We extract watch listings, reference numbers, grey market pricing, box/papers status, and delivery windows from Uhrinstinkt.de. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Watch Listings objects from uhrinstinkt.de. All fields typed and schema-versioned.
"reference_number": "126610LN", "brand": "Rolex", "model": "Submariner", "price": 13450.0, "currency": "EUR", "availability_status": "In Stock", "box_included": true, "papers_included": true, "condition": "New"
| # | reference_number | brand | model | sub_model | price | retail_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specifications objects from uhrinstinkt.de. All fields typed and schema-versioned.
"reference_number": "126610LN", "caliber": "3235", "movement_type": "Automatic", "power_reserve_hours": 70, "case_material": "Oystersteel", "case_diameter_mm": 41, "water_resistance_atm": 30, "glass_type": "Sapphire crystal"
| # | reference_number | caliber | movement_type | power_reserve_hours | case_material | case_diameter_mm |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Financing objects from uhrinstinkt.de. All fields typed and schema-versioned.
"reference_number": "126610LN", "price": 13450.0, "currency": "EUR", "finance_available": true, "finance_monthly_min": 245.5, "finance_months_max": 60, "finance_apr": 7.9, "price_timestamp": "2026-05-12T10:15:00Z"
| # | reference_number | price | retail_price | discount_abs | discount_pct | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Availability & Shipping objects from uhrinstinkt.de. All fields typed and schema-versioned.
"reference_number": "126610LN", "stock_status": "Available immediately", "delivery_days_min": 1, "delivery_days_max": 3, "ships_from_country": "DE", "warranty_months": 60, "return_policy_days": 14, "free_shipping": true
| # | reference_number | stock_status | delivery_days_min | delivery_days_max | ships_from_country | warranty_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Brand Data objects from uhrinstinkt.de. All fields typed and schema-versioned.
"brand_name": "Rolex", "collection_name": "Submariner", "category": "Dive Watches", "gender": "Men's watch/Unisex", "style": "Sport", "production_status": "Active", "total_listings_count": 482
| # | brand_name | collection_name | category | gender | style | year_introduced |
|---|---|---|---|---|---|---|
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Our Uhrinstinkt pipeline parses complex technical specification tables, tracks grey market pricing against retail MSRP, and monitors exact delivery windows across thousands of references.
Capture exact manufacturer reference numbers to map inventory against your internal product databases or competitor listings.
Track actual selling prices versus recommended retail prices (MSRP) to calculate exact discount percentages per model.
Extract normalized data for calibers, case diameters, materials, and power reserves from unstructured HTML tables.
Track exact availability statuses — from 'Available immediately' to 'Delivery time 3-4 weeks' — to gauge market supply.
Identify whether a listing includes original box and papers, and verify the condition (New, Unworn, Pre-owned).
Extract minimum monthly payments, APR percentages, and maximum term lengths for watches offering consumer finance.
Scrape entire brand hierarchies, categorising watches by collection, gender, and style.
Run daily or hourly diffs to capture price drops or changes in delivery timelines without processing the entire catalogue.
Extract localized pricing and shipping estimates based on selected delivery regions.
Brief in. Clean data out.
Provide target brands, collections, or specific reference numbers. We design the extraction schema.
We configure Scrapy crawlers, handle pagination, and build regex parsers for technical specification tables.
Schema validation ensures reference numbers match standard formats and price outliers are flagged.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or delivered via Webhook.
Extracting luxury watch data requires more than simple HTTP requests. Here is how we ensure data accuracy.
Technical specifications on luxury watch sites are often inconsistently formatted. We use custom regex and NLP normalization to ensure 'Stainless Steel', 'Oystersteel', and 'Steel' map to a standard schema.
We maintain a hash index of last-seen values per reference number. Subsequent runs only push diffs — reducing compute cost and providing a clean changelog of price movements.
To prevent IP bans during full catalogue sweeps, we route requests through German residential proxies, matching the geographic footprint expected by the target server.
We strip whitespace, special characters, and brand-specific prefixes to output clean, joinable reference numbers that match your internal inventory systems.
Every pipeline run is monitored for null-rate spikes in critical fields like price or reference number, ensuring structural DOM changes are caught immediately.
Grey market dealers monitor competitor pricing to adjust their own margins and stay competitive on platforms like Chrono24.
Luxury watch manufacturers audit online retailers to identify unauthorized discounts and protect brand equity.
Analysts track the spread between retail MSRP and grey market prices to evaluate brand desirability and depreciation curves.
Traders identify mispriced models across different platforms by joining normalized reference numbers.
Dealers track delivery windows across the market to anticipate supply shortages for specific high-demand calibers.
Developers populate watch enthusiast databases and collection-tracking apps with accurate technical specifications.
"Uhrinstinkt.de provides critical visibility into the European grey market for luxury watches, but extracting normalized reference numbers and technical specs requires precision parsing."
Parsing luxury watch listings involves highly unstructured specification tables and variable accessory statuses (box and papers). DataFlirt handles the complex normalization of calibers, materials, and reference numbers, delivering structured datasets so your analysts can focus on pricing models, not regex maintenance.
Everything supported by our uhrinstinkt.de 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 for dynamic financing widgets and lazy-loaded images.
We route requests through German ISP proxies to match expected geographic traffic patterns and avoid rate limits during full catalogue extractions.
Pipelines run on AWS Lambda and ECS. Airflow handles daily scheduling and diff calculation. State is managed in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About uhrinstinkt.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and specification data is generally permissible. DataFlirt extracts only public, non-authenticated watch listings. We do not extract personal data or bypass authentication walls. Clients should consult legal counsel for their specific use cases.
We build custom regex and NLP parsers to extract and normalize data from unstructured HTML tables. This ensures that variations in caliber names or case materials are output in a standardized format.
Yes. Our change detection system monitors the availability status and delivery window text for every reference number, logging changes over time.
We can configure pipelines to run daily or hourly depending on your requirements. Daily runs are standard for full catalogue sweeps, while targeted hourly runs monitor specific high-volatility reference numbers.
Yes. We capture the minimum monthly payment, maximum term length, and APR percentage for listings that offer consumer financing.
Our minimum engagement covers weekly extraction of up to 10,000 reference numbers. Contact us for custom quotes on higher frequencies or full-site sweeps.
Absolutely. We provide a sample run of up to 200 watches as part of the pre-engagement scoping process to validate schema fit and normalization quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily price feed or a complete technical specification database — we scope, build, and operate the pipeline. Tell us what you need.
