We extract outerwear listings, size and colour permutations, stock availability, and review metrics from Carhartt. 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 Product Listings objects from carhartt.com. All fields typed and schema-versioned.
"style_number": "103828", "title": "Detroit Jacket", "category": "Men > Outerwear", "price": 109.99, "available_colours": "['Carhartt Brown', 'Black', 'Navy']", "fabric_tech": "['Rugged Flex']", "fit_type": "Relaxed Fit", "rating": 4.7
| # | style_number | title | category | sub_category | price | available_colours |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Inventory & Variants objects from carhartt.com. All fields typed and schema-versioned.
"style_number": "103828", "variant_id": "103828_BRN_L_REG", "colour": "Carhartt Brown", "size": "Large Regular", "stock_status": "In Stock", "low_stock_warning": false, "price": 109.99, "scraped_at": "2026-05-12T09:14:00Z"
| # | style_number | variant_id | colour | size | stock_status | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from carhartt.com. All fields typed and schema-versioned.
"review_id": "REV-839210", "style_number": "103828", "rating": 5, "title": "Classic for a reason", "date": "2026-04-18", "verified_buyer": true, "fit_rating": "True to size", "helpful_votes": 14
| # | review_id | style_number | rating | title | text | date |
|---|---|---|---|---|---|---|
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| 2 | ||||||
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Complete list of extractable fields for Fabric & Tech Specs objects from carhartt.com. All fields typed and schema-versioned.
"style_number": "103828", "material_composition": "100% Cotton Ringspun Duck", "weight_oz": "12", "technologies": "['Rugged Flex']", "lining_material": "Blanket Lining", "features": "['Corduroy-trimmed collar', 'Left-chest pocket with zipper']", "care_instructions": "Machine wash warm"
| # | style_number | material_composition | weight_oz | technologies | care_instructions | origin |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category & Search objects from carhartt.com. All fields typed and schema-versioned.
"keyword": "winter jackets", "category_path": "Men > Outerwear > Winter Jackets", "position": 3, "style_number": "104050", "title": "Washed Duck Insulated Active Jac", "price": 129.99, "badges": "['Bestseller']", "rating": 4.8
| # | keyword | category_path | position | style_number | title | price |
|---|---|---|---|---|---|---|
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Our Carhartt scraper handles every layer of the catalogue: complex size-colour matrices, dynamic stock indicators, fabric specifications, and customer review corpora.
Title, category, fit type, description, and high-resolution image URLs scraped at the style level.
Capture every combination of size, length, and colour for complex apparel listings.
Monitor inventory status, backorder dates, and low-stock warnings across all permutations.
Extract proprietary technology tags like Rugged Flex, Rain Defender, and Force, along with material weights.
Track MSRP, current price, and clearance markdowns to monitor discount strategies.
Full review text, ratings, fit feedback, and helpful votes paginated across all product reviews.
Crawl full category trees to map the site hierarchy and track product positioning.
Extract source URLs for product photography, including detail shots and flat lays.
Run continuous pipelines at daily cadences with change-detection diffing to monitor stock drops.
Brief in. Clean data out.
Provide categories, search terms, or style numbers. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for carhartt.com.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Apparel sites use complex JavaScript frameworks for variant selection and inventory checks. Here is how we extract reliable data.
Retail sites deploy strict bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management to ensure uninterrupted access.
Carhartt product pages rely heavily on JavaScript to update prices and stock status when a user selects a size or colour. We use Playwright to execute these scripts and capture the correct data for every permutation.
E-commerce DOM structures change frequently during sales or site updates. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing variants, and coverage drops.
Retailers monitor Carhartt pricing, clearance events, and discount depths to adjust their own promotional strategies.
Merchandising teams analyse size and colour availability to understand demand patterns and inform their own buys.
Supply chain analysts track stockouts and replenishment cycles on core workwear items.
Product development teams mine reviews for feedback on fit, durability, and fabric performance to guide future designs.
Computer vision and NLP models are trained on high-quality product imagery and detailed apparel descriptions.
Brands track authorised retailer pricing against direct-to-consumer channels to ensure parity.
"Carhartt's catalogue represents the industry standard for workwear durability and pricing, but extracting its complex variant matrices requires dedicated infrastructure."
Most teams underestimate the investment required: reliable apparel scraping requires handling intricate size-colour permutations, dynamic inventory endpoints, residential proxies, and full JavaScript rendering. DataFlirt absorbs that complexity so your engineers can focus on analysis.
Everything supported by our carhartt.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 and variant interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions to maintain state during variant extraction.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About carhartt.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product and pricing information is generally permissible. DataFlirt targets only public, non-authenticated data. Clients should review site Terms of Service and consult legal counsel.
We use residential ISP proxies, full Playwright browser sessions, and realistic request timing to ensure reliable extraction without triggering blocks.
Yes. Our pipeline iterates through all available options on the product page to capture price, stock status, and identifiers for every specific variant.
Pipelines can be configured to run daily or multiple times a day to capture stock changes and clearance updates promptly.
Yes. We paginate through the review sections to extract ratings, text, fit feedback, and helpful votes.
Engagements typically start with a defined list of categories or styles delivered on a weekly cadence. Contact us for a precise quote based on volume.
Yes. We provide a sample run of up to 50 styles to validate schema fit and data completeness before contracting.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous stock-monitoring feed, we build and operate the pipeline. Tell us what you need.
