We extract active inventory, sold archives, historical era categorisation, and gemstone specifications from Lang Antiques. Delivered as clean JSON, CSV, or Parquet to S3 or PostgreSQL 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 Active Jewelry Listings objects from langantiques.com. All fields typed and schema-versioned.
"sku": "110-1-9842", "title": "Art Deco Diamond Engagement Ring", "price": 14500.0, "era": "Art Deco", "metal_type": "Platinum", "diamond_ctw": 1.25, "ring_size": "6.25", "condition": "Excellent"
| # | sku | title | price | era | metal_type | gemstone_primary |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Sold Archive objects from langantiques.com. All fields typed and schema-versioned.
"archive_id": "130-1-5521", "title": "Victorian Sapphire and Diamond Cluster Ring", "sold_date": "2024-11-12", "original_price": 8200.0, "era": "Victorian", "metal_type": "18k Yellow Gold", "primary_stone": "Sapphire", "maker": "Unknown"
| # | archive_id | title | sold_date | original_price | era | metal_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Diamond & Gemstone Specs objects from langantiques.com. All fields typed and schema-versioned.
"sku": "110-1-9842", "stone_type": "Diamond", "carat_weight": 1.25, "cut": "Old European Cut", "colour": "J", "clarity": "VS2", "certification": "GIA", "measurements": "6.82 x 6.75 x 4.12 mm"
| # | sku | stone_type | carat_weight | cut | colour | clarity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Designer & Maker Data objects from langantiques.com. All fields typed and schema-versioned.
"maker_name": "Cartier", "era_active": "1847-Present", "origin_country": "France", "signature_type": "Engraved", "piece_count": 142, "category": "Fine Jewelry", "description": "Founded in Paris by Louis-Francois Cartier."
| # | maker_name | era_active | hallmark_image_url | origin_country | signature_type | piece_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Category & Taxonomy objects from langantiques.com. All fields typed and schema-versioned.
"category_name": "Engagement Rings", "sub_category": "Art Deco Engagement Rings", "url": "/engagement-rings/art-deco.html", "item_count": 412, "min_price": 2500.0, "max_price": 125000.0, "popular_era": "Art Deco"
| # | category_name | sub_category | url | item_count | min_price | max_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Lang Antiques scraper handles the complexities of unstructured antique descriptions, extracting precise gemstone metrics, historical eras, and archive pricing into a strict relational schema.
Categorise items by Victorian, Edwardian, Art Deco, Retro, and Mid-Century eras based on metadata and description parsing.
Parse unstructured text to extract carat weight, cut, colour, clarity, and certification details for primary and secondary stones.
Extract historical pricing and item specifications from the extensive Lang Antiques sold archive to build valuation models.
Download and map multiple high-resolution images per SKU, including hallmark close-ups and profile views.
Extract designer names, origin countries, and signature types from product specifications.
Identify and normalise metal types including platinum, 18k gold, palladium, and mixed metal compositions.
Extract current ring sizes, sizing constraints, and physical piece dimensions in millimetres.
Capture references to GIA, AGL, and other gemological laboratory reports linked to specific pieces.
Run daily pipelines to track new arrivals, price adjustments, and items moving to the sold archive.
Brief in. Clean data out.
Select target categories, eras, or the entire sold archive. We map the extraction schema to your database requirements.
We configure Scrapy crawlers to navigate the catalogue, parsing unstructured descriptions into strict data types.
Schema validation, null-rate checks on gemstone metrics, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or PostgreSQL database on your specified cadence.
Extracting data from Lang Antiques requires parsing complex, highly variable descriptions of unique historical items. Here is how we normalise the dataset.
Antique jewelry descriptions are narrative and highly variable. We use custom regex pipelines and NLP classification to extract strict carat weights, metal types, and eras from paragraph text, converting narrative descriptions into queryable columns.
Lang Antiques maintains a massive archive of sold pieces spanning decades. We implement deep crawler logic to paginate through thousands of historical records, bypassing dead links and extracting original list prices for valuation models.
Each piece features multiple high-resolution images crucial for visual analysis. Our pipeline asynchronously downloads, hashes, and uploads these assets to your S3 bucket, linking the object URIs back to the structured metadata record.
A Victorian rose-cut diamond is described differently than a modern brilliant cut. We normalise these variations into a unified schema, ensuring that your database can query across all eras and cut styles seamlessly.
To prevent disruption to the target site while extracting the deep archive, we implement strict concurrency limits, exponential backoff, and residential IP rotation to distribute request load safely.
Appraisers and auction houses use historical sold data to build algorithmic pricing models for estate jewelry.
Computer vision teams train models on categorised high-resolution images to automatically identify eras and cuts.
Analysts track the velocity of specific eras moving from active to sold, identifying rising demand for Art Deco or Retro pieces.
Estate jewelers monitor active listings to benchmark their own inventory pricing against a market leader.
Alternative asset funds track high-value vintage jewelry appreciation rates over time using the sold archive.
Insurance companies reference historical replacement costs for specific antique configurations and gemstone grades.
"Lang Antiques maintains the most comprehensive public archive of sold vintage jewelry. Extracting this catalogue provides the baseline for any antique valuation model."
Parsing unstructured antique jewelry descriptions requires strict schema enforcement. DataFlirt extracts carat weights, historical eras, and hallmark data from raw text, delivering normalised datasets ready for machine learning and pricing analysis. We handle the crawling complexity so your team can focus on the data.
Everything supported by our langantiques.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 deep archive crawl orchestration and deduplication. Playwright renders dynamic catalogue filters and pagination elements.
We route requests through US-based residential IPs to maintain access while extracting the extensive historical catalogue.
Pipelines run on AWS ECS. Airflow manages scheduling for daily new-arrival syncs and one-off historical backfills.
Data delivered to where your team already works — no new tooling required.
About langantiques.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and archive data. We do not attempt to bypass authentication to access customer details or internal notes.
We deploy specific crawlers that paginate through the historical archive, extracting the original list price, sale date approximations, and full item specifications to build comprehensive historical datasets.
Yes. Our pipeline downloads the highest resolution source images available for each piece, including specific hallmark and profile shots, delivering them directly to your specified S3 bucket.
We use custom regex patterns and NLP to extract structured data like carat weight, cut, colour, and clarity from the narrative descriptions provided on the product pages.
For the historical archive, we typically perform a one-off bulk extraction. For active inventory, we configure daily or weekly pipelines to capture new arrivals, price changes, and items moving to sold status.
Yes. We extract the explicitly stated era (e.g., Victorian, Art Deco) and normalise this data into a strict categorical column for easy filtering and analysis.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need the complete sold archive for valuation models or daily active inventory syncs, we build and operate the pipeline. Tell us your requirements.
