We extract product specifications, bundle pricing, stock levels, and customer reviews from madeincookware.com. 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 Specs objects from madeincookware.com. All fields typed and schema-versioned.
"sku": "FRY-SS-10", "product_name": "10-Inch Stainless Clad Frying Pan", "material": "5-Ply Stainless Clad", "ply_count": 5, "max_temperature": "800F", "manufacture_origin": "Italy", "weight": "2.25 lbs"
| # | sku | product_name | category | material | ply_count | max_temperature |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Inventory objects from madeincookware.com. All fields typed and schema-versioned.
"sku": "FRY-SS-10", "base_price": 109.0, "sale_price": 99.0, "currency": "USD", "discount_percentage": 9, "in_stock": true, "inventory_level": "In Stock"
| # | sku | base_price | sale_price | currency | discount_percentage | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Customer Reviews objects from madeincookware.com. All fields typed and schema-versioned.
"review_id": "REV-89211", "sku": "FRY-SS-10", "star_rating": 5, "review_title": "Heats evenly", "review_body": "Best pan I own. Heavy bottom prevents scorching.", "verified_buyer": true, "helpful_votes": 12, "review_date": "2023-11-14"
| # | review_id | sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Collections & Bundles objects from madeincookware.com. All fields typed and schema-versioned.
"bundle_id": "BNDL-SOUS", "bundle_name": "The Sous Chef Set", "total_price": 599.0, "savings_amount": 150.0, "item_count": 11, "included_skus": "['FRY-SS-10', 'SAUCE-SS-2', 'STOCK-SS-8']"
| # | bundle_id | bundle_name | url | total_price | savings_amount | item_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Media Assets objects from madeincookware.com. All fields typed and schema-versioned.
"sku": "FRY-SS-10", "primary_image_url": "https://cdn.madeincookware.com/image.jpg", "video_url": "None", "alt_text": "10 inch stainless steel frying pan on stove", "image_width": 2000, "image_height": 2000, "gallery_urls": "['https://cdn.madeincookware.com/img1.jpg', 'https://cdn.madeincookware.com/img2.jpg']"
| # | sku | primary_image_url | gallery_urls | lifestyle_image_urls | video_url | alt_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Extract deep product specifications, dynamic pricing, and rich review data from their modern headless commerce architecture.
Capture every individual pan, knife, and plate with its corresponding variant ID, colour, and size option.
Extract deep technical specifications including ply count, construction material, maximum oven temperature, and manufacturing origin.
Map complex cookware sets to their individual component SKUs, calculating base prices versus bundle savings.
Paginate through their third-party review provider to extract full text, star ratings, and verified buyer flags for every product.
Monitor inventory flags and out-of-stock indicators across all SKUs to track supply chain velocity.
Extract URLs for primary product shots, lifestyle gallery images, and instructional video assets.
Preserve the exact site taxonomy, mapping products to collections like Bakeware, Tabletop, or Knives.
Capture 'Frequently Bought Together' and 'Complete the Set' data points to map internal product associations.
Bypass HTML parsing entirely by intercepting the underlying Next.js and Shopify Storefront API JSON payloads.
Brief in. Clean data out.
Provide target categories, specific SKUs, or request a full site crawl. We design the extraction schema together.
We configure Playwright crawlers, API interceptors, and proxy rotation to handle madeincookware.com's frontend.
Schema validation, null-rate checks, and bundle math verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern DTC brands use complex frontend frameworks. Here is how we extract structured data reliably.
Instead of writing fragile CSS selectors for dynamic React components, our Playwright workers intercept the underlying JSON payloads powering the headless frontend. This yields cleaner data and avoids breakage during UI updates.
Cookware sets feature dynamic pricing based on variant selections. We execute the JavaScript bundle configurator to capture all possible price permutations and map them to their constituent SKUs.
Customer reviews are loaded asynchronously via third-party providers. We interface directly with these review APIs, paginating through thousands of historical reviews while bypassing frontend display limits.
We maintain a hash index of last-seen values for pricing and stock status. Subsequent runs only push diffs, reducing downstream processing load and providing a precise timeline of inventory changes.
Even DTC sites employ basic rate limiting and WAF protection. We route traffic through US-based residential proxies to maintain high concurrency without triggering IP blocks.
Competitor brands monitor base prices, bundle discounts, and seasonal sale depths to optimise their own pricing models.
Market analysts track catalogue expansion, new product launches, and category focus areas to understand brand strategy.
Product development teams analyse ply counts, material choices, and manufacturing origins to inform their own sourcing.
NLP models ingest the review corpus to identify common pain points (e.g., warping, staining) and positive feedback trends.
Analysts track out-of-stock durations and restock velocities to estimate inventory turnover and supply chain health.
Retailers and distributors aggregate high-resolution imagery and care instructions for internal reference systems.
"Made In Cookware represents the modern DTC kitchen segment — high-quality materials, dynamic bundle pricing, and rich customer sentiment locked behind complex frontends."
Extracting data from modern headless commerce architectures requires more than simple HTTP GET requests. We parse underlying API states, hydrate dynamic bundle pricing logic, and paginate through thousands of third-party review widgets to deliver normalised, structured output. You focus on the analysis; we handle the pipeline.
Everything supported by our madeincookware.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.
Playwright handles initial page loads to capture authentication tokens, while custom Python parsers target the underlying headless commerce APIs directly for cleaner data.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request to bypass WAF protections and rate limits inherent to modern DTC platforms.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About madeincookware.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review data is generally permissible. DataFlirt targets only public, non-authenticated endpoints. We do not extract personal user data or bypass login walls. Clients should consult their own legal counsel regarding specific use cases.
Made In Cookware relies heavily on dynamic sets. Our parsers extract the base price of each component SKU, the combined retail value, and the final bundle price, allowing you to calculate exact discount percentages.
Yes. We bypass the frontend display limits by paginating directly through the third-party review provider's API, capturing thousands of historical reviews including text, ratings, and dates.
We can configure pipelines to poll stock status at hourly intervals for specific high-priority SKUs, or run daily sweeps across the entire catalogue.
We extract the raw CDN URLs for all product imagery, including primary shots, lifestyle galleries, and instructional videos. We can deliver the URLs or download the assets directly to your S3 bucket.
Our minimum engagement covers a daily full-catalogue sync. Pricing scales based on delivery frequency and whether you require historical review extraction.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or continuous price-monitoring feeds — we scope, build, and operate the pipeline. Tell us what you need.
