We extract commercial kitchen equipment specs, pricing signals, stock availability, and manufacturer details from KaTom. 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 katom.com. All fields typed and schema-versioned.
"sku": "119-2345", "item_number": "2345", "manufacturer_model": "T-49-HC", "title": "True T-49-HC 54 inch Solid Door Reach-In Refrigerator", "brand": "True Refrigeration", "price": 4215.0, "stock_status": "In Stock", "rating": 4.8
| # | sku | item_number | manufacturer_model | title | brand | category_path |
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Complete list of extractable fields for Technical Specs objects from katom.com. All fields typed and schema-versioned.
"sku": "119-2345", "width_inches": 54.13, "depth_inches": 29.5, "height_inches": 78.38, "voltage": "115", "phase": "1", "nsf_certified": true, "energy_star": true
| # | sku | width_inches | depth_inches | height_inches | weight_lbs | voltage |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Stock objects from katom.com. All fields typed and schema-versioned.
"sku": "119-2345", "base_price": 4215.0, "map_price": 4215.0, "discount_percentage": 0, "availability_message": "Ships in 1-2 business days", "lead_time_days": 2, "free_shipping_eligible": true, "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | base_price | map_price | discount_percentage | bulk_pricing_tiers | availability_message |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Categories objects from katom.com. All fields typed and schema-versioned.
"category_id": "cat_892", "name": "Reach-In Refrigerators", "parent_category": "Commercial Refrigeration", "breadcrumb_path": "Home > Commercial Refrigeration > Reach-In Refrigerators", "product_count": 1245, "url": "https://www.katom.com/cat/reach-in-refrigerators.html", "thumbnail_url": "https://www.katom.com/images/cat_892.jpg"
| # | category_id | name | parent_category | breadcrumb_path | product_count | url |
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Complete list of extractable fields for Reviews & Ratings objects from katom.com. All fields typed and schema-versioned.
"review_id": "rev_99281", "sku": "119-2345", "author_name": "Chef Marco", "review_date": "2025-11-04", "star_rating": 5, "review_title": "Reliable workhorse", "verified_buyer": true, "helpful_votes": 12
| # | review_id | sku | author_name | review_date | star_rating | review_title |
|---|---|---|---|---|---|---|
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Our KaTom scraper handles commercial equipment catalogues, dynamic B2B pricing, spec sheet extraction, and freight variables - with JavaScript rendering and anti-bot circumvention built in.
Title, model numbers, descriptions, dimensions, weight, and images scraped at the SKU level with parent-child variant mapping.
Capture base price, MAP pricing, bulk discounts, and freight costs - timestamped per crawl.
Extract voltage, phase, horsepower, BTU, and dimensions into structured columns.
Identify NSF, UL, Energy Star, and ADA compliance badges for every product.
Monitor stock status, factory lead times, shipping origins, and freight class classifications.
Extract URLs for spec sheets, user manuals, warranty PDFs, and CAD drawings.
Full review text, star ratings, helpful vote counts, and verified buyer flags across all product pages.
Map OEM replacement parts and accessories to their parent equipment models.
Run one-off bulk exports or configure continuous pipelines with change-detection diffing for price and stock updates.
Brief in. Clean data out.
Provide SKU lists, category URLs, or brand targets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for katom.com.
Schema validation, null-rate checks, price-outlier detection, and sample outputs before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
KaTom utilizes modern eCommerce bot protection and complex product variations. Here is how we stay resilient.
eCommerce bot detection operates on TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
KaTom product pages use JavaScript to load real-time stock and dynamic freight calculations. We run full Playwright browser sessions to capture data that headless HTTP clients miss entirely.
eCommerce sites change DOM structures frequently. Our selector strategy uses multiple fallback chains per field - CSS selectors, XPath, and structured data extraction - ensuring pipeline stability.
For large equipment catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops - and respond before you notice.
Restaurant supply dealers monitor KaTom pricing, MAP compliance, and freight policies to adjust their own pricing algorithms.
B2B distributors extract technical specs, dimensions, and NSF certifications to enrich their own product information management (PIM) systems.
Manufacturers track their own product representation, review sentiment, and share of search against competitors in the KaTom catalogue.
Procurement teams monitor stock availability and factory lead times across thousands of SKUs to anticipate equipment shortages.
ML teams use structured commercial kitchen equipment datasets to train recommendation engines and procurement chatbots.
Service companies extract OEM part relationships to build compatibility databases for equipment repair routing.
"KaTom holds the definitive catalogue for commercial kitchen equipment specs and B2B pricing - but extracting it requires handling complex variations and freight data at scale."
Most teams underestimate the investment required: reliable KaTom scraping requires residential proxies, full JavaScript rendering for dynamic pricing, and normalising unstructured technical specifications. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our katom.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, deduplication, and retry logic. Playwright handles JavaScript rendering and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. 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 katom.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from KaTom is generally permissible under applicable law in the US. DataFlirt targets only public, non-authenticated product, pricing, and specification data. We do not extract personal data or circumvent authentication walls. Clients should review KaTom's terms and consult legal counsel for specific use cases.
We use full Playwright browser sessions to execute JavaScript, allowing the page to load dynamic pricing elements and availability statuses just as a real user would see them.
Our standard pipeline extracts the URLs to the PDF spec sheets and manuals. If you require text extraction from within the PDFs, we can configure a custom processing step using OCR and document parsing libraries.
We can configure pipelines to run at your required cadence. Full catalogue refreshes typically run daily or weekly, while specific high-priority SKU lists can be monitored hourly for price changes.
Yes. We extract specifications from HTML tables and normalise keys like 'Width', 'Depth', 'Voltage', and 'Phase' into consistent JSON fields, regardless of how individual manufacturers format their descriptions.
Our smallest packages start at a defined SKU list (typically 5,000-20,000 SKUs) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 SKUs or 20 category pages as part of the pre-engagement scoping process, allowing you to validate schema fit and field completeness before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off equipment catalogue dump or a continuous price-monitoring feed across 300,000 SKUs - we scope, build, and operate the pipeline. Tell us what you need.
