We extract furniture catalogues, dimension matrices, material specifications, and pin-code pricing from Urban Ladder. 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 urbanladder.com. All fields typed and schema-versioned.
"sku": "FNSF51BRP3", "title": "Apollo Sofa", "category": "Living", "price": 34999.0, "list_price": 45999.0, "material": "Teak Wood", "assembly_required": false, "in_stock": true
| # | sku | title | category | sub_category | brand | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dimensions & Specs objects from urbanladder.com. All fields typed and schema-versioned.
"sku": "FNSF51BRP3", "height_cm": 85.0, "width_cm": 190.5, "depth_cm": 90.0, "weight_kg": 45.5, "frame_material": "Solid Wood", "finish_type": "Walnut", "seating_capacity": 3
| # | sku | height_cm | width_cm | depth_cm | weight_kg | seating_capacity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Inventory objects from urbanladder.com. All fields typed and schema-versioned.
"sku": "FNSF51BRP3", "pin_code": "560001", "price": 34999.0, "delivery_charge": 0.0, "in_stock": true, "delivery_days": 5, "emi_starting": 1648.0, "scraped_at": "2023-10-24T08:12:00Z"
| # | sku | pin_code | price | delivery_charge | delivery_days | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from urbanladder.com. All fields typed and schema-versioned.
"review_id": "REV-8921", "sku": "FNSF51BRP3", "rating": 4.5, "review_title": "Excellent finish", "review_text": "Wood quality is great.", "verified_buyer": true, "review_date": "2023-09-12", "helpful_votes": 12
| # | review_id | sku | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Finishes objects from urbanladder.com. All fields typed and schema-versioned.
"parent_sku": "FNSF51", "variant_sku": "FNSF51BRP3", "variant_type": "Fabric Colour", "variant_name": "Maroon", "price_diff": 0.0, "is_default": true, "stock_status": "Available", "image_url": "https://cdn.urbanladder.com/images/maroon-sofa.jpg"
| # | parent_sku | variant_sku | variant_type | variant_name | image_url | price_diff |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper handles nested category structures, dynamic pin-code pricing, complex variant matrices, and high-resolution image extraction - with full JavaScript rendering for modern SPA architecture.
Title, description, materials, care instructions, and warranty details extracted directly from product pages.
Height, width, depth, weight, and seating capacity normalised across all SKUs for accurate spatial analysis.
Location-based pricing, delivery estimates, and availability captured by injecting specific pin codes into the session.
Extract all combinations of fabric colours, wood finishes, and size variations tied to a parent product.
Real-time stock availability and delivery timelines mapped by geographic delivery zones.
Capture bank discounts, No-Cost EMI details, and promotional pricing flags per product.
Identify DIY versus Carpenter assembly flags to understand logistics and installation costs.
Extract CDN URLs for main product images, lifestyle shots, and material swatches without downloading blobs.
Extract customer feedback, star ratings, review dates, and verified buyer tags across the catalogue.
Brief in. Clean data out.
Provide target categories, pin codes, or specific SKUs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management tailored to urbanladder.com.
Schema validation, null-rate checks, and price anomaly detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket or data warehouse on agreed cadence.
Extracting furniture data requires handling heavy image payloads, dynamic location states, and nested variant structures.
Urban Ladder alters pricing and availability based on pin codes. We inject target pin codes via cookies and API headers to capture accurate local pricing and delivery estimates.
The platform relies heavily on modern JavaScript frameworks. We use Playwright to execute React hydration, ensuring dynamic pricing and variant widgets load completely before extraction.
Furniture items often have dozens of combinations involving fabric type, colour, and wood finish. We recursively map all variant combinations to ensure no SKU is missed.
Downloading high-resolution furniture images slows down pipelines. We extract direct CDN URLs for main images and swatches, allowing you to download them asynchronously.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost and downstream processing load for your data teams.
Furniture brands track pricing, discount strategies, and seasonal sales across specific pin codes to optimise their own pricing.
Retailers analyse material trends, finish popularity, and category depth to inform their procurement and manufacturing decisions.
Analysts track popular finishes, fabrics, and dimensions to identify consumer preferences and whitespace in the home decor market.
Machine learning teams train recommendation engines and spatial models using accurate furniture dimensions and room concept groupings.
Logistics teams correlate delivery timelines and assembly requirements across different geographic zones to benchmark performance.
Supply chain analysts correlate stock availability with seasonal demand spikes to improve inventory forecasting models.
"Furniture e-commerce relies heavily on dimension accuracy, material specifications, and location-based logistics. Extracting this requires a pipeline that understands complex product variants."
Most generic scrapers fail on furniture sites because they cannot handle nested variant matrices (fabric + finish combinations) or location-dependent pricing. DataFlirt maintains session state across pin codes and extracts the full dimension matrix, so your analysts receive structured, analysis-ready records without manual cleaning.
Everything supported by our urbanladder.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 executes JavaScript rendering and manages location-specific cookie sessions.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions to maintain pin-code state.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About urbanladder.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Urban Ladder is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We inject target pin codes into the browser session via cookies and API headers. This ensures the pricing, stock availability, and delivery estimates reflect the exact geographic zone you are targeting.
Yes. We map parent-child relationships for all variant combinations, ensuring every fabric colour, wood finish, and size option is extracted as a distinct, linked record.
We extract the direct CDN URLs for main images, lifestyle shots, and material swatches. This keeps the pipeline fast and allows your systems to download the assets asynchronously.
We can configure pipelines to run daily catalogue refreshes or hourly checks on a specific subset of high-priority SKUs to monitor flash sales and stock changes.
Yes. We extract the grouped SKUs featured in room concepts, allowing you to understand how products are bundled and marketed together.
Engagements typically start with a defined category or SKU list. Contact us with your specific data requirements and delivery cadence for a scoped quote.
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 50,000 SKUs - we scope, build, and operate the pipeline. Tell us what you need.
