We extract used machinery listings, technical specifications, dealer inventories, and market pricing from Surplus Record. 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 Machinery Listings objects from surplusrecord.com. All fields typed and schema-versioned.
"listing_id": "SR-94821", "category": "Machine Tools", "manufacturer": "Haas", "model": "VF-2", "year": 2018, "condition": "Used", "location": "Chicago, IL"
| # | listing_id | category | sub_category | manufacturer | model | year |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specs objects from surplusrecord.com. All fields typed and schema-versioned.
"listing_id": "SR-94821", "voltage": "220V", "phase": "3-Phase", "spindle_speed": "8100 RPM", "table_size": "36 x 14 in", "control_type": "Haas CNC", "axis_count": 3
| # | listing_id | voltage | phase | tonnage | spindle_speed | table_size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dealer Intelligence objects from surplusrecord.com. All fields typed and schema-versioned.
"dealer_id": "D-492", "dealer_name": "Midwest Machinery", "city": "Detroit", "state": "MI", "active_listings_count": 142, "membership_year": 1998
| # | dealer_id | dealer_name | contact_person | phone | website | |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Electrical Equipment objects from surplusrecord.com. All fields typed and schema-versioned.
"listing_id": "SR-11204", "equipment_type": "Transformer", "kva_rating": 1500, "primary_voltage": "13800V", "secondary_voltage": "480V", "frequency": "60Hz"
| # | listing_id | equipment_type | kva_rating | primary_voltage | secondary_voltage | enclosure_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search & Taxonomy objects from surplusrecord.com. All fields typed and schema-versioned.
"search_query": "cnc lathe", "category_path": "Machine Tools > Lathes > CNC", "result_count": 1245, "page_number": 1, "sponsored_listings": false, "scrape_timestamp": "2026-10-24T08:12:00Z"
| # | search_query | category_path | result_count | page_number | listing_ids | sponsored_listings |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Surplus Record scraper parses unstructured dealer descriptions into normalised technical specifications. We handle the pagination, taxonomy traversal, and anti-bot systems so you get clean warehouse-ready records.
Extract machine tools, electrical equipment, chemical processing gear, and packaging machinery across all top-level categories.
Parse unstructured dealer text to extract discrete fields for voltage, tonnage, spindle speeds, axis counts, and motor horsepower.
Track active listings, added assets, and removed inventory for specific dealers or regions to monitor market liquidity.
Monitor time-on-market for specific asset classes by tracking listing creation dates and removal timestamps.
Traverse the entire Surplus Record taxonomy, maintaining parent-child category relationships for every extracted asset.
Specific parsing logic for transformers and generators, capturing kVA ratings, primary/secondary voltages, and phase data.
Extract high-resolution image URLs for visual inspection and machine learning training pipelines.
Run continuous pipelines with hash-based diffing to only emit new listings or assets with updated descriptions.
Standardise city, state, and country data to calculate logistics costs and evaluate regional asset availability.
Handle temporary site timeouts, rate limits, and connection resets automatically without dropping records.
Brief in. Clean data out.
Provide category URLs, manufacturer lists, or dealer IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for surplusrecord.com.
Schema validation, null-rate checks, price-outlier detection, and sample normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Industrial directories rely on dealer-submitted text, leading to massive data inconsistency. Here is how we standardise it.
Directory sites implement rate limiting and basic bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain high throughput without triggering blocks.
Dealers format listings inconsistently. We apply custom parsing rulesets to extract standard metrics — converting varying formats of RPM, voltage, and tonnage into clean, typed numerical fields in your final dataset.
Surplus Record contains hundreds of thousands of listings nested deep within subcategories. Our crawlers handle infinite scroll and deep pagination loops, ensuring total catalogue coverage without missing buried assets.
For daily monitoring, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs — reducing compute cost and downstream processing load for your data engineering team.
We monitor extraction yields for key fields like manufacturer, year, and model. If a site layout change causes a drop in data capture, our observability stack alerts our engineers before you receive incomplete data.
Equipment appraisers and financial institutions track historical listing data to build depreciation models and establish fair market value for industrial assets.
Machinery dealers monitor competitor inventory, time-on-market metrics, and regional availability to optimise their own acquisition and pricing strategies.
Manufacturing procurement teams scan the secondary market for specific CNC machines or electrical equipment to bypass long OEM lead times.
Lenders analyse secondary market liquidity for specific asset classes to assess collateral risk before issuing equipment financing loans.
Private equity firms evaluate the health of specific manufacturing sectors by tracking the volume of liquidated assets hitting the secondary market.
Service providers identify aging equipment clusters by region to target maintenance, retrofit, and repair services to specific facilities.
"Surplus Record holds the pulse of the secondary industrial market, but the data is locked in decades of inconsistent dealer text formats."
Extracting machinery data requires more than a simple HTTP client. We deploy custom parsing logic to normalise voltage, tonnage, and spindle speeds from unstructured descriptions, bypass bot protection, and deliver structured asset intelligence directly to your warehouse.
Everything supported by our surplusrecord.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, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 surplusrecord.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated machinery listings and dealer profiles. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review the target site's ToS and consult legal counsel for specific use cases.
We use custom Python parsing rulesets and regex patterns tailored to specific asset classes. This allows us to extract discrete numerical values (like 480V, 500 Ton, or 10000 RPM) from unstructured paragraph text submitted by dealers.
Yes. We can scope the pipeline to monitor specific dealer profile pages, extracting their entire active inventory and tracking newly added or removed assets on a daily or weekly basis.
Pipelines can be configured for daily, weekly, or monthly cadences depending on your requirements. Change-detection diffing ensures you only process updated records.
We extract all pricing data visible on the listing. However, many dealers list assets as 'Price on Request' or require a direct inquiry. We cannot extract hidden prices that require manual dealer communication.
Our minimum engagement typically starts with a defined category subset or a specific list of target dealers. Contact us with your exact data requirements for a scoped technical proposal.
Absolutely. We provide a sample run of up to 500 listings as part of the pre-engagement scoping process so you can validate our specification normalisation logic and schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of CNC machinery or continuous monitoring of dealer inventories — we scope, build, and operate the pipeline. Tell us what you need.
