We extract gemstone listings, auction histories, carat specifications, and seller ratings from Gem Rock Auctions. 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 Auction Listings objects from gemrockauctions.com. All fields typed and schema-versioned.
"item_id": "1849201", "title": "2.45 ct Natural Unheated Blue Sapphire", "gemstone_type": "Sapphire", "weight_carats": 2.45, "current_bid": 450.0, "bid_count": 14, "ends_at": "2026-05-14T18:00:00Z", "reserve_met": true
| # | item_id | title | category | gemstone_type | weight_carats | dimensions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Gemstone Specs objects from gemrockauctions.com. All fields typed and schema-versioned.
"item_id": "1849201", "gemstone_type": "Sapphire", "colour": "Vivid Blue", "cut": "Oval Mixed", "clarity": "VVS", "treatment": "Unheated", "origin": "Sri Lanka", "certification": "GIA Report Included"
| # | item_id | gemstone_type | colour | shape | cut | clarity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bid History objects from gemrockauctions.com. All fields typed and schema-versioned.
"item_id": "1849201", "bid_id": "b_94821", "bidder_username": "gemhunter99", "bid_amount": 450.0, "currency": "USD", "bid_time": "2026-05-13T14:22:10Z", "auto_bid": true
| # | item_id | bid_id | bidder_username | bid_amount | bid_time | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from gemrockauctions.com. All fields typed and schema-versioned.
"seller_id": "s_4021", "username": "OpalKings", "store_name": "Opal Kings Australia", "feedback_score": 14502, "positive_pct": 99.8, "items_sold": 48291, "sheriff_approved": true, "country": "Australia"
| # | seller_id | username | store_name | feedback_score | positive_pct | items_sold |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Buy It Now objects from gemrockauctions.com. All fields typed and schema-versioned.
"item_id": "1928402", "title": "1.10 ct Emerald Cut Emerald", "price": 1200.0, "currency": "USD", "make_offer_available": true, "seller_name": "ColombianGems", "shipping_cost": 25.0, "return_policy": "14 days money back"
| # | item_id | title | category | price | currency | make_offer_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Gem Rock Auctions scraper handles dynamic bid components, normalises complex gemological specifications, and bypasses bot protection to deliver structured auction intelligence.
Parse unstructured description fields into normalised columns for carat weight, colour, clarity grading, cut type, and treatment disclosures.
Monitor starting bids, reserve prices, bid counts, and final closing prices. Track bidder usernames to identify market participants.
Isolate and track No Reserve auctions in real time. Configure high-frequency polling as auctions approach closing times.
Extract verification status and audit notes from the Gemstone Sheriff program to assess listing accuracy and seller credibility.
Aggregate seller feedback scores, total items sold, geographic origin, and return policies across the marketplace.
Track fixed-price inventory, capture Make Offer eligibility, and monitor price drops on stale inventory.
Normalise gemstone origin data (e.g., Lightning Ridge Opals, Muzo Emeralds) for precise market valuation models.
Capture high-resolution image URLs and embedded video links for ML training or external catalogue syncing.
Maintain a hash index of active listings. Only push data when new bids are placed or specifications are updated.
Brief in. Clean data out.
Select target categories, gemstone types, or specific sellers. We map the extraction schema to your requirements.
We configure crawlers to handle pagination, parse specification tables, and manage session state for accurate bid data.
Schema validation ensures carat weights, prices, and dates are strictly typed. We monitor for null-rate spikes.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed schedule.
Gem Rock Auctions updates bids dynamically and uses complex DOM structures for specifications. We normalise the chaos.
Auction platforms rely on websockets and XHR requests for live bid updates. We utilise Playwright to render these dynamic components, ensuring the captured bid is strictly accurate at the time of extraction.
Sellers input gemstone specs inconsistently. We apply regex and NLP-based parsing to normalise fields like 'carat', 'treatment', and 'clarity' into structured, queryable columns.
High-frequency polling of ending auctions triggers rate limits. We distribute requests across a pool of residential IPs with rotated browser fingerprints to maintain uninterrupted access.
Instead of re-delivering static listing data, our pipeline computes state differences. You receive payloads only when a new bid is placed or an auction concludes, reducing downstream ingest costs.
High-quality imagery and video are critical for gemstone assessment. We extract the source URLs from the underlying CDN, bypassing lazy-loading mechanisms.
Jewelers and appraisers build pricing models based on historical auction clear prices for specific carat, colour, and clarity combinations.
Dealers monitor No Reserve auctions and Make Offer listings to source inventory below wholesale market value.
Marketplace sellers track competitor inventory turnover, pricing strategies, and customer feedback to optimise their own listings.
Alternative asset investors track appreciation trends for investment-grade gemstones like untreated sapphires and rubies.
Computer vision teams pair high-resolution gemstone images with structured grading data to train automated assessment models.
Analysts monitor search velocity and bid frequency to identify emerging consumer preferences for specific gemstone cuts or origins.
"Gem Rock Auctions contains the most transparent pricing data for coloured gemstones globally, but extracting structured specs from varied seller inputs requires engineered normalisation."
Building a scraper for an auction site is simple; maintaining state across thousands of concurrent auctions while normalising complex gemological data is an infrastructure challenge. DataFlirt manages the residential proxies, JavaScript rendering, and schema validation so your team receives clean, warehouse-ready data.
Everything supported by our gemrockauctions.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 orchestrates the crawl and deduplicates listings. Playwright executes JavaScript to capture real-time bid updates and render dynamic specification tables.
We utilise residential ISP proxies to avoid IP bans during aggressive polling of ending auctions. Rotation prevents rate-limiting across the marketplace.
Pipelines execute on AWS Lambda for burst scaling during auction peaks. Airflow manages scheduling and dependencies, with state tracked in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About gemrockauctions.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available listing, bid, and seller data is generally permissible. DataFlirt extracts only public, non-authenticated information. We do not bypass login walls to access private messages or user account details. Clients must ensure their specific use case complies with applicable laws and the platform's Terms of Service.
Yes. We configure streaming pipelines that identify No Reserve auctions and increase polling frequency as the auction approaches its end time, delivering updates via Webhook.
Sellers often use varied formats for carat, cut, and clarity. We apply custom parsing logic and NLP to extract these values from titles and unstructured descriptions, outputting them into strictly typed columns.
Yes. We capture the complete bid history table for each listing, including bidder usernames, bid amounts, and precise timestamps.
Absolutely. Pipelines can be scoped to specific categories (e.g., Opals, Sapphires), specific sellers, or items meeting certain criteria (e.g., Buy It Now only).
Frequency is configurable. We support daily catalogue sweeps for inventory tracking, and sub-minute polling for active auctions nearing completion.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of historical auction data or a continuous feed of active listings — we scope, build, and operate the pipeline. Tell us what you need.
