We extract diamond specifications, designer collections, pricing signals, and store inventory from Kay Jewelers. 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 Engagement Rings objects from kay.com. All fields typed and schema-versioned.
"sku": "994012345", "title": "Neil Lane Engagement Ring 1 ct tw Diamonds 14K White Gold", "collection": "Neil Lane Bridal", "metal_type": "14K White Gold", "diamond_carat": "1.00", "diamond_color": "I", "diamond_clarity": "I1", "price": 2999.99, "list_price": 3499.99, "sizing_available": "['5', '5.5', '6', '6.5', '7', '7.5', '8']"
| # | sku | title | collection | metal_type | diamond_carat | diamond_color |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Diamonds objects from kay.com. All fields typed and schema-versioned.
"sku": "D-1029384", "shape": "Round", "carat": "1.50", "color": "H", "clarity": "VS2", "cut": "Ideal", "symmetry": "Excellent", "polish": "Excellent", "fluorescence": "None", "certificate_type": "GIA", "price": 8450.0
| # | sku | shape | carat | color | clarity | cut |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from kay.com. All fields typed and schema-versioned.
"sku": "994012345", "base_price": 3499.99, "current_price": 2999.99, "discount_pct": 14.2, "promotion_name": "Bridal Event", "clearance_flag": false, "vault_value_flag": false, "scraped_at": "2026-08-14T10:15:22Z"
| # | sku | base_price | current_price | discount_pct | promotion_name | clearance_flag |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Watches objects from kay.com. All fields typed and schema-versioned.
"sku": "W-882341", "brand": "Bulova", "model": "Marine Star", "movement": "Automatic", "case_material": "Stainless Steel", "dial_colour": "Blue", "strap_material": "Silicone", "water_resistance": "200m", "price": 495.0
| # | sku | brand | model | movement | case_material | dial_colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Inventory objects from kay.com. All fields typed and schema-versioned.
"store_id": "KY-1042", "store_name": "Mall of America", "address": "123 Retail Blvd, Bloomington, MN", "zip_code": "55425", "sku": "994012345", "in_stock_status": true, "pickup_available": true, "distance_miles": 3.2
| # | store_id | store_name | address | zip_code | sku | in_stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Kay Jewelers pipeline handles complex product matrices: diamond 4Cs, designer collections, ring sizing availability, and regional store inventory.
Extract granular diamond details including carat weight, cut, colour, clarity, symmetry, and certification type for every SKU.
Categorise inventory by designer collections such as Neil Lane, Le Vian, and Center of Me.
Capture Vault Value pricing, clearance markdowns, and promotional event discounts timestamped per crawl.
Parse metal purity and type (10K, 14K, 18K, platinum, sterling silver) accurately from descriptions and metadata.
Simulate regional zip codes to extract Buy Online, Pick Up In Store (BOPIS) availability and local stock levels.
Extract available ring sizes per SKU, identifying out-of-stock sizes and custom sizing options.
Pull detailed horological data including movement type, case diameter, dial colour, and water resistance.
Capture primary, alternate, and 360-degree view image URLs for visual analysis and catalogue matching.
Extract customer ratings, review text, and helpful votes to gauge sentiment on specific designs.
Brief in. Clean data out.
Provide target categories, designer names, or specific zip codes for inventory tracking.
We configure Playwright crawlers to handle dynamic pricing widgets and regional inventory modals.
Schema validation ensures diamond 4Cs and metal types are correctly parsed and normalised.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Jewellery retail sites rely heavily on dynamic frontend frameworks and regional inventory systems. Here is how we extract data reliably.
Kay's promotional pricing and Vault Value discounts are often loaded asynchronously via JavaScript after the initial page load. We execute full Playwright sessions to ensure the final, rendered price is captured rather than the base HTML price.
Store availability changes based on user location. Our pipeline injects target zip codes into the session state, allowing us to map inventory levels across hundreds of regional stores simultaneously.
Diamond specifications are frequently buried in unstructured description paragraphs. We use regex and NLP extraction models to parse out exact carat weights, colour grades, and clarity ratings into structured JSON fields.
Retail sites use perimeter protection to block volumetric scraping. We route requests through US-based residential ISP proxies with realistic browser fingerprints to maintain uninterrupted access to the catalogue.
A single ring design may have dozens of variants based on metal type and ring size. We map parent-child SKU relationships so every specific combination is tracked accurately.
Jewellery retailers track Kay's promotional events and base pricing to adjust their own discount strategies.
Industry analysts aggregate retail pricing across specific carat and clarity brackets to map consumer diamond trends.
Brands like Bulova or Le Vian monitor their own SKUs on Kay to ensure MAP compliance and proper brand representation.
Supply chain analysts track stockouts on popular bridal designs across regional stores to forecast demand.
Fashion tech companies use structured jewellery metadata to train visual similarity and style-matching models.
Market researchers analyse the influx of lab-grown versus natural diamonds in Kay's catalogue over time.
"Kay Jewelers holds critical pricing and specification data for the mass-market diamond industry, but extracting it requires parsing complex gemological matrices."
Most teams underestimate the difficulty of scraping jewellery retail sites. Reliable extraction requires rendering dynamic pricing widgets, mapping complex diamond specification matrices, and simulating regional zip codes for store-level inventory. DataFlirt manages this infrastructure so your team can focus on market analysis rather than DOM maintenance.
Everything supported by our kay.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, zip code simulation, and interaction flows.
We maintain pools of US residential ISP proxies. Rotation happens per-request with sticky sessions where regional inventory consistency is required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About kay.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail sites is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data or circumvent authentication walls.
Yes. We can simulate sessions for specific zip codes to extract Buy Online, Pick Up In Store (BOPIS) availability and local stock status for any given SKU.
Our extraction schema uses regex and NLP parsing to pull exact specifications (carat, cut, colour, clarity) from product descriptions and feature bullets, delivering them as normalised data fields.
Yes. We capture base price, current price, discount percentages, and specific promotional flags (like Vault Value or clearance) during each pipeline run.
Pipelines can be scheduled at daily or weekly cadences. Full catalogue refreshes typically complete within 4-6 hours depending on the depth of the category tree.
Yes. We map products to their respective collections, allowing you to filter the dataset for specific brands like Neil Lane, Le Vian, or Bulova.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily pricing feed or a comprehensive extraction of diamond specifications, we scope, build, and operate the pipeline. Tell us what you need.
