We extract used machinery listings, technical specifications, seller locations, and market pricing from Exapro. 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 exapro.com. All fields typed and schema-versioned.
"listing_id": "EX93821", "title": "Trumpf TruLaser 3030 Fiber Laser cutting machine", "manufacturer": "Trumpf", "model": "TruLaser 3030 Fiber", "year_of_manufacture": 2016, "location_country": "Germany", "condition": "Good", "usage_hours": 14500
| # | listing_id | title | manufacturer | model | year_of_manufacture | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Technical Specs objects from exapro.com. All fields typed and schema-versioned.
"listing_id": "EX93821", "spec_name": "Laser power", "spec_value": "3000", "unit": "W", "weight_kg": 8500, "power_kw": 3.0, "control_unit": "Siemens Sinumerik 840D"
| # | listing_id | spec_name | spec_value | unit | dimensions_length | dimensions_width |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Seller Data objects from exapro.com. All fields typed and schema-versioned.
"seller_id": "DL4921", "seller_type": "Dealer", "location_country": "Italy", "location_city": "Milan", "languages_spoken": "['Italian', 'English', 'German']", "active_listings_count": 42, "member_since": "2018-04-12"
| # | seller_id | seller_name | seller_type | location_country | location_city | languages_spoken |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Market objects from exapro.com. All fields typed and schema-versioned.
"listing_id": "EX93821", "list_price": 185000, "currency": "EUR", "price_on_request": false, "shipping_terms": "EXW", "incoterms": "Ex Works", "tax_status": "Excluding VAT"
| # | listing_id | list_price | currency | price_on_request | estimated_value | shipping_terms |
|---|---|---|---|---|---|---|
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| 2 | ||||||
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Complete list of extractable fields for Taxonomy & Search objects from exapro.com. All fields typed and schema-versioned.
"category_id": "C104", "category_name": "Laser cutting machines", "parent_category": "Sheet metal working", "breadcrumb_path": "Machine tools > Sheet metal working > Laser cutting machines", "result_count": 842, "language_locale": "en", "search_position": 4
| # | category_id | category_name | parent_category | breadcrumb_path | result_count | url |
|---|---|---|---|---|---|---|
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Our Exapro scraper standardises fragmented machinery specifications across hundreds of categories, handling multi-language listings and complex technical tables with precision.
Title, manufacturer, model, year, description, and images scraped at the individual listing level.
Parse highly variable specification tables (dimensions, weight, power, capacity) into structured key-value pairs.
Extract listings across Exapro's multiple language subdomains, maintaining consistent taxonomy and IDs.
Capture equipment location, seller region, and incoterms to support supply chain and transport cost modelling.
Extract operational hours, production cycles, and stated condition grades to evaluate asset depreciation.
Reconstruct the full category tree, from top-level industrial sectors down to specific machine sub-types.
Capture listed prices, currency conversions, and 'Price on Request' flags for market valuation models.
Track when listings are removed or flagged as sold to estimate market liquidity and turnover rates.
Run continuous pipelines at daily or weekly cadences to maintain an accurate view of the secondary market.
Brief in. Clean data out.
Provide target categories, manufacturers, or machine types. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and spec-table parsers tailored to Exapro's DOM structure.
Schema validation, unit normalisation checks, and missing-field detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting B2B marketplace data introduces unique normalisation challenges. Here is how we ensure data quality.
A CNC lathe has entirely different specifications than a packaging machine. Exapro listings use varied table structures. We deploy category-specific parsers that map unstructured HTML tables into a normalised JSON schema, ensuring consistent units (e.g., standardising kW vs HP).
Exapro operates across multiple languages, often with the same machine listed in different locales. We extract the core listing ID and map fields to a primary English taxonomy, preventing duplicate records while retaining original text for NLP tasks.
B2B marketplaces deploy rate-limiting and Cloudflare challenges to deter bulk scraping. Our crawlers use residential ISP proxies with realistic browser fingerprints and request pacing to maintain uninterrupted extraction without triggering blocks.
Used machinery inventory is highly transient. We maintain a state index of all known listings. Subsequent runs flag items that have disappeared or changed price, allowing you to model market absorption rates and actual transaction velocity.
Every run emits structured logs to our observability stack. We alert on schema drift, category tree changes, and null-rate spikes in critical fields like price or year of manufacture. SLA uptime is contractual.
Appraisers and financial institutions use historical pricing and depreciation curves to value collateral accurately.
OEMs track the secondary market volume of their own machinery to forecast replacement cycles and parts demand.
Used equipment dealers monitor competitor inventory, time-on-market, and pricing strategies to optimise their own stock.
Data science teams correlate usage hours, age, and condition grades to build lifecycle and failure-rate models.
Manufacturers automate the sourcing of specific machine types globally, triggering alerts when matching equipment is listed.
Underwriters assess asset liquidity and residual value risk by analysing secondary market depth for specific equipment categories.
"Exapro holds the global pulse on secondary market machinery, but extracting uniform technical specifications across hundreds of disparate equipment categories requires serious normalisation."
Most teams underestimate the investment required: reliable Exapro scraping involves parsing highly variable specification tables, normalising multi-language listings, and detecting transient inventory states. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our exapro.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 across EU regions. 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 exapro.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Exapro is generally permissible for business intelligence purposes. DataFlirt targets only public, non-authenticated machinery listings and technical specifications. We do not extract personal data or circumvent authentication walls. Clients should review Exapro's ToS and consult legal counsel for specific use cases.
We deploy category-specific parsing rules. Our pipeline identifies the machine category and maps the unstructured HTML spec table to a predefined schema for that category, ensuring fields like 'spindle speed' or 'clamping force' are captured correctly with their respective units.
Yes. Exapro operates across multiple language locales. Our pipeline can target specific locales or aggregate listings globally, using the unique listing ID to deduplicate records while capturing the localised description and metadata.
We track listing presence and status flags over time. If a listing is removed or explicitly marked as sold, our change-detection diffing will emit a status update for that listing ID in the subsequent run.
We configure pipeline cadences based on your requirements. Standard deployments run daily or weekly full-catalogue refreshes. We can also configure higher-frequency polling for specific high-value categories.
Our minimum engagement typically starts with a defined set of categories or manufacturers with weekly delivery. For full-site extraction across all industrial sectors, we price based on compute volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 listings across your target categories as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and normalisation quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off machinery catalogue dump or a continuous inventory feed across 100K listings — we scope, build, and operate the pipeline. Tell us what you need.
