We extract luxury product catalogues, pricing across regions, material compositions, sizing availability, and collection metadata from versace.com. Delivered as clean JSON, CSV, or Parquet.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Details objects from versace.com. All fields typed and schema-versioned.
"sku": "1004182-1A03190_1B000", "name": "La Medusa Small Handbag", "category": "Women", "sub_category": "Bags", "material_composition": "100% Calf Leather", "made_in": "Italy", "colourway": "Black", "fit_type": "One Size"
| # | sku | name | category | sub_category | description | material_composition |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Availability objects from versace.com. All fields typed and schema-versioned.
"sku": "1004182-1A03190_1B000", "region": "UK", "currency": "GBP", "retail_price": 1450.0, "discounted_price": "None", "in_stock": true, "available_sizes": "['UNI']", "scraped_at": "2026-05-12T10:15:00Z"
| # | sku | region | currency | retail_price | discounted_price | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Collections & Runway objects from versace.com. All fields typed and schema-versioned.
"collection_name": "Spring-Summer 2026", "season": "SS26", "designer": "Donatella Versace", "look_number": "14", "featured_skus": "['1012345-1A04567_1B000', '1004182-1A03190_1B000']", "theme": "Milanese Glamour", "release_date": "2026-02-15"
| # | collection_name | season | designer | look_number | featured_skus | runway_video_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Media & Imagery objects from versace.com. All fields typed and schema-versioned.
"sku": "1004182-1A03190_1B000", "primary_image_url": "https://versace.com/dw/image/v2/BGWN_PRD/on/demandware.static/-/Sites-ver-master-catalog/default/dw12345/original/1004182.jpg", "gallery_urls": "['https://versace.com/dw/image/v2/BGWN_PRD/on/demandware.static/-/Sites-ver-master-catalog/default/dw12346/original/1004182_back.jpg']", "model_height": "178cm", "model_size_worn": "IT 40", "image_angles": "['Front', 'Back', 'Detail', 'On Model']", "asset_type": "High-Res JPEG"
| # | sku | primary_image_url | gallery_urls | video_url | model_height | model_size_worn |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sizing & Fit objects from versace.com. All fields typed and schema-versioned.
"sku": "1011111-1A02222_1B000", "size_system": "IT", "available_sizes": "['38', '40', '42', '44']", "measurements_chest": "86cm", "measurements_waist": "64cm", "measurements_hips": "92cm", "fit_notes": "Slim fit, true to size."
| # | sku | size_system | available_sizes | size_guide_url | measurements_chest | measurements_waist |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our versace.com scraper navigates region-specific storefronts, extracts high-resolution asset metadata, and maps complex sizing matrices across the entire catalogue.
Extract details across Ready-to-Wear, Bags, Shoes, and Accessories. Capture product names, descriptions, and SKUs.
Track pricing in USD, EUR, GBP, and JPY by routing requests through geo-specific proxy pools.
Parse fabric composition percentages, origin country (Made in Italy), and specific care instructions.
Monitor stock levels across Italian (IT) sizing matrices and detect out-of-stock variations in real time.
Capture URLs for high-resolution product photography, runway imagery, and model fit shots.
Map distinct variations like Barocco prints, La Greca motifs, and seasonal colour palettes to parent SKUs.
Preserve the navigation tree from top-level departments down to specific product sub-categories.
Run pipelines daily or weekly to track new collection drops and seasonal markdowns.
Detect when specific sizes or highly anticipated items sell out, useful for demand forecasting.
Brief in. Clean data out.
Select target regions, categories, and extraction frequency. We map the schema to your requirements.
We configure Playwright crawlers, manage geo-proxies, and handle session state for versace.com.
Schema validation, null-rate checks, and currency normalisation before production launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on schedule.
High-end fashion sites use dynamic rendering and strict geo-routing. Here is how we extract data reliably.
Versace enforces strict geo-redirects based on IP address. We use localised residential proxies to access specific regional storefronts, ensuring accurate local pricing and currency extraction.
Product availability and sizing matrices rely heavily on client-side JavaScript. We use Playwright to execute page scripts and intercept XHR responses containing the underlying JSON product data.
We extract the highest resolution image URLs from Demandware asset CDNs without downloading the files directly, keeping the data payload lightweight while preserving media references.
Luxury items often feature distinct SKUs for every colour and size combination. Our pipeline reconstructs these relationships, mapping child variants back to the parent product model.
To prevent IP bans and maintain stealth, we strictly control request concurrency and introduce randomised delays modelled on organic browsing behaviour.
Luxury retailers monitor regional price discrepancies and seasonal markdown strategies to optimise their own pricing.
Fashion analysts track material usage, colourway distribution, and category depth to forecast seasonal trends.
Brand protection agencies use official product metadata and imagery to identify unauthorised sellers and fake listings.
Machine learning teams ingest high-resolution product imagery and descriptive metadata to train computer vision models.
Strategists analyse regional stock availability and localised pricing to determine market demand and supply chain efficiency.
Resale platforms cross-reference original retail prices, material compositions, and release seasons to authenticate and price second-hand items.
"Luxury fashion relies on precise material, sizing, and regional pricing data. Versace.com holds this intelligence, but accessing it systematically requires a purpose-built extraction pipeline."
Extracting data from luxury brand sites involves navigating heavy JavaScript frameworks, high-resolution media assets, and strict geo-blocking. DataFlirt manages the proxy rotation and session hydration so your analysts receive normalised catalogues without maintaining complex crawler infrastructure.
Everything supported by our versace.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 crawl orchestration and deduplication. Playwright handles JavaScript rendering and intercepts XHR requests for product JSON data.
We maintain pools of residential ISP proxies across global regions. Rotation and sticky sessions ensure accurate regional pricing without triggering bot protection.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling and dependency chains. Monitoring via Prometheus and Grafana ensures data continuity.
Data delivered to where your team already works — no new tooling required.
About versace.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product catalogues and pricing data is generally permissible. DataFlirt extracts only public, non-authenticated information. We do not attempt to bypass login screens or extract personally identifiable information (PII).
Versace uses IP-based geo-routing. We utilise residential proxies located in your target regions (e.g., UK, Italy, US, Japan) to ensure the site serves the correct local currency and stock availability.
We extract the direct CDN URLs for the highest resolution images available. We do not download the binary image files, which keeps the data delivery fast and lightweight while allowing you to fetch assets as needed.
Yes. The pipeline captures stock status at the individual size level, allowing you to monitor when specific variants sell out or are restocked.
For luxury catalogues, daily or weekly runs are standard. We configure the schedule based on your requirements to track new collection drops or seasonal sales.
We build managed pipelines tailored to your scope. Engagements typically start with a defined set of target regions and categories, delivered on a scheduled cadence.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or continuous regional price monitoring, we handle the infrastructure. Define your scope and we deliver the data.
