We extract product listings, pricing signals, colour variations, material specifications, and stock depth from Radley. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from radley.co.uk. All fields typed and schema-versioned.
"sku": "H7182001", "product_name": "Dukes Place Medium Ziptop Grab", "collection": "Dukes Place", "price": 199.0, "currency": "GBP", "colour": "Black", "material": "Pebble Leather", "stock_status": "In Stock"
| # | sku | product_name | collection | category | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Promotions objects from radley.co.uk. All fields typed and schema-versioned.
"sku": "H7182001", "base_price": 199.0, "sale_price": 139.0, "discount_pct": 30, "currency": "GBP", "promotion_text": "Mid Season Sale", "is_outlet": false, "price_timestamp": "2026-08-14T10:05:00Z"
| # | sku | base_price | sale_price | discount_pct | currency | promotion_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Product Specifications objects from radley.co.uk. All fields typed and schema-versioned.
"sku": "H7182001", "product_type": "Grab Bag", "leather_type": "Grain Leather", "hardware_colour": "Gold", "closure_type": "Zip", "weight": "0.6 kg", "interior_details": "Triple compartment interior", "dustbag_included": true
| # | sku | product_type | leather_type | hardware_colour | closure_type | weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Inventory & Variants objects from radley.co.uk. All fields typed and schema-versioned.
"parent_sku": "H7182", "variant_sku": "H7182001", "colour_name": "Black", "colour_hex": "#000000", "size": "Medium", "stock_level": 45, "low_stock_warning": false
| # | parent_sku | variant_sku | colour_name | colour_hex | size | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category & Taxonomy objects from radley.co.uk. All fields typed and schema-versioned.
"sku": "H7182001", "primary_category": "Handbags", "sub_category": "Grab Bags", "collection_name": "Dukes Place", "breadcrumbs": "Home > Handbags > Grab Bags", "position_in_grid": 4, "filter_tags": "['Leather', 'Black', 'Zip Fastening', 'Everyday']", "scraped_at": "2026-08-14T10:05:33Z"
| # | sku | primary_category | sub_category | collection_name | breadcrumbs | position_in_grid |
|---|---|---|---|---|---|---|
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Our Radley scraper navigates category grids, hydrates dynamic colour swatches, and tracks inventory changes across the entire product catalogue.
Extract comprehensive details for handbags, purses, luggage, and footwear, including all metadata fields Radley surfaces.
Map parent products to child SKUs based on colour options, capturing distinct pricing and stock statuses for each variant.
Capture base prices, sale prices, and promotional labels across mainline and outlet categories.
Extract structured data for dimensions, strap drop lengths, weight, and hardware finishes from unstructured description blocks.
Track in stock, out of stock, and low stock warnings at the variant level to monitor inventory depth.
Extract clean URLs for all product images, including alternate angles, model shots, and interior views.
Track product placement within category grids, breadcrumb paths, and applied filter tags.
Capture 'Complete the look' and 'You may also like' product recommendations to map accessory relationships.
Run daily or hourly pipelines that only deliver changed records, optimising downstream processing costs.
Brief in. Clean data out.
Provide Radley category URLs, target collections, or specific product types. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for radley.co.uk.
Schema validation, null-rate checks, and sample data reviews before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern eCommerce platforms use dynamic hydration and perimeter defences. Here is how we build resilient pipelines.
We route requests through UK residential ISP proxies with realistic browser fingerprints to bypass perimeter bot protection and ensure stable access to the catalogue.
Radley product pages load colour variants and associated stock statuses dynamically via JavaScript. We run full Playwright browser sessions to capture data that simple HTTP requests miss.
eCommerce frontends update frequently. Our selector strategy uses multiple fallback chains per field, including CSS, XPath, and JSON-LD structured data, preventing pipeline breaks during site updates.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs for pricing or stock changes, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes or coverage drops and resolve issues before they impact your data warehouse.
Retailers and brands track Radley pricing, promotional cadences, and outlet discounting to inform their own pricing strategies.
Merchandising teams analyse Radley category structures, colour distributions, and material choices to identify market gaps.
Fashion analysts track product lifecycle velocities, popular colourways, and new collection launches to forecast accessory trends.
Brands monitor product catalogues to identify unauthorised resellers matching SKUs against official retail channels.
Machine learning teams use structured Radley product images and metadata to train computer vision models for fashion recognition.
Supply chain analysts monitor out-of-stock rates across categories to estimate demand velocity and production cycles.
"Radley's product catalogue contains precise material specifications and pricing structures that define the accessible luxury segment, requiring structured extraction."
Most teams underestimate the investment required: reliable Radley scraping requires handling dynamic swatch hydration, regional pricing variations, and frequent layout updates. DataFlirt absorbs that complexity so your engineers can focus on the analysis rather than the infrastructure.
Everything supported by our radley.co.uk 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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About radley.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Radley is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and stock data. We do not extract personal data or circumvent authentication walls. Clients should consult legal counsel for specific use cases.
Yes. Our pipeline maps the parent product to all available child colour SKUs, capturing the specific price, imagery, and stock status for each variant.
Yes. We track both base prices and promotional sale prices across all categories, including dedicated outlet sections on the site.
We use Playwright to execute JavaScript and hydrate dynamic elements like colour swatches and stock indicators. Our selector chains use multiple fallbacks to ensure stability during frontend updates.
Pipelines can be configured to run daily, hourly, or on custom schedules depending on your requirements. Stock status is accurate as of the timestamp recorded during the specific run.
Our packages start at defined category lists with weekly delivery. For full catalogue extraction or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 products as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price and stock monitoring, we scope, build, and operate the pipeline. Tell us what you need.
