We extract designer collections, product specifications, material data, and real-time stock levels from Ferm Living. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Details objects from fermliving.com. All fields typed and schema-versioned.
"sku": "110426543", "name": "Rico Sofa 3 Seater", "category": "Furniture", "designer": "Ferm Living Studio", "material": "Boucle", "colour": "Sand", "price": 3499.0, "currency": "EUR"
| # | sku | name | category | sub_category | designer | material |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Specifications objects from fermliving.com. All fields typed and schema-versioned.
"sku": "110426543", "care_instructions": "Vacuum frequently on medium power.", "certification": "FSC Mix Certified wood", "assembly_required": false, "outdoor_use": false, "warranty_years": 2
| # | sku | care_instructions | certification | testing_standards | assembly_required | outdoor_use |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Stock objects from fermliving.com. All fields typed and schema-versioned.
"sku": "110426543", "region": "EU", "price_eur": 3499.0, "price_dkk": 25999.0, "in_stock": true, "lead_time_days": 14
| # | sku | region | price_eur | price_usd | price_gbp | price_dkk |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Media Assets objects from fermliving.com. All fields typed and schema-versioned.
"sku": "110426543", "primary_image_url": "https://fermliving.com/cdn/rico_sofa_front.jpg", "gallery_image_urls": "['https://fermliving.com/cdn/rico_sofa_side.jpg', 'https://fermliving.com/cdn/rico_sofa_detail.jpg']", "assembly_manual_pdf": "None", "care_guide_pdf": "https://fermliving.com/docs/boucle_care.pdf", "model_3d_url": "https://fermliving.com/3d/110426543.gltf"
| # | sku | primary_image_url | gallery_image_urls | lifestyle_image_urls | model_3d_url | video_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Collections objects from fermliving.com. All fields typed and schema-versioned.
"collection_id": "col_rico", "collection_name": "Rico Collection", "designer": "Ferm Living Studio", "launch_year": 2019, "product_count": 18, "category_focus": "Lounge Furniture"
| # | collection_id | collection_name | designer | launch_year | product_count | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Furniture retail requires precise dimensional data, fabric specifications, and high-resolution assets. We model Ferm Living's catalogue into a strictly typed schema.
Map complex parent child relationships across fabrics, colours, and sizes. Every variant gets a distinct SKU record with its specific pricing and imagery.
Extract and normalise height, width, depth, and seat height measurements into structured numeric fields and standard units.
Monitor inventory status and estimated dispatch windows. Useful for dropshippers and trade partners managing client expectations.
Capture pricing across EUR, USD, GBP, and DKK by rotating geolocation headers and session cookies per request.
Extract direct URLs for assembly manuals, care guides, energy labels, and 3D model files hosted on their CDN.
Bypass thumbnail compression to extract the highest resolution product, gallery, and lifestyle images available on the platform.
Capture FSC certifications, responsible production tags, and material origin data for eco-conscious retail filtering.
Group products by their respective design collections and attribute correct designer profiles to each SKU.
Run daily diffs to detect new product launches, discontinued items, and price adjustments without processing the entire catalogue.
Brief in. Clean data out.
Specify the categories, regions, and data points you need. We map the target schema together.
We configure crawlers to handle Ferm Living's dynamic variant loading and image CDNs.
We verify dimensional data types, ensure null rates are within bounds, and validate cross currency pricing.
Clean structured data pushed to your S3 bucket, BigQuery, or delivered via Webhook.
Ferm Living uses a modern frontend stack. Here is how we extract clean data from dynamic interfaces.
Furniture products often load pricing and imagery dynamically based on fabric and colour selection. We use Playwright to iterate through variant combinations in the DOM, capturing the exact state for each SKU.
Frontend clients request compressed images. We parse the CDN URL structure to strip compression parameters, returning the original high resolution asset links required for print or high end retail display.
Pricing and availability change based on the user's location. We route requests through region specific residential proxies to capture accurate local market data.
Care instructions and material lists are often stored as unstructured HTML blocks. We apply regex and NLP parsing to extract discrete materials and percentages into queryable fields.
We distribute requests across proxy pools and implement adaptive concurrency to avoid triggering edge firewall rules or degrading site performance during catalogue sweeps.
Sync product details, imagery, and pricing directly into your PIM system without manual data entry.
Populate 3D room planners and mood board applications with accurate dimensions and high res lifestyle assets.
Monitor pricing strategies, new collection launches, and material trends against your own furniture catalogue.
Maintain accurate stock levels and lead times to prevent selling items that are backordered at the supplier.
Analyse assortment width, price point distribution, and sustainability claims across the Scandinavian design sector.
Train computer vision models on structured pairs of product imagery, 3D models, and spatial dimensions.
"Ferm Living's catalogue contains precise material specifications, dimensional data, and designer attributions required by high end retail aggregators."
Extracting structured data from modern design brands requires handling complex variant matrices, high resolution media CDNs, and nested technical specifications. DataFlirt models this spatial and material data accurately, maintaining strict schema validation across every product category.
Everything supported by our fermliving.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 execution for dynamic variant loading.
We route requests through European residential proxies to access accurate regional pricing and avoid rate limits.
Pipelines run on AWS infrastructure. Airflow handles scheduling and dependency management for daily catalogue sweeps.
Data delivered to where your team already works — no new tooling required.
About fermliving.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product data is generally permissible. DataFlirt extracts only public catalogue information, pricing, and specifications. We do not bypass authentication walls or extract user data. Clients should review terms of service and consult legal counsel for their specific use case.
Yes. We use headless browsers to iterate through all available fabric and colour combinations, capturing the specific price, imagery, and SKU for each variant.
We can configure pipelines to poll specific SKUs daily or hourly depending on your requirements and the target site's rate limits.
Yes. We parse raw text strings into discrete numeric fields for height, width, depth, and seat height, normalising units to your preference.
No. DataFlirt only extracts publicly visible retail pricing. We do not scrape gated B2B portals or trade account interfaces.
Yes. We provide a sample run covering a subset of categories to validate the schema and data quality before full pipeline deployment.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop copying dimensions manually. We build and maintain the pipeline to deliver clean structured data directly to your systems.
