We extract product listings, size-level stock, pricing signals, fabric details, and collection metadata from Reiss. 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 Metadata objects from reiss.com. All fields typed and schema-versioned.
"sku": "R-123-456", "title": "Milano Wool Blend Tailored Blazer", "category": "Womens", "sub_category": "Blazers", "colour": "Navy", "fit_type": "Tailored", "style_code": "T89-102"
| # | sku | title | category | sub_category | description | colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Markdown objects from reiss.com. All fields typed and schema-versioned.
"sku": "R-123-456", "original_price": 250.0, "current_price": 175.0, "discount_pct": 30, "currency": "GBP", "markdown_status": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | original_price | current_price | discount_pct | currency | markdown_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizing objects from reiss.com. All fields typed and schema-versioned.
"sku": "R-123-456", "size": "UK 10", "in_stock": true, "low_stock_warning": true, "stock_qty": 3, "delivery_estimate": "2-3 Working Days", "store_availability": false
| # | sku | size | in_stock | low_stock_warning | stock_qty | delivery_estimate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Materials & Care objects from reiss.com. All fields typed and schema-versioned.
"sku": "R-123-456", "main_fabric": "55% Wool, 45% Polyester", "lining_fabric": "100% Viscose", "dry_clean_only": true, "iron_instructions": "Cool iron", "origin_country": "Portugal", "sustainability_tags": "['Recycled Lining']"
| # | sku | main_fabric | lining_fabric | wash_instructions | iron_instructions | dry_clean_only |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Media Assets objects from reiss.com. All fields typed and schema-versioned.
"sku": "R-123-456", "primary_image_url": "https://reiss.com/media/images/primary.jpg", "gallery_image_urls": "['img1.jpg', 'img2.jpg']", "model_height": "5ft 10in", "model_size_worn": "UK 8", "asset_timestamp": "2026-05-12T09:14:33Z"
| # | sku | primary_image_url | gallery_image_urls | video_url | model_height | model_size_worn |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Reiss scraper handles the complexities of fashion retail: dynamic size availability, variant matrix mapping, and region-specific pricing - with full anti-bot circumvention built in.
Link colours to parent styles and extract the full matrix of available sizes per colourway.
Monitor exact availability per size. Detect low stock warnings and out-of-stock statuses across the catalogue.
Capture clean CDN URLs for zoom imagery, gallery shots, and product videos without watermarks.
Extract raw material percentages, lining details, and specific care instructions for supply chain analysis.
Track GBP, USD, and EUR dynamically by routing requests through region-specific residential proxies.
Identify sale items, calculate discount depths, and track price changes across seasonal transitions.
Crawl Men, Women, and Children taxonomies systematically to ensure complete catalogue coverage.
Extract model height and size worn data to normalise fit expectations across product categories.
Run continuous pipelines and only export changed stock or price records to reduce warehouse bloat.
Brief in. Clean data out.
Provide category URLs, search terms, or specific product IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and bot mitigation for reiss.com.
Schema validation, null-rate checks, price-outlier detection, and sample payloads before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fashion retail scraping requires navigating dynamic inventory matrices and strict CDN bot protections. Here is our technical approach.
Retailers use edge protection to block automated traffic. We use residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass rate limits.
Fashion SKUs are nested. We execute JavaScript to hydrate the DOM and extract the complete matrix of size and colour variants for a single parent style.
Prices change based on the IP region. We route requests through specific geographic proxy pools to capture accurate GBP, USD, or EUR pricing.
We maintain a hash index of last-seen values. Subsequent runs only push diffs for stock levels or prices, reducing compute cost and downstream processing load.
High-resolution images are often hidden in script tags. Our parsers extract the raw CDN links directly from the underlying JSON payloads rather than scraping thumbnails.
Retailers monitor Reiss pricing, markdown timing, and discount depths to optimise their own seasonal sales.
Merchandisers track category weighting, colour prevalence, and fabric choices to inform future collection planning.
Pricing teams analyse the correlation between stock depth and markdown velocity to improve clearance strategies.
Machine learning teams use high-res product imagery and metadata to train visual search and tagging models.
Analysts track origin country and fabric composition data to model supply chain dependencies and costs.
Strategy teams evaluate regional pricing disparities and stock availability to plan geographic market entries.
"Reiss maintains a highly structured, premium product catalogue - extracting it accurately requires handling complex size-colour matrices and strict bot mitigation layers."
Most engineering teams underestimate the complexity of scraping modern fashion retailers. Accurately mapping SKUs across multiple colours, sizes, and regional pricing tiers requires dedicated infrastructure. DataFlirt manages the residential proxy rotation, JavaScript execution, and schema validation so your team can focus on merchandising intelligence.
Everything supported by our reiss.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 retry logic. Playwright handles JavaScript rendering and interaction flows for complex size matrices.
We maintain pools of residential ISP proxies across key regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About reiss.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available catalogue and pricing information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass edge protections.
Yes. We route requests through geographically specific proxy pools to extract the correct regional pricing and stock availability.
Real-time streaming pipelines achieve low latency for stock signals. Full catalogue refreshes complete within a defined window depending on the total SKU count.
Yes. We parse the underlying JSON payloads to extract the raw CDN URLs for high-resolution gallery and zoom imagery.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record for pricing and stock levels from the date your pipeline starts.
Our smallest packages start at a defined category list with weekly delivery. For full catalogue tracking, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 500 SKUs 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 a continuous inventory feed - we scope, build, and operate the pipeline. Tell us what you need.
