | discount_pct | discount_abs | active_promotions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Stock objects from redtape.com. All fields typed and schema-versioned.
"sku": "RTE3491", "variant_id": "RTE3491-BLK-08", "size": "UK 8", "colour": "Black", "stock_status": "In Stock", "low_stock_warning": false, "scraped_at": "2026-05-12T10:14:33Z"
| # | sku | variant_id | size | colour | stock_status | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Navigation objects from redtape.com. All fields typed and schema-versioned.
"primary_category": "Footwear", "sub_category": "Sports Shoes", "gender": "Men", "collection_name": "Athleisure", "position": 14, "url": "https://redtape.com/mens/footwear/sports-shoes", "scraped_at": "2026-05-12T10:15:01Z"
| # | breadcrumb | category_id | primary_category | sub_category | gender | collection_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our pipeline handles Redtape's dynamic frontend, extracting product specifications, sizing matrices, and deep discount structures at the SKU level.
Extract sole materials, upper construction, closure types, and weight metrics across all shoe categories.
Capture fabric compositions, fit types, patterns, collar styles, and wash care instructions for clothing lines.
Monitor MRP versus selling price, calculating exact discount percentages and absolute savings per SKU.
Map available sizes (UK/US/EU for shoes, S/M/L/XL for apparel) against specific colour variants.
Track out-of-stock indicators and low-stock warnings at the individual size and variant level.
Link parent products to child colour variants, ensuring accurate grouping of related SKUs.
Extract high-resolution product images, lifestyle shots, and sizing chart graphics.
Scrape full breadcrumb trails to classify products accurately within Redtape's taxonomy.
Identify recently added products and track their initial pricing and availability status.
Brief in. Clean data out.
Provide target categories, specific collections, or full catalogue requirements. We design the schema.
We configure Scrapy / Playwright crawlers to handle Redtape's JavaScript pagination and variant selection.
Schema validation, discount calculation checks, and variant mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting accurate retail data requires handling modern JavaScript frameworks and dynamic pricing logic.
Redtape frequently updates pricing via client-side JavaScript. We use Playwright to execute these scripts, ensuring the scraped selling price matches what a real user sees.
Stock status depends on the selected size and colour combination. Our crawlers systematically iterate through these DOM states to capture accurate variant-level availability.
Category pages use infinite scroll or lazy loading. We simulate user scroll behaviour to trigger API calls, ensuring complete extraction of all products within a category.
Marketing overlays and newsletter signups can obstruct DOM elements. Our interaction scripts detect and dismiss these modals before attempting data extraction.
To prevent blocking, we distribute requests across residential proxy pools and implement randomised delays modelled on human browsing patterns.
Retailers monitor Redtape's aggressive discounting strategies to adjust their own promotional calendars.
Merchandisers analyse category depth and variant breadth to inform their own private label development.
Analysts track the frequency and depth of flash sales to understand Redtape's inventory clearance velocity.
Footwear brands track Redtape's new category expansions, such as athleisure and formal wear.
Fashion analysts aggregate colour, material, and pattern data to identify prevailing consumer preferences.
E-commerce platforms ingest Redtape specifications to normalise attributes across third-party sellers.
"Redtape moves millions of units through aggressive discounting and rapid inventory turnover. Tracking their catalogue requires precision extraction at the SKU and size level."
Extracting accurate pricing from Redtape means navigating flash sales, dynamic size-level stock indicators, and complex product variants. DataFlirt handles the JavaScript rendering and pagination logic so your retail analytics team receives clean, normalised data without maintaining custom scrapers.
Everything supported by our redtape.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles orchestration and deduplication. Playwright handles JavaScript rendering, variant selection, and dynamic price hydration.
We maintain pools of residential ISP proxies to avoid rate limits and IP blocking during high-frequency catalogue scans.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling and dependencies. Postgres stores state and diff hashes.
Data delivered to where your team already works — no new tooling required.
About redtape.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and product information is generally permissible under applicable law. DataFlirt extracts only public catalogue data and does not bypass authentication walls or extract personal user data.
We use Playwright to execute client-side JavaScript, ensuring we capture the final rendered price, including active flash sales and promotional discounts.
Yes. Our crawlers iterate through size selectors to capture availability and SKU-specific details for each size variant.
We can configure pipelines for daily full-catalogue refreshes or high-frequency hourly scans for specific high-priority categories or SKUs.
Engagements typically start with a defined set of categories or a minimum of 5,000 SKUs with weekly delivery. Contact us for custom volume pricing.
Yes. We provide a sample extraction of up to 500 SKUs during the scoping phase to validate schema fit and data accuracy.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily pricing feed or a comprehensive catalogue dump, we manage the infrastructure. Tell us your requirements.
