We extract product catalogues, pricing signals, fabric specifications, and customisation options from Kalkifashion. 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 kalkifashion.com. All fields typed and schema-versioned.
"sku": "KLK4921", "title": "Crimson Red Bridal Lehenga", "category": "Bridal", "price": 145000.0, "currency": "INR", "fabric": "Raw Silk", "work_type": "Zardosi", "in_stock": true, "colour": "Red"
| # | sku | title | category | sub_category | collection_name | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Variants objects from kalkifashion.com. All fields typed and schema-versioned.
"sku": "KLK4921", "base_price": 145000.0, "discount_pct": 10, "discounted_price": 130500.0, "size_options": "['S', 'M', 'L', 'Custom']", "custom_tailoring_fee": 2500.0, "ready_to_ship": false, "delivery_days": 45
| # | sku | base_price | discount_pct | discounted_price | currency | size_options |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Fabric & Specifications objects from kalkifashion.com. All fields typed and schema-versioned.
"sku": "KLK4921", "primary_fabric": "Raw Silk", "lining_material": "Crepe", "embroidery_details": "Heavy Zardosi and Sequins", "care_instructions": "Dry Clean Only", "weight_kg": 3.5, "designer": "Kalki Design Studio"
| # | sku | primary_fabric | secondary_fabric | lining_material | embroidery_details | care_instructions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Category & Navigation objects from kalkifashion.com. All fields typed and schema-versioned.
"category_id": "CAT-BRIDAL-01", "category_name": "Bridal Lehengas", "parent_category": "Bridal", "product_count": 842, "seo_title": "Designer Bridal Lehengas Online", "sort_order": 1, "url_slug": "bridal-lehengas"
| # | category_id | category_name | parent_category | url_slug | product_count | seo_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from kalkifashion.com. All fields typed and schema-versioned.
"review_id": "REV-99482", "sku": "KLK4921", "reviewer_name": "Anjali M.", "rating": 5, "review_title": "Stunning craftsmanship", "review_date": "2023-11-14", "verified_buyer": true, "location": "Mumbai"
| # | review_id | sku | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Kalkifashion scraper extracts deep product metadata, including intricate fabric details, custom tailoring parameters, and multi-currency pricing logic across their entire catalogue.
Capture full catalogues of lehengas, sarees, and gowns with designer attribution.
Extract detailed material compositions, lining fabrics, and specific embroidery techniques like Zari and Zardosi.
Track pricing variations across INR, USD, GBP, and EUR to monitor international retail parity.
Map standard size charts alongside custom stitching options, measurement guides, and associated tailoring fees.
Scrape primary, secondary, and zoom-level image URLs essential for visual merchandising and AI training.
Monitor 'Ready to Ship' tags, pre-order timelines, and out-of-stock indicators at the SKU and size level.
Reconstruct the exact navigation tree from main categories down to niche sub-collections like Haldi Outfits.
Extract rich HTML descriptions, styling tips, and specific garment care instructions.
Collect customer sentiment, star ratings, and verified purchase text to analyse product reception.
Run extractions daily or weekly to catch new collection drops and seasonal sale price adjustments.
Brief in. Clean data out.
Provide target categories, collections, or specific product URLs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for kalkifashion.com.
Schema validation, null-rate checks, and data typing verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Extracting high-fidelity fashion data requires handling dynamic sizing matrices, infinite scroll collections, and regional pricing logic.
Kalkifashion alters pricing based on geolocation IP. We route requests through specific regional proxies to extract accurate localised pricing and international shipping fees.
Bridal wear involves standard sizes, custom tailoring, and blouse stitching options. Our schema normalises these nested JSON structures into flat, queryable variant rows.
Category pages use heavy JavaScript lazy loading. We utilise Playwright to simulate user scroll behaviour, ensuring no SKUs are missed in deep category archives.
Product galleries load low-res thumbnails first. Our crawlers intercept network requests to extract the underlying high-resolution CDN image URLs directly.
We maintain a hash index of known SKUs. Subsequent runs only push new arrivals or products with updated pricing, reducing your downstream processing load.
Fashion retailers monitor Kalkifashion pricing strategies across domestic and international markets.
Merchandisers track the volume of new arrivals in specific categories like pastel lehengas or fusion wear.
Computer vision teams use high-resolution ethnic wear imagery and fabric metadata to train classification models.
Analysts compare INR base prices against USD and GBP markups to understand international logistics and margin strategies.
Supply chain teams monitor ready-to-ship versus made-to-order ratios to gauge production lead times.
Textile researchers aggregate fabric composition data to identify shifts in seasonal material preferences.
"Indian ethnic couture relies on intricate metadata like fabric, work type, and custom sizing. Extracting this requires a pipeline that understands fashion taxonomy."
Off-the-shelf scrapers fail on complex apparel sites because they cannot handle nested variant matrices, regional pricing overlays, and lazy-loaded image galleries. DataFlirt engineers custom extraction logic tailored to Kalkifashion specific DOM structure, ensuring high-fidelity data delivery without the maintenance overhead.
Everything supported by our kalkifashion.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.
Combines high-speed asynchronous crawling with full JavaScript rendering for infinite scroll and dynamic pricing widgets.
Utilises country-specific residential IPs to bypass regional blocks and extract accurate localised currency data.
Pipeline includes strict data typing and null-rate monitoring to catch frontend DOM changes before they corrupt your warehouse.
Data delivered to where your team already works — no new tooling required.
About kalkifashion.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We route crawler traffic through US-based residential proxies to trigger Kalkifashion regional pricing logic, capturing the exact USD values shown to American customers.
We normalise the sizing matrix. Standard sizes are mapped to a standard variants array, while custom tailoring options, measurement requirements, and associated fees are mapped to a dedicated customisation object in the JSON schema.
Yes. We bypass the low-resolution thumbnails loaded on the frontend and extract the direct CDN URLs for the highest resolution images available for each product.
Absolutely. We configure delta-crawls that monitor specific category URLs daily, alerting you or pushing data only when new SKUs are detected.
Yes. We extract the structured specification tables, capturing primary fabric, lining material, work type, and wash care instructions into distinct database columns.
Yes. We capture Ready to Ship flags and the estimated dispatch days listed on the product page, which vary significantly between standard apparel and custom bridal wear.
20-minute scoping call. Pilot dataset within the week. Production within two. From daily price monitoring to full catalogue extraction for visual AI training. Tell us your data requirements and we will handle the infrastructure.
