We extract designer listings, condition grades, authenticity markers, and Clair appraisal indices from Rebag. 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 Designer Listings objects from rebag.com. All fields typed and schema-versioned.
"item_id": "1492837", "designer": "Chanel", "model": "Classic Double Flap Bag Quilted Caviar Medium", "category": "Bags", "condition_grade": "Excellent", "price": 8500.0, "retail_price": 10200.0, "colour": "Black", "stock_status": "In Stock"
| # | item_id | designer | model | category | sub_category | condition_grade |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Clair Pricing Data objects from rebag.com. All fields typed and schema-versioned.
"clair_code": "CH-BAG-CLDF-QCAV-MD", "designer": "Chanel", "model": "Classic Double Flap Medium", "current_resale_value": 8500.0, "retention_value_pct": 83.3, "historical_high": 9200.0, "retail_value": 10200.0, "last_updated_timestamp": "2026-05-12T09:14:00Z"
| # | clair_code | item_id | designer | model | current_resale_value | historical_high |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Condition & Authenticity objects from rebag.com. All fields typed and schema-versioned.
"item_id": "1492837", "overall_condition": "Excellent", "exterior_wear": "Minor scuffs on base corners", "hardware_wear": "Faint scratches", "inclusions": "['Dust bag', 'Authenticity card', 'Box']", "year_manufactured": "2021", "authenticity_guarantee": true
| # | item_id | overall_condition | exterior_wear | interior_wear | hardware_wear | odour |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Deals objects from rebag.com. All fields typed and schema-versioned.
"item_id": "1492837", "list_price": 8500.0, "sale_price": 8075.0, "final_sale_flag": false, "promo_eligible": true, "shipping_cost": 0.0, "currency": "USD", "price_timestamp": "2026-05-12T09:14:00Z"
| # | item_id | list_price | sale_price | final_sale_flag | promo_eligible | shipping_cost |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Watches & Jewellery objects from rebag.com. All fields typed and schema-versioned.
"item_id": "9827364", "designer": "Rolex", "model": "Datejust Automatic 36", "case_material": "Stainless Steel and 18K Yellow Gold", "dial_colour": "Champagne", "papers_included": true, "box_included": true, "year": "2019"
| # | item_id | designer | model | movement | case_material | dial_colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Rebag scraper handles every layer of the platform: designer catalogues, condition metadata, Clair appraisal values, and high-resolution imagery - with JavaScript rendering and anti-bot circumvention built in.
Extract exact designer names, model variations, hardware types, and material specifications for every listed item.
Capture the Comprehensive Luxury Appraisal Index for Resale values, including retention percentages and historical highs.
Extract granular condition reports including exterior wear, interior wear, hardware scratches, and odour notes.
Track serial codes, production years, and included accessories like dust bags, authenticity cards, and original boxes.
Capture direct URLs to high-resolution product imagery for authentication training and visual catalogue building.
Maintain exact Rebag taxonomy across bags, watches, fine jewellery, and accessories.
Track list prices, promotional discounts, and final sale markers with timestamped precision.
Monitor inventory velocity by tracking when items move from available to sold out.
Extract horological details including movement type, case material, bezel specifications, and dial colour.
Brief in. Clean data out.
Provide designer lists, category URLs, or Clair codes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for rebag.com.
Schema validation, null-rate checks, price-outlier detection, and condition mapping before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Rebag protects its proprietary Clair data and inventory catalogues with modern bot mitigation. Here is how we maintain stable extraction.
Rebag employs aggressive bot protection to guard its Clair index. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to bypass perimeter defences.
Rebag relies heavily on client-side rendering and GraphQL APIs. We intercept GraphQL network requests directly to extract clean JSON payloads for Clair data and inventory, bypassing the need to scrape the DOM where possible.
Product images are served via CDNs with dynamic sizing parameters. We normalise these URLs to extract the highest available resolution for your computer vision models.
For large designer catalogues, we maintain a hash index of last-seen values per item. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops - and respond before you notice.
Resale platforms and pawn shops use Rebag pricing data to establish baseline market values for designer goods.
Financial analysts track the Clair index to calculate asset depreciation and retention values for luxury brands.
Other luxury consignors monitor Rebag inventory velocity, discount strategies, and payout margins.
Machine learning teams use high-resolution Rebag imagery and condition notes to train counterfeit-detection models.
Fashion forecasting agencies track which designer models are flooding the resale market versus which are held by collectors.
Alternative asset funds monitor the historical price curves of specific Rolex, Patek Philippe, and Hermès models.
"Rebag's Clair index is the definitive pricing standard for luxury resale, but accessing it systematically requires overcoming aggressive anti-scraping layers."
Most teams underestimate the investment required: reliable Rebag scraping requires residential proxies, full JavaScript rendering for their React frontend, GraphQL query construction for Clair data, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our rebag.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, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 rebag.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Rebag is generally permissible under applicable law. DataFlirt targets only public, non-authenticated inventory, pricing, and Clair index data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review Rebag ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
Yes. We extract the Comprehensive Luxury Appraisal Index for Resale data, including current resale value, retail value, historical highs and lows, and the retention value percentage for specific designer models.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined designer set. Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on size.
By default, we extract the direct CDN URLs for the highest resolution images available. If your use case requires direct image downloads (e.g., for ML training), we can configure the pipeline to fetch and push the binary files directly to your S3 bucket.
Our smallest packages start at a defined category list (typically 5,000-20,000 items) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 items as part of the pre-engagement scoping process - so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across the Clair index - we scope, build, and operate the pipeline. Tell us what you need.
