We extract fine jewelry specifications, estate piece details, gemstone metadata, and dynamic pricing from Ross-Simons. 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 ross-simons.com. All fields typed and schema-versioned.
"sku": "914823", "title": "14kt Yellow Gold Byzantine Bracelet", "metal_type": "14kt Yellow Gold", "price": 495.0, "in_stock": true, "category": "Bracelets"
| # | sku | title | brand | category | sub_category | metal_type |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Estate Jewelry objects from ross-simons.com. All fields typed and schema-versioned.
"sku": "882190", "era": "Vintage", "condition": "Excellent", "metal_purity": "18kt", "one_of_a_kind": true, "price": 1250.0
| # | sku | title | era | condition | metal_purity | gemstone_details |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Clearance objects from ross-simons.com. All fields typed and schema-versioned.
"sku": "914823", "current_price": 295.0, "original_price": 495.0, "clearance_flag": true, "discount_pct": 40, "promotion_text": "Extra 20% Off Clearance"
| # | sku | current_price | original_price | clearance_flag | discount_pct | promotion_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Gemstone Details objects from ross-simons.com. All fields typed and schema-versioned.
"sku": "773211", "stone_type": "Diamond", "stone_cut": "Round", "carat_weight": 1.5, "stone_colour": "H", "stone_clarity": "SI1"
| # | sku | stone_type | stone_cut | stone_colour | stone_clarity | carat_weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from ross-simons.com. All fields typed and schema-versioned.
"review_id": "REV-9921", "sku": "914823", "star_rating": 5, "review_title": "Classic beauty", "review_date": "2023-11-14", "verified_buyer": true
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
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Our scraper extracts deep jewelry metadata: carat weights, metal purities, estate origins, and clearance discounts. Built with residential proxies and JavaScript rendering for accurate stock detection.
Title, descriptions, metal specifications, sizes, and every metadata field Ross-Simons surfaces - scraped at SKU level with variant mapping.
Monitor one-of-a-kind vintage and estate pieces. Capture era, condition, and unique provenance details.
Extract structured data for carat weights, stone cuts, colour grades, clarity, and metal purities across all listings.
Capture current price, original list price, clearance flags, and promotional discount text - timestamped per crawl.
Extract primary and secondary product image URLs at maximum resolution for visual analysis and cataloguing.
Map available ring sizes, chain lengths, and bracelet dimensions to specific SKUs and stock statuses.
Full review text, star ratings, helpful vote counts, and verified buyer flags across all product pages.
Track in-stock status, low inventory warnings, and backorder dates for fast-moving clearance items.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, search terms, or specific SKU lists. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for ross-simons.com.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Scraping fine jewelry catalogues requires precise metadata extraction and handling of dynamic stock states. Here is how we build resilient pipelines.
Retail sites deploy bot detection based on IP reputation and request frequency. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain access.
Ross-Simons product pages rely on JavaScript for dynamic pricing updates, size availability, and image galleries. We run full Playwright browser sessions to capture data that headless HTTP clients miss.
Estate jewelry pieces are unique and disappear from the catalogue once sold. Our pipelines account for 404 errors on sold items, logging them as out-of-stock rather than failing the extraction run.
Jewelry metadata structures vary wildly between rings, necklaces, and watches. Our selector strategy uses multiple fallback chains per field to ensure consistent extraction across diverse product categories.
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 for price and stock updates.
Jewelry retailers monitor Ross-Simons pricing, clearance events, and promotional discounts to optimise their own pricing strategies.
Appraisers and vintage dealers track estate listings, conditions, and pricing to establish market values for unique pieces.
Analysts track popular gemstone cuts, metal types, and category growth to identify consumer preferences in fine jewelry.
Brands audit discount depth and clearance velocity to understand inventory liquidation trends in the jewelry sector.
Merchandising teams analyse Ross-Simons catalogue breadth across rings, bracelets, and necklaces to inform their own product development.
Machine learning teams use structured jewelry descriptions, specifications, and images to train visual recognition and recommendation models.
"Ross-Simons holds a highly structured catalogue of fine and estate jewelry. Querying this data requires a pipeline built for complex variants and one-of-a-kind inventory."
Most teams underestimate the complexity of jewelry metadata. Extracting carat weights, metal purities, and dynamic clearance pricing requires full JavaScript rendering, proxy rotation, and daily schema maintenance. DataFlirt absorbs that complexity so your engineers can focus on analysis, not infrastructure.
Everything supported by our ross-simons.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 for dynamic jewelry sizing.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request to prevent IP bans and ensure consistent access to catalogue pages.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About ross-simons.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail websites is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and specification data. We do not extract personal data or circumvent authentication walls.
We use full Playwright browser sessions to execute the JavaScript that drives the size selection and stock status UI. This ensures we capture accurate availability for every ring size or chain length variant.
Yes. Our pipelines are designed to handle the unique nature of estate jewelry. We capture specific era metadata, condition reports, and handle the logic for items that disappear from the catalogue once sold.
Pipelines can be configured to run daily or intra-day. For clearance monitoring, we typically recommend a daily refresh to capture markdown events and promotional text changes accurately.
Yes. We extract and normalise structured data for carat weights, metal purities, stone cuts, colour grades, and clarity across all applicable product listings.
Yes. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off estate catalogue dump or a continuous clearance-monitoring feed - we scope, build, and operate the pipeline. Tell us what you need.
