We extract complex jewellery configurations, dynamic metal pricing, stone mapping, and review data from Jewlr. 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 Base Listings objects from jewlr.com. All fields typed and schema-versioned.
"product_id": "JWL0014", "title": "Classic Four Stone Mother's Ring", "category": "Rings", "sub_category": "Mother's Rings", "base_price": 129.0, "currency": "USD", "rating": 4.8, "review_count": 1420
| # | product_id | title | category | sub_category | base_price | currency |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Customisation Matrices objects from jewlr.com. All fields typed and schema-versioned.
"product_id": "JWL0014", "stone_count": 4, "engraving_supported": true, "max_engraving_lines": 2, "max_chars_per_line": 15, "metal_types": "['925 Sterling Silver', '10k White Gold', '14k Rose Gold']", "stone_options": "['Simulated Birthstone', 'Genuine Birthstone']"
| # | product_id | metal_types | stone_count | stone_options | engraving_supported | max_engraving_lines |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Dynamic Pricing objects from jewlr.com. All fields typed and schema-versioned.
"product_id": "JWL0014", "selected_metal": "14k Yellow Gold", "selected_stones": "Genuine Birthstone", "final_price": 489.0, "list_price": 549.0, "discount_pct": 11, "ships_by_date": "2026-05-20"
| # | product_id | selected_metal | selected_stones | chain_selection | final_price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews objects from jewlr.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "product_id": "JWL0014", "reviewer_name": "Sarah M.", "star_rating": 5, "verified_buyer": true, "review_date": "2026-03-12", "helpful_votes": 14
| # | review_id | product_id | reviewer_name | star_rating | review_date | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Categories objects from jewlr.com. All fields typed and schema-versioned.
"category_id": "CAT-042", "name": "Promise Rings", "slug": "promise-rings", "parent_category": "Rings", "product_count": 342, "meta_title": "Custom Promise Rings | Jewlr", "meta_description": "Shop personalised promise rings with custom engravings and birthstones."
| # | category_id | name | slug | parent_category | product_count | banner_image_url |
|---|---|---|---|---|---|---|
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Jewlr relies on highly dynamic product pages where pricing and images change based on metal, stone, and engraving selections. Our scraper handles the JavaScript execution required to map these permutations.
Capture titles, base prices, descriptions, and default imagery across all categories including rings, necklaces, and charms.
Extract all possible combinations of metals (Silver, 10k/14k Gold), stones (simulated vs genuine), and chain lengths per product.
Record the exact price delta for every customisation option, tracking how a base $99 ring scales to $500+ with genuine stones and gold.
Map the structural limits of personalisation: maximum characters, allowed fonts, and line counts per specific item.
Capture the generated 3D render URLs that correspond to specific metal and stone configurations.
Extract full review text, star ratings, and verified buyer status to analyse customer sentiment on specific product lines.
Track dynamic dispatch dates that fluctuate based on the complexity of the selected customisation.
Map the entire site hierarchy from top-level navigation down to specific sub-collections and seasonal campaigns.
Run continuous pipelines to detect changes in base metal pricing, new product additions, or promotional discounts.
Brief in. Clean data out.
Provide target categories, product lists, or full catalogue requirements. We design the variant schema together.
We configure Playwright crawlers to handle the JavaScript product configurator and extract dynamic pricing arrays.
Schema validation, null-rate checks, and variant price outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Jewlr's frontend is entirely state-driven. Here is how we extract structured data from complex interactive components.
Jewlr's pricing and image renders do not exist in the static HTML. We run full Playwright browser sessions to interact with the UI, selecting different metals and stones to trigger the XHR requests that return final pricing.
A single mother's ring with 5 stones and 6 metal options generates thousands of combinations. Our pipeline intelligently iterates through the configuration matrix to build a complete pricing table without redundant requests.
Frequent state changes and rapid API calls trigger rate limits. Our crawlers use residential ISP proxies with realistic request pacing to maintain uninterrupted access to the product APIs.
eCommerce frontends update frequently. We use multi-layer fallback chains targeting data attributes, JSON payloads in the page source, and visual DOM elements to ensure the pipeline survives minor site updates.
We maintain a hash index of base prices and variant modifiers. Subsequent runs only push diffs, reducing compute cost and downstream processing load when tracking daily precious metal price adjustments.
Direct-to-consumer jewellery brands monitor Jewlr's base prices and premium modifiers for 14k gold and genuine stones to optimise their own pricing strategies.
Merchandising teams analyse category expansion, new product launches, and review velocity to identify trending styles in personalised jewellery.
Analysts track the ratio of simulated versus genuine stone offerings to gauge consumer price sensitivity and market positioning.
Machine learning teams use the structured combinations of metals, stones, and engravings as training data for generative design models.
Brands mine the review corpus to understand customer pain points regarding sizing, shipping times, or material quality.
Supply chain teams correlate product visibility and review growth with seasonal events (Mother's Day, Valentine's Day) to model demand.
"Jewlr contains millions of potential product permutations based on metal and stone selections - none of which are accessible via a simple static crawl."
Extracting custom jewellery data requires executing heavy JavaScript to generate variant pricing and rendering dynamic product images. DataFlirt handles the browser automation and configuration matrices so your engineering team can focus on data modelling rather than maintaining complex DOM selectors.
Everything supported by our jewlr.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 the JavaScript rendering required to operate the Jewlr product configurator.
We maintain pools of residential ISP proxies to distribute request load and prevent rate-limiting when mapping extensive variant matrices.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management, ensuring data is delivered strictly on your requested cadence.
Data delivered to where your team already works — no new tooling required.
About jewlr.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing, product details, and reviews from Jewlr is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract personal user data or circumvent authentication walls. Clients should consult legal counsel for their specific use cases.
We do not brute-force every combination via the frontend UI. Our pipeline analyses the configuration payload and API endpoints to extract the underlying pricing logic and variant modifiers, allowing us to reconstruct the full matrix efficiently.
Yes. When a user selects a specific metal and birthstone, Jewlr generates a specific image render. We capture the URL patterns and parameters required to link these images to their respective data rows.
We can schedule full catalogue refreshes at daily or weekly cadences. Because precious metal prices fluctuate, we ensure the base prices and variant modifiers reflect the current live state at the time of extraction.
Yes. We paginate through the review sections on product pages, extracting the text, rating, date, and verified buyer status for sentiment analysis.
Our smallest packages start at a defined category list with weekly delivery. For full-catalogue matrix extraction, we price based on compute volume and delivery frequency. Contact us for a scoped quote.
Absolutely. We provide a sample run of up to 100 base products and their complete variant matrices as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price monitoring across the customisation matrix, we scope, build, and operate the pipeline. Tell us what you need.
