We extract designer furniture listings, upholstery variants, dimensions, pricing, and stock status from Made.com. 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 made.com. All fields typed and schema-versioned.
"product_id": "SOF-SCO-001", "title": "Scott 3 Seater Sofa", "category": "Sofas", "price": 999.0, "currency": "GBP", "designer": "Made Studio", "material": "Cotton Velvet", "weight": 54.5
| # | product_id | title | category | sub_category | price | currency |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Stock objects from made.com. All fields typed and schema-versioned.
"sku": "SOF-SCO-001-BLU", "price": 999.0, "original_price": 1199.0, "discount_pct": 16, "in_stock": true, "dispatch_time": "3-5 working days", "delivery_cost": 39.0, "scraped_at": "2026-05-12T09:14:00Z"
| # | product_id | sku | price | original_price | discount_pct | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Materials & Variants objects from made.com. All fields typed and schema-versioned.
"sku": "SOF-SCO-001-BLU", "parent_id": "SOF-SCO-001", "colour": "Petrol Blue", "fabric_type": "Velvet", "leg_material": "Dark stained wood", "filling_material": "Foam and feather", "variant_price": 999.0
| # | sku | parent_id | colour | fabric_type | leg_material | finish |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from made.com. All fields typed and schema-versioned.
"review_id": "REV-89214", "product_id": "SOF-SCO-001", "rating": 5, "review_title": "Stunning sofa", "review_text": "Beautiful colour and very comfortable. Assembly was minimal.", "verified_purchase": true, "review_date": "2026-04-12"
| # | review_id | product_id | author_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Designer Profiles objects from made.com. All fields typed and schema-versioned.
"designer_name": "Busetti Garuti Redaelli", "origin_country": "Italy", "collection_name": "Elona", "product_count": 14, "designer_bio": "An Italian design trio known for minimalist storage solutions.", "inspiration_text": "Combining brass accents with matte finishes."
| # | designer_id | designer_name | designer_bio | origin_country | collection_name | product_count |
|---|---|---|---|---|---|---|
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Our Made.com scraper captures every detail of the furniture catalogue: complex upholstery variants, exact dimensions, designer biographies, and real-time dispatch estimates.
Extract title, category, dimensions, weight, care instructions, and material composition for every item.
Capture all colour, fabric, and leg finish combinations linked to their specific SKUs and pricing.
Monitor real-time stock availability, estimated dispatch times, and delivery costs across the catalogue.
Extract current price, original list price, discount percentages, and active sale badges.
Structured extraction of height, width, depth, and seat height into queryable numeric fields.
Full text, star ratings, and verified purchase status for product feedback analysis.
Extract URLs for all product gallery images, fabric swatches, and lifestyle shots.
Scrape designer profiles, biographies, and collection relationships linked to specific products.
Run scheduled pipelines that only push records when price, stock, or dispatch times change.
Brief in. Clean data out.
Provide category URLs, specific collections, or designer names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for made.com.
Schema validation, null-rate checks, price-outlier detection, and sample variants before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured homeware data requires rendering complex product configurators and parsing unstructured specification text. Here is how we build it.
Made.com relies on complex JavaScript applications to display fabric variants, pricing updates, and stock availability. We run full Playwright browser sessions to hydrate these components, capturing accurate data for every possible upholstery combination.
Dimensions, materials, and care instructions are often presented in varying formats depending on the product type. Our extraction logic uses regex and NLP to normalise these unstructured blocks into clean, queryable numeric fields.
To prevent IP bans during full catalogue scrapes, our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management.
For tracking stock drops and sale events, we maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing dimensions, or category structure changes, fixing selectors before you notice data loss.
Furniture retailers monitor competitor pricing, discount strategies, and seasonal sale events to adjust their own positioning.
Merchandising teams analyse material trends, colour variants, and category density to identify gaps in their own product lines.
Logistics teams track dispatch lead times across different furniture categories to benchmark industry delivery standards.
Proptech companies and AI startups use structured dimension and material data to build 3D room planners and recommendation engines.
Design agencies monitor designer collaborations, new fabric introductions, and collection launches to predict upcoming interior trends.
Manufacturers track customer reviews and ratings to understand common pain points with assembly, fabric durability, or delivery.
"Made.com defines modern British interior trends, but extracting structured dimension and material data requires rendering complex product configurators."
Most teams underestimate the investment required: reliable Made.com scraping requires residential proxies, full JavaScript rendering for fabric selectors, and daily schema maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our made.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, 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 across UK/EU regions. 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 made.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Made.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and specification data. We do not extract personal data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 503/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
Yes. Furniture sites often list dimensions as plain text strings. We apply custom parsing logic to extract height, width, depth, and seat height into distinct, queryable numeric fields in centimetres or millimetres.
Full catalogue refreshes at daily cadence complete within a 2-4 hour window depending on scale. For specific high-priority categories, we can configure intraday pipelines to track flash sales and stock drops.
We extract the direct URLs to the highest resolution images available in the product gallery, including lifestyle shots and specific fabric swatches. We do not host the image files, but deliver the URLs for your systems to ingest.
Our packages start at defined category lists with weekly delivery. For full catalogue extraction or custom normalisation requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 200 products as part of the pre-engagement scoping process — so you can validate dimension parsing, variant completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across all categories — we scope, build, and operate the pipeline. Tell us what you need.
