We extract designer listings, condition grades, pricing signals, and authenticity details from Fashionphile. 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 fashionphile.com. All fields typed and schema-versioned.
"id": "142985", "brand": "Hermes", "title": "Togo Birkin 30 Black", "category": "Handbags", "condition": "Excellent", "price": 18500.0, "retail_price": 12500.0
| # | id | brand | title | category | sub_category | condition |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Discounts objects from fashionphile.com. All fields typed and schema-versioned.
"id": "142985", "current_price": 18500.0, "original_price": 19500.0, "discount_pct": 5, "estimated_retail": 12500.0, "currency": "USD"
| # | id | current_price | original_price | discount_pct | estimated_retail | price_drop_history |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Condition & Authenticity objects from fashionphile.com. All fields typed and schema-versioned.
"id": "142985", "condition_grade": "Excellent", "condition_details": "Faint scratches on hardware.", "serial_number": "Z Stamp", "date_code": "2021", "inclusions": "['Box', 'Dustbag', 'Clochette', 'Lock', 'Keys']"
| # | id | condition_grade | condition_details | serial_number | date_code | authenticity_guarantee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sold Archive objects from fashionphile.com. All fields typed and schema-versioned.
"id": "138402", "brand": "Chanel", "title": "Caviar Quilted Medium Double Flap Black", "final_price": 8200.0, "sold_date": "2026-05-10T14:22:00Z", "days_on_market": 14
| # | id | brand | title | final_price | sold_date | days_on_market |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from fashionphile.com. All fields typed and schema-versioned.
"keyword": "rolex submariner", "position": 1, "id": "151902", "brand": "Rolex", "title": "Stainless Steel 40mm Submariner Date 116610LN", "current_price": 11500.0
| # | keyword | position | id | brand | title | current_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Fashionphile scraper handles the entire luxury catalogue: active listings, sold archives, condition grading, and dynamic price drops.
Title, brand, material, measurements, hardware colour, and inclusions scraped for every active item.
Capture Fashionphile's specific grading system from Flawless to Fair, including granular defect notes and smell grades.
Monitor dynamic discounting algorithms over time. Track base price, current discount, and estimated retail price.
Extract serial numbers, date codes, blind stamps, and year of manufacture for authentication models.
Scrape historical sold listings to build accurate pricing benchmarks for secondary market valuation.
Map complex hierarchies from designer brand down to specific bag models and strap configurations.
Extract unwatermarked image URLs for visual authentication and machine learning pipelines.
Capture the estimated retail price versus resale price to calculate secondary market premiums.
Run continuous pipelines with change detection to capture new arrivals and status updates instantly.
Brief in. Clean data out.
Provide designer names, categories, or URLs. We map the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session handling for fashionphile.com.
Schema validation, price-outlier detection, and null-rate checks before full launch.
JSON, CSV, or Parquet pushed to your data warehouse on agreed cadence.
Luxury resale platforms employ strict rate limiting and dynamic DOM rendering. Here is how we maintain reliable extraction.
Fashionphile blocks data centre IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access.
Listing pages load pricing and availability via JavaScript. We run full Playwright browser sessions to ensure dynamic React components render completely before extraction.
Items frequently move from active to cart holds to sold. We track these state transitions across runs to provide accurate market liquidity metrics.
We parse the underlying CDN URLs to extract the highest resolution imagery available, bypassing compressed thumbnails.
For large brand catalogues, we maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Competitors price match against Fashionphile's dynamic discount curves to optimise their own inventory.
ML models ingest high-resolution images and condition notes to train automated authentication classifiers.
Luxury houses track secondary market volume and depreciation rates to measure brand strength.
Buyers identify underpriced assets in specific categories by comparing resale prices to historical sold data.
Correlating sold velocity with brand and model types to predict future secondary market trends.
Tracking the spread between primary retail prices and secondary resale value over time.
"Fashionphile represents the most accurate pricing index for secondary luxury goods, but extracting that data requires navigating complex anti-bot systems."
Most teams underestimate the investment required: reliable Fashionphile scraping requires residential proxies, full JavaScript rendering, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our fashionphile.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 and dynamic React components.
We maintain pools of residential ISP proxies. Rotation happens per-request to bypass strict rate limiting and IP bans.
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 fashionphile.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available listing data is generally permissible. DataFlirt targets only public product, pricing, and condition data. We do not extract personal user data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions, and request timing modelled on human behaviour to prevent 403 blocks.
Pipelines can be configured for daily full-catalogue refreshes or intra-day checks on specific high-value categories to track price drops.
Yes. We can scrape historical sold listings to provide pricing benchmarks for specific brands, models, and condition grades.
Yes. We parse the CDN URLs to deliver the highest resolution unwatermarked imagery available on the platform.
Our smallest packages start at tracking specific designer categories with weekly delivery. Contact us with your use case for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily dump of Hermes bags or full catalogue monitoring across 150K listings - we scope, build, and operate the pipeline. Tell us what you need.
