We extract furniture listings, dimension specifications, material details, delivery timelines, and reviews from Article. 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 article.com. All fields typed and schema-versioned.
"sku": "U-1234", "title": "Sven Charme Tan Sofa", "category": "Sofas", "price": 1899.0, "currency": "USD", "materials": "Full-aniline leather", "assembly_required": true
| # | sku | title | category | sub_category | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Delivery objects from article.com. All fields typed and schema-versioned.
"sku": "U-1234", "in_stock": true, "stock_status_text": "In Stock", "estimated_dispatch": "1-3 days", "delivery_fee": 49.0, "backorder_date": "None"
| # | sku | in_stock | stock_status_text | estimated_dispatch | delivery_fee | warehouse_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dimensions & Specs objects from article.com. All fields typed and schema-versioned.
"sku": "U-1234", "overall_width": "88 in", "overall_depth": "38 in", "overall_height": "34 in", "seat_height": "19 in", "clearance": "8 in"
| # | sku | overall_width | overall_depth | overall_height | seat_height | seat_depth |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from article.com. All fields typed and schema-versioned.
"review_id": "REV-98231", "sku": "U-1234", "star_rating": 5, "review_date": "2026-03-12", "review_text": "Beautiful mid-century design. Leather is soft and high quality.", "verified_buyer": true
| # | review_id | sku | reviewer_name | star_rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Collections & Bundles objects from article.com. All fields typed and schema-versioned.
"collection_id": "COL-SVEN", "collection_name": "Sven Collection", "primary_sku": "U-1234", "bundle_price": 2499.0, "room_type": "Living Room", "style_tags": "['Mid-Century Modern', 'Leather']"
| # | collection_id | collection_name | collection_url | primary_sku | related_skus | bundle_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Article scraper handles dynamic inventory APIs, complex dimension accordions, and paginated review endpoints, delivering analysis-ready data straight to your warehouse.
Extract SKUs, titles, descriptions, and category taxonomy across all furniture lines and decor accessories.
Parse unstructured text into precise numerical fields for overall dimensions, seat depth, clearance, and weight.
Monitor in-stock status, exact backorder dates, and low stock warnings at the SKU level.
Capture dispatch estimates and shipping tier pricing based on specific geographic zip codes.
Record base prices, clearance markdowns, and bundle savings across the entire product catalogue.
Extract full review text, star ratings, verified buyer flags, and helpful vote counts across all paginated pages.
Scrape URLs for main product images, lifestyle shots, dimension diagrams, and detailed fabric swatches.
Map parent-child relationships between individual pieces and coordinated room sets.
Run hourly inventory checks or weekly full catalogue dumps with change-detection diffing.
Brief in. Clean data out.
Provide category URLs or specific SKUs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for article.com.
Schema validation, null-rate checks, and dimension format normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
E-commerce scraping requires navigating dynamic APIs and unstructured text. Here is how we build resilient extraction pipelines.
Article updates inventory and delivery estimates dynamically based on location data. We run full Playwright browser sessions to capture accurate, region-specific stock levels.
Furniture dimensions are often nested in unstructured text or dynamic accordions. Our parsers extract and normalise width, depth, height, and clearance into typed numerical fields.
E-commerce sites deploy rate limiting. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain uninterrupted access.
For daily inventory tracking, we maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream load.
Every run emits structured logs to our observability stack. We alert on schema drift, missing dimensions, and coverage drops, responding before you notice.
Furniture retailers track Article pricing, bundle discounts, and shipping fees to maintain competitive positioning.
Merchandising teams analyse Article catalogue breadth, colour options, and material trends to inform product development.
Analysts monitor backorder dates and out-of-stock rates to gauge supply chain health and demand spikes.
Consultants aggregate review volume and sentiment across collections to evaluate brand performance and customer satisfaction.
3D rendering and room planning applications ingest precise dimension data and high-res imagery for virtual staging.
Data teams track the introduction of new fabrics, styles, and categories to predict seasonal home decor trends.
"Article provides a masterclass in direct-to-consumer furniture retail, but extracting their highly structured dimension, material, and inventory data requires custom parsers and dynamic rendering."
Most teams underestimate the complexity of scraping modern e-commerce storefronts. Reliable Article scraping requires handling dynamic inventory APIs, normalising nested dimension data, and bypassing rate limits. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our article.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 retry logic. Playwright handles JavaScript rendering for dynamic delivery estimates and inventory states.
We maintain pools of residential ISP proxies. Rotation happens per request with sticky sessions where required for location-based pricing.
Pipelines run on AWS Lambda and ECS. 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 article.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Article is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or bypass authentication walls.
Article calculates delivery times and stock based on location. We use Playwright to simulate specific zip codes, capturing accurate, region-specific inventory data.
Yes. We parse the specification accordions to extract overall dimensions, seat depth, arm height, and clearance, normalising them into structured numerical fields.
Inventory and pricing pipelines can run at hourly cadences. Full catalogue refreshes typically complete within a 2-4 hour window.
Absolutely. We extract all material specifications, including fabric composition, wood types, and care instructions.
Yes. We maintain a time-series table per SKU, allowing you to track base prices, bundle discounts, and clearance markdowns over time.
Our packages start with full catalogue extraction delivered weekly. For higher frequency inventory tracking, we price based on volume and delivery cadence.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous inventory monitoring across their entire SKU base, we scope, build, and operate the pipeline. Tell us what you need.
