We extract product listings, regional pricing signals, size availability, and promotional campaigns from Pomelo. 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 pomelo.com. All fields typed and schema-versioned.
"sku": "PML-DRS-8921-BLK", "product_id": "8921", "title": "Midi Slip Dress", "category": "Clothing", "sub_category": "Dresses", "colour": "Black", "fabric_composition": "100% Polyester", "currency": "THB"
| # | sku | product_id | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from pomelo.com. All fields typed and schema-versioned.
"sku": "PML-DRS-8921-BLK", "region": "SG", "currency": "SGD", "original_price": 49.9, "current_price": 34.9, "discount_percentage": 30, "pomelo_perks_eligible": true, "promo_badge": "Sale"
| # | sku | region | currency | original_price | current_price | discount_percentage |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizing objects from pomelo.com. All fields typed and schema-versioned.
"sku": "PML-DRS-8921-BLK", "size": "M", "in_stock": true, "stock_level": 4, "low_stock_warning": true, "waitlist_available": false, "region_availability": "['SG', 'TH', 'MY']"
| # | sku | size | in_stock | stock_level | low_stock_warning | restock_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from pomelo.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "sku": "PML-DRS-8921-BLK", "rating": 4.5, "review_title": "Perfect fit", "fit_feedback": "True to size", "date_posted": "2026-03-14", "verified_purchase": true
| # | review_id | sku | rating | review_title | review_body | reviewer_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Taxonomy objects from pomelo.com. All fields typed and schema-versioned.
"category_id": "CAT-102", "category_name": "Midi Dresses", "parent_category": "Dresses", "breadcrumb_trail": "['Clothing', 'Dresses', 'Midi Dresses']", "product_count": 412, "is_new_arrival": false, "is_sale_category": false
| # | category_id | category_name | parent_category | breadcrumb_trail | product_count | sort_order |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Extract fast-fashion data at the granular level. We handle regional geofencing, infinite scroll pagination, and dynamic inventory states to deliver clean product records.
Scrape titles, descriptions, fabric composition, care instructions, and model measurements across all clothing and accessory categories.
Track in-stock status and low-stock warnings for every size variant (XXS to XXL) per SKU.
Capture region-specific pricing and currency conversions across Thailand, Singapore, Malaysia, Indonesia, and global storefronts.
Monitor original prices, current prices, discount percentages, and campaign-specific promo badges.
Extract CDN URLs for all product gallery images, preserving high-resolution assets for visual analysis.
Aggregate customer ratings, written reviews, and specific fit feedback (e.g., runs small, true to size).
Detect fresh SKU additions in real time to analyse fast-fashion release cycles and trend adoption.
Identify items eligible for loyalty program discounts and cash-back multipliers.
Receive only updated records for price changes and stock movements, optimising warehouse storage and compute.
Brief in. Clean data out.
Specify target regions, categories, and extraction frequency. We map the Pomelo schema to your requirements.
We deploy Playwright crawlers, configure regional residential proxies, and handle dynamic content loading.
Automated checks for null rates, price anomalies, and missing variants before production deployment.
Structured data pushed to your S3 bucket, BigQuery, or via Webhook on your defined schedule.
Modern retail sites use dynamic rendering and geo-blocking. Here is how we maintain reliable extraction pipelines.
Pomelo relies heavily on client-side rendering and infinite scroll for category pages. We utilise full Playwright sessions to execute JavaScript, trigger scroll events, and hydrate the DOM before extraction.
Pricing and inventory vary significantly between Thailand, Singapore, and global storefronts. We route requests through residential proxies physically located in the target region to capture accurate local data.
Clothing items contain complex variant matrices (colour x size). Our pipeline traverses the underlying JSON state objects to map exact stock levels for every permutation without requiring manual interaction.
Aggressive scraping triggers CDN blocks and API rate limits. We implement exponential backoff, request jitter, and IP rotation to maintain extraction velocity without degrading target site performance.
Retail sites frequently update their frontend frameworks. We use hybrid selection strategies combining CSS, XPath, and Next.js data object extraction to prevent pipeline failure during site updates.
Fashion retailers track Pomelo's pricing strategy, discount depth, and promotional calendars to adjust their own positioning.
Analysts monitor new arrival velocity and category expansion to identify emerging fast-fashion trends in Southeast Asia.
Supply chain teams track stock-out rates and restock frequencies to estimate sales volume and production cycles.
Machine learning teams harvest high-resolution product imagery and category metadata to train computer vision models for apparel.
Merchants analyse price parity between Thai, Singaporean, and Malaysian storefronts to identify regional margin opportunities.
Retail strategists study how Pomelo phases out end-of-season stock through tiered discounting and flash sales.
"Fast fashion moves quickly. Without automated pipelines tracking SKU-level changes daily, you are analysing last week's market dynamics."
Extracting data from modern omnichannel retailers requires more than simple HTTP requests. Pomelo's use of React, infinite scroll pagination, and strict regional pricing demands a sophisticated infrastructure. DataFlirt manages the proxies, browser rendering, and schema maintenance so you receive clean, normalised data ready for immediate analysis.
Everything supported by our pomelo.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, infinite scroll, and dynamic content hydration.
We maintain pools of residential ISP proxies across Southeast Asia. Rotation happens per-request to bypass regional blocks and capture accurate local pricing.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About pomelo.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We use region-specific residential proxies to load the localized versions of pomelo.com, allowing us to extract accurate pricing in THB, SGD, MYR, IDR, or USD.
Our Playwright integration simulates real user scroll behaviour, forcing the Next.js frontend to hydrate the DOM with subsequent product batches until the category is exhausted.
We can configure pipelines to run daily or hourly depending on your requirements. Delta exports ensure you only process records where stock availability has changed.
We extract the direct CDN URLs for all high-resolution gallery images. We do not host the images, but provide the URLs in the structured data output.
Yes. Our schema captures exact variant availability, including specific sizes that are out of stock, low in stock, or available for waitlist.
Scraping publicly available product and pricing data is generally permissible. DataFlirt extracts only public information and does not bypass authentication walls or collect personally identifiable information.
20-minute scoping call. Pilot dataset within the week. Production within two. Specify your target categories and delivery frequency. We build, monitor, and maintain the extraction infrastructure.
