We extract diamond inventories, luxury watch specifications, pricing signals, and collection catalogues from Reeds. 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 Watches objects from reeds.com. All fields typed and schema-versioned.
"sku": "1983742", "brand": "Tudor", "title": "Black Bay 58", "price": 3950.0, "movement": "Automatic", "case_size": "39mm", "stock_status": "In Stock"
| # | sku | brand | collection | title | price | list_price |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Diamonds objects from reeds.com. All fields typed and schema-versioned.
"diamond_id": "D-102938", "shape": "Round", "carat": 1.5, "colour": "G", "clarity": "VS1", "cut": "Excellent", "price": 8450.0
| # | diamond_id | shape | carat | cut | colour | clarity |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Jewellery objects from reeds.com. All fields typed and schema-versioned.
"sku": "8472910", "category": "Engagement Rings", "metal_type": "Gold", "metal_purity": "14k", "total_carat_weight": 2.0, "price": 4200.0
| # | sku | category | title | metal_type | metal_purity | gem_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Inventory objects from reeds.com. All fields typed and schema-versioned.
"sku": "8472910", "current_price": 4200.0, "original_price": 4800.0, "discount_pct": 12.5, "online_stock": true, "scrape_timestamp": "2026-10-24T14:22:00Z"
| # | sku | current_price | original_price | discount_pct | clearance_flag | online_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from reeds.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "sku": "1983742", "rating": 5, "review_title": "Stunning timepiece", "review_date": "2026-09-15", "verified_buyer": true
| # | review_id | sku | reviewer_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
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Our Reeds scraper handles every layer of the platform: diamond search filters, watch specifications, dynamic pricing, and store-level inventory checks.
Extract the 4Cs, certification details, fluorescence, symmetry, and polish metrics from the dynamic diamond search interface.
Capture movement type, case material, dial colour, water resistance, and crystal type for high-end watch brands.
Monitor current prices, clearance discounts, and promotional pricing across the entire catalogue.
Extract URLs for primary product images, alternate angles, and 360-degree video assets.
Maintain the exact category hierarchy and collection associations for every SKU.
Check stock availability at specific Reeds retail locations using zip code or store ID inputs.
Extract customer ratings, review text, and verified buyer status across product pages.
Target specific brand landing pages like Rolex, Tudor, or Pandora to isolate competitor catalogues.
Extract base setting prices and compatible diamond options from the Build Your Own Ring tool.
Brief in. Clean data out.
Provide category URLs, brand names, or specific diamond parameters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and XHR interception for reeds.com.
Schema validation, null-rate checks, and data typing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Jewellery sites rely heavily on dynamic filtering and high-density pagination. Here is how we maintain data integrity.
The Reeds diamond search tool loads data asynchronously via API calls. We bypass the frontend rendering entirely, intercepting the raw JSON payloads to extract thousands of diamond records per minute with perfect accuracy.
Large categories like engagement rings span hundreds of pages. Our crawlers manage stateful pagination and session cookies to ensure zero dropped records across deep catalogue sweeps.
We route requests through US-based residential proxies to distribute load and avoid IP blocks, mimicking legitimate customer browsing patterns.
Watch specifications and jewellery details often use different HTML structures. We map multiple XPath and CSS fallback selectors to normalise data across disparate product templates.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Jewellery retailers track pricing on identical watch models and similar diamond specifications to adjust their own pricing algorithms.
Market analysts aggregate 4C data to track wholesale vs retail markup trends across different diamond shapes and sizes.
Dealers monitor availability of high-demand watch models to gauge primary market supply constraints.
Merchandising teams compare category depth, brand representation, and price point distribution against their own catalogues.
Watch and jewellery brands audit the site to ensure adherence to Minimum Advertised Price agreements.
Analysts track new product additions and clearance movements to identify shifting consumer preferences in metal types and gem styles.
"Reeds holds a massive, structured dataset of diamond specifications and luxury watch metadata. Extracting it requires navigating complex dynamic filters and strict rate limits."
Most teams underestimate the investment required to scrape fine jewellery catalogues. Reliable Reeds extraction requires handling dynamic XHR endpoints for diamond searches, managing residential proxy pools to avoid IP bans, and parsing highly variable metadata across watch and jewellery categories. DataFlirt absorbs that complexity so you can focus on analysis.
Everything supported by our reeds.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 executes JavaScript for dynamic product filters and stock checks.
We maintain pools of residential US proxies to bypass rate limits and geographic access restrictions.
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 reeds.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Reeds is generally permissible. DataFlirt targets only public product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We intercept the underlying XHR/API requests that populate the frontend search tool, allowing us to extract thousands of diamond records efficiently without rendering the UI for each item.
Yes. We map the specific metadata tables on luxury watch product pages to extract precise specifications including movement, case material, and water resistance.
We can configure pipelines to run daily, weekly, or at custom intervals. Change detection ensures you only process updated pricing records.
Yes. We can submit specific zip codes or store IDs during the crawl to extract local inventory status for targeted SKUs.
We extract the high-resolution image URLs. If you require the actual image files, we can configure a pipeline to download and push them to your S3 bucket.
Our minimum engagement starts with a defined category or brand list. Contact us with your specific requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full diamond inventory dump or continuous price-monitoring for luxury watches - we scope, build, and operate the pipeline. Tell us what you need.
