We extract product specifications, spatial dimensions, fabric grades, mattress details, and localized stock availability from Raymour & Flanigan. 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 raymourflanigan.com. All fields typed and schema-versioned.
"sku": "200145892", "title": "Chenille Sectional Sofa", "brand": "Cindy Crawford Home", "base_price": 1899.95, "sale_price": 1599.95, "dimensions_width": 124.0, "dimensions_depth": 98.0, "dimensions_height": 38.0, "colour_name": "Slate Gray", "fabric_grade": "Performance", "financing_available": true
| # | sku | title | category | sub_category | brand | base_price |
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Complete list of extractable fields for Availability & Delivery objects from raymourflanigan.com. All fields typed and schema-versioned.
"sku": "200145892", "zip_code": "10001", "delivery_eligible": true, "estimated_delivery_days": 3, "white_glove_available": true, "pickup_eligible": true, "nearest_store_id": "RF-NY-012", "store_stock_status": "In Stock", "shipping_surcharge": 0.0
| # | sku | zip_code | delivery_eligible | estimated_delivery_days | white_glove_available | pickup_eligible |
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Complete list of extractable fields for Store Locations objects from raymourflanigan.com. All fields typed and schema-versioned.
"store_id": "RF-NY-012", "store_name": "Manhattan Showroom", "store_type": "Showroom", "city": "New York", "state": "NY", "zip_code": "10023", "latitude": 40.7749, "longitude": -73.9822, "has_outlet": false
| # | store_id | store_name | store_type | address_line_1 | city | state |
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Complete list of extractable fields for Mattress Data objects from raymourflanigan.com. All fields typed and schema-versioned.
"sku": "900451234", "brand": "Beautyrest", "size": "Queen", "comfort_level": "Plush", "mattress_type": "Hybrid", "thickness_inches": 14.5, "cooling_technology": true, "adjustable_base_compatible": true, "warranty_years": 10
| # | sku | brand | model_name | size | comfort_level | mattress_type |
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Complete list of extractable fields for Reviews & Ratings objects from raymourflanigan.com. All fields typed and schema-versioned.
"review_id": "REV-892114", "sku": "200145892", "rating": 4, "review_title": "Great sectional, firm cushions", "review_text": "Fits perfectly in our living room. Fabric is easy to clean.", "review_date": "2025-11-04", "verified_buyer": true, "helpful_votes": 12, "photos_attached": true
| # | review_id | sku | reviewer_name | rating | review_title | review_text |
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Furniture retail data is notoriously unstructured. We map complex SKU variations including fabric grades, configurations, and spatial dimensions into clean, queryable schemas.
Extract all colourways, fabric grades, and configuration options (e.g., left-arm vs right-arm facing) mapped to distinct child SKUs.
Normalise width, depth, and height measurements from raw text descriptions into structured numeric fields for spatial analysis.
Simulate sessions across target zip codes to capture regional pricing variations, delivery estimates, and local warehouse stock levels.
Monitor mainline versus outlet inventory, capturing markdowns, floor sample pricing, and limited-stock clearance items.
Extract dedicated attributes for mattresses including comfort level, coil count, cooling tech, and adjustable base compatibility.
Deconstruct '5-Piece Bedroom Sets' into individual component SKUs, mapping package pricing against individual item costs.
Capture promotional financing offers, APR details, and minimum monthly payment calculations displayed on product pages.
Extract paginated reviews, star ratings, verified buyer flags, and user-generated photo URLs for sentiment and quality analysis.
Track all physical showroom and outlet locations, including geocoordinates, operating hours, and contact details.
Brief in. Clean data out.
Provide categories, URLs, or a list of target zip codes for localised data. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and zip-code injection logic.
Schema validation, null-rate checks, dimension parsing verification, and sample datasets before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from Raymour & Flanigan requires handling dynamic inventory systems, localised delivery logic, and complex product variants.
Furniture availability and delivery timelines depend entirely on the user's location. We inject specific zip codes into the browser session cookies and local storage via Playwright to extract accurate regional data.
A single sectional sofa can have hundreds of configurations based on fabric, colour, and orientation. We map the underlying JSON payload driving the frontend configurator to extract every valid combination without brute-forcing the UI.
Dimensions are often buried in unstructured HTML descriptions (e.g., 'W: 84" D: 38" H: 36"'). Our pipeline uses strict regex parsing to extract these into distinct numeric columns for database ingestion.
Promotional pricing, financing terms, and real-time store stock are hydrated via client-side JavaScript. We execute full browser sessions to ensure all asynchronous XHR calls complete before DOM extraction.
For large catalogues, we maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load for your data engineering team.
Furniture retailers track base pricing, promotional markdowns, and financing offers to maintain competitive parity in regional markets.
Merchandising teams analyse category depth, fabric options, and brand representation to identify gaps in their own product offerings.
Logistics teams monitor estimated delivery days across different zip codes to benchmark last-mile delivery performance.
Computer vision and generative AI models ingest product dimensions, categories, and image URLs to train spatial planning algorithms.
Analysts track showroom and outlet locations to map retail density and evaluate expansion opportunities in target MSAs.
Product teams mine review text and ratings to identify common defects, material durability issues, and feature requests.
"Furniture retail data is heavily localized and structurally complex. Extracting it requires managing spatial dimensions, fabric matrices, and zip-code specific inventory at scale."
Most teams struggle with the variant matrices of furniture catalogues. A single sofa might have 40 fabric options, each altering the price and delivery timeline based on the user's zip code. DataFlirt handles the session management and variant normalisation so you receive clean, relational data ready for analysis.
Everything supported by our raymourflanigan.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, cookie sessions, and zip-code injection required for localised furniture data.
We maintain pools of US residential ISP proxies to route requests organically, preventing IP bans and ensuring accurate regional pricing responses.
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 raymourflanigan.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail websites is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and store data. We do not extract personal data or circumvent authentication walls.
We use Playwright to simulate user sessions, injecting specific zip codes into the site's location manager via cookies and local storage. This ensures the pricing, delivery estimates, and stock levels reflect the exact region you are targeting.
Yes. We map the underlying configuration data to extract every valid combination of fabric, colour, and orientation, outputting them as distinct child SKUs with their respective price deltas.
Yes. Raw text descriptions containing measurements are parsed using regex into structured numeric fields (width, depth, height, weight) to enable spatial filtering and analysis in your database.
Full catalogue refreshes typically complete within a 12-hour window. For specific high-priority SKUs or categories, we can configure hourly pipelines to monitor flash sales or clearance markdowns.
Yes. The pipeline can be configured to monitor specific outlet locations, capturing floor sample availability and clearance pricing distinct from standard warehouse stock.
Our minimum engagement starts at a defined category or SKU list with weekly delivery. We price based on data volume, frequency, and the number of target zip codes required for localisation.
Yes. We provide a sample run of up to 500 SKUs across a few categories as part of the pre-engagement scoping process, allowing you to validate schema fit and dimension parsing accuracy.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across hundreds of zip codes — we scope, build, and operate the pipeline. Tell us what you need.
