We extract truck listings, heavy machinery specifications, pricing signals, and seller intelligence from autoline.info. 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 Commercial Vehicles objects from autoline.info. All fields typed and schema-versioned.
"listing_id": "V2394817", "make": "Volvo", "model": "FH 500", "category": "Tractor unit", "registration_year": 2019, "mileage_km": 420500, "price": 54900.0, "currency": "EUR", "axle_configuration": "4x2", "emission_class": "Euro 6"
| # | listing_id | make | model | category | registration_year | mileage_km |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Construction Machinery objects from autoline.info. All fields typed and schema-versioned.
"listing_id": "M9928341", "category": "Excavators", "sub_category": "Crawler excavators", "make": "Caterpillar", "model": "320", "operating_weight_kg": 22500, "operating_hours": 4150, "price": 89000.0, "currency": "EUR", "condition": "Used"
| # | listing_id | category | sub_category | make | model | operating_weight_kg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from autoline.info. All fields typed and schema-versioned.
"listing_id": "V2394817", "price": 54900.0, "price_net": 45750.0, "currency": "EUR", "vat_deductible": true, "vat_rate": 20.0, "leasing_available": true, "price_timestamp": "2026-05-12T09:14:00Z", "availability_status": "In stock"
| # | listing_id | price | price_net | currency | vat_deductible | vat_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Data objects from autoline.info. All fields typed and schema-versioned.
"seller_id": "D48291", "seller_name": "EuroTrucks GmbH", "seller_type": "Dealer", "country": "Germany", "city": "Hamburg", "languages_spoken": "['German', 'English', 'Polish']", "active_listings_count": 142, "member_since": "2015-03-12", "rating": 4.8
| # | seller_id | seller_name | seller_type | country | city | address |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search Results objects from autoline.info. All fields typed and schema-versioned.
"keyword": "Volvo FH", "category": "Tractor units", "position": 1, "listing_id": "V2394817", "promoted": true, "price": 54900.0, "currency": "EUR", "registration_year": 2019, "location": "Germany", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | category | position | listing_id | title | price |
|---|---|---|---|---|---|---|
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| 3 |
Our Autolinefo scraper handles every layer of the marketplace: commercial truck listings, construction machinery specifications, dynamic pricing, and seller intelligence.
Extract make, model, axle configuration, gross weight, and Euro emission class for trucks and tractors.
Capture operating weight, engine power, hours used, and attachment details for excavators and loaders.
Capture listing price, VAT status, net price, and currency conversions timestamped per crawl.
Extract dealer name, location, active listing count, languages spoken, and rating metrics.
Track odometer readings, operating hours, and registration years to feed depreciation models.
Parse unstructured technical descriptions into normalised key-value pairs for easy querying.
Scrape autoline.info across European, Asian, and American subdomains with localised data.
Identify sponsored placements and premium dealer listings within category search results.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide category URLs, keyword sets, or seller IDs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for autoline.info.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Autoline.info employs strict rate limiting and bot detection. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management trained on real user behaviour patterns.
We run full Playwright browser sessions with JavaScript execution, lazy-load triggering, and dynamic widget hydration, capturing data that headless HTTP clients miss entirely.
Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and text-pattern matching, so a layout change does not break your data pipeline.
For large vehicle catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and storage bloat.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops, responding before you notice.
Dealerships monitor market pricing for used commercial vehicles to optimise trade-in offers and inventory pricing.
Logistics companies track specific truck configurations and emission standards to source fleet expansions.
Analysts track inventory levels across European markets to identify supply constraints and demand shifts.
ML teams use historical pricing and mileage data to train residual value prediction models for heavy machinery.
Commercial vehicle OEMs track secondary market volume for their own and competitor brands to gauge market saturation.
Financial institutions verify asset values and market liquidity for heavy machinery underwriting and risk assessment.
"Autoline.info holds the most comprehensive dataset for European commercial vehicles and heavy machinery, but extracting it requires a dedicated pipeline."
Most teams underestimate the investment required: reliable Autolinefo scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our autoline.info 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.
We maintain pools of residential ISP proxies across EU regions. Rotation happens per-request with sticky sessions where required.
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 autoline.info scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated vehicle and machinery data. We do not extract personal data, circumvent authentication walls, or violate GDPR.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
We support all primary categories including commercial vehicles, construction machinery, agricultural machinery, municipal vehicles, buses, and spare parts.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined target set. Full category refreshes at daily cadence complete within a 4-8 hour window.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per listing for price, operating hours, and availability from the date your pipeline starts.
Our smallest packages start at a defined category list with weekly delivery. For larger datasets or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 listings as part of the pre-engagement scoping process, so you can validate schema fit and data quality before signing any 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 price-monitoring feed across 100K vehicles, we scope, build, and operate the pipeline. Tell us what you need.
