We extract apparel listings, pricing signals, vendor catalogues, scrapbook looks, and user reviews from Limeroad. 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 limeroad.com. All fields typed and schema-versioned.
"product_id": "18492041", "title": "Black Cotton Kurta Set", "brand": "Aurelia", "price": 1299.0, "mrp": 2599.0, "discount_pct": 50, "colour": "Black", "fabric": "Cotton", "size_options": "['S', 'M', 'L', 'XL']", "in_stock": true
| # | product_id | title | brand | vendor_name | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Scrapbooks & Looks objects from limeroad.com. All fields typed and schema-versioned.
"scrapbook_id": "sb_98412", "creator_name": "Priya Sharma", "theme_title": "Festive Evening Wear", "likes_count": 342, "products_included": "['18492041', '19283012', '17482910']", "total_look_price": 4597.0, "style_tags": "['ethnic', 'festive', 'black']", "creation_date": "2023-10-14T08:22:00Z"
| # | scrapbook_id | creator_name | creator_handle | theme_title | likes_count | shares_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from limeroad.com. All fields typed and schema-versioned.
"product_id": "18492041", "current_price": 1299.0, "mrp": 2599.0, "discount_pct": 50, "lrm_credits_applicable": 129, "stock_status_per_size": "['S:in_stock', 'M:out_of_stock', 'L:in_stock']", "delivery_estimate_days": 4, "price_timestamp": "2023-11-02T14:30:00Z"
| # | product_id | current_price | mrp | discount_pct | lrm_credits_applicable | size_skus |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from limeroad.com. All fields typed and schema-versioned.
"review_id": "rv_849201", "product_id": "18492041", "user_name": "Neha V.", "star_rating": 4, "review_text": "Good fabric, but fits a bit tight around the shoulders. Order one size up.", "verified_buyer": true, "size_purchased": "M", "helpful_votes": 14
| # | review_id | product_id | user_name | star_rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Limeroad blends traditional eCommerce with social discovery. Our scrapers navigate infinite-scroll feeds, dynamic React components, and complex variant structures to deliver structured apparel data.
Capture titles, brands, fabric composition, wash care instructions, size availability, and high-resolution image URLs across all fashion categories.
Extract user-generated looks, including creator details, engagement metrics (likes/shares), and the exact product IDs that make up the outfit.
Monitor MRP, selling price, applied discounts, and Limeroad credit applicability. Detect flash sales and promotional pricing instantly.
Track vendor assortments, identify top-performing sellers within specific ethnic or western wear categories, and monitor their catalogue size.
Extract stock availability at the SKU/size level to understand sell-through rates and identify broken sizes across the catalogue.
Run daily diffs to track new product additions and price changes, or schedule weekly full-catalogue refreshes.
Brief in. Clean data out.
Provide Limeroad categories, vendor IDs, or scrapbook themes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and infinite-scroll handling for limeroad.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern fashion sites use dynamic rendering and aggressive rate limiting. Here is how we maintain pipeline stability.
Limeroad heavily relies on React for rendering product feeds and scrapbooks. We use Playwright to execute JavaScript, trigger lazy-loading for images, and handle infinite scroll pagination to ensure complete category capture.
To bypass rate limits and IP bans, we route requests through Indian residential proxy networks. Request headers and TLS fingerprints are spoofed to mimic standard mobile and desktop browsers.
Fashion data requires precise mapping of parent products to child SKUs (sizes and colours). Our parsers normalise this nested data into flat, queryable formats suitable for relational databases.
For large catalogues, we maintain a hash index of last-seen values per field. 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, schema drift, and coverage drops — responding before you notice.
Fashion brands analyze Limeroad scrapbooks to identify emerging colour palettes, fabric preferences, and styling combinations.
Competitor platforms and D2C brands monitor Limeroad's discount structures and promotional pricing to optimise their own pricing strategies.
Retailers track category depth, new product launch velocity, and brand representation to inform their inventory purchasing decisions.
Computer vision teams use scraped apparel images and associated metadata (fabric, pattern, fit) to train visual search and recommendation models.
Marketplaces monitor vendor performance, catalogue size, and pricing strategies across Limeroad's third-party seller network.
Brands aggregate product reviews and ratings to identify quality issues, sizing complaints, and overall customer satisfaction.
"Limeroad's unique scrapbook format blends social commerce with traditional retail—creating a rich dataset of styling preferences and product affinities."
Extracting data from Limeroad requires navigating infinite-scroll feeds, dynamic React components, and strict rate limits. DataFlirt manages the proxy rotation, JavaScript execution, and schema maintenance so your engineers get clean, structured fashion data without the operational overhead.
Everything supported by our limeroad.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, infinite scroll, and interaction flows for React components.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request to bypass rate limits and geographical blocks.
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 limeroad.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Limeroad is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, vendor, and scrapbook data. We do not extract personal user data or circumvent authentication walls.
We use Playwright to execute JavaScript and simulate user scrolling behaviour. This ensures we trigger all lazy-loaded API calls and capture complete category and scrapbook feeds without missing items.
Yes. We extract the scrapbook metadata (theme, likes, creator) and the specific product IDs embedded within the look, allowing you to map styling preferences to actual inventory.
For targeted SKU lists, we can configure hourly pipelines to capture flash sales and dynamic pricing. Full catalogue refreshes typically run on a daily or weekly cadence depending on volume.
Our smallest packages start at a defined category or vendor list (typically 10,000-50,000 SKUs) with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
Yes. We provide a sample run of up to 1,000 products or 100 scrapbooks as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off apparel catalogue dump or a continuous price-monitoring feed across categories — we scope, build, and operate the pipeline. Tell us what you need.
