SSample Fabrication18m ago
CNC machinistCleveland, USSOURCE-LINKED BLUE-COLLAR JOB DATA · SCOPED API + BULK EVALUATION
The blue-collar job market,ready to query and export.
Query and export source-linked blue-collar vacancies from small employers that broad, ATS-led datasets often miss.
BUILT FOR Staffing agencies Job boards + AI aggregators Workforce analysts
Build recruitment lead lists from current hiring signals. Add hard-to-find skilled-trade jobs to job boards and AI products. Measure demand by market, occupation and employer. Start with the records you need—without an enterprise-only contract.
FITS YOUR STACK
Scoped API evaluation Apify planned n8n via HTTP Zapier via webhook
PRODUCT STORYFrom public pages to usable job data
PUBLIC EMPLOYER SITESFRAGMENTED
NNorthline Energy27m ago
Industrial electricianCalgary, CAHHarbor Cooling41m ago
HVAC technicianTampa, USTTransit Works1h ago
Diesel mechanicBrisbane, AUAAlloy Systems2h ago
Production welderBirmingham, UKThousands of listings. Different layouts. No shared schema.
Hard-to-find public job pages
CSV, JSON and API-shaped records
Source and observation context
Standard HTTP and structured data
PLANNED DELIVERY PATHS
Job data that lands where your team already works.
Standard HTTP and structured JSON make the dataset suitable for developer tools, automation platforms and your own data stack. Native templates are a planned product layer.
Tell us what you useApify
PLANNEDActor or dataset adapter
n8n
WORKFLOWHTTP Request workflow
Zapier
WORKFLOWWebhooks or custom request
Scoped API
EARLY ACCESSREST + JSON evaluation
BLUE COLLAR JOB DATAHTTPS / JSON
APIFYN8NZAPIERYOUR APP
Early access is scoped directly with buyers. Native templates and vendor partnerships are not yet available.
01 / 11THE DATA GAP
HIDDEN IN PLAIN SIGHT
One job page shows an opening.Connected records show demand.
Collection is only the beginning. Useful job postings data needs a shared structure, repeated observation and clear provenance.
01TITLE ≠ ROLE
Every source speaks differently.
Titles, places, work types and dates arrive in thousands of different shapes.
02LAST SEEN —
Listings quietly disappear.
Repeated observation is what reveals whether a listing opened, changed or left the source.
03SOURCE / MANY
Employers stay fragmented.
Public hiring demand is scattered across individual career sites and local vacancy pages.
04RECORD → SIGNAL
The market remains invisible.
One job page shows an opening. Connected records show the shape of demand.
02 / 11THE VISIBILITY LAYER
ONE RECORD / FIVE STATES
The web does not share a schema.The dataset does.
Follow one vacancy from an isolated source page into a traceable record that can join the wider market view. Select a stage to see what changes and what is preserved.
SCHEMA / V1STEP 03 / 05
CURRENT RECORD
Electrician
OCCUPATIONElectrical trades
CHANGEDCITYRotterdam
NORMALIZEDWORK TYPEFull-time
NORMALIZEDPROVENANCE RETAINED
03 / 11DELIVERY MODES
ONE LAYER / THREE PRODUCTS
One dataset. Three ways to work with it.
Use the same normalized layer through a job posting API, as bulk job data or as evidence for labor-market reporting.
01
QUERY
Job data API
Discuss a scoped evaluation using market, category, employer, place and observation fields.
Inspect sample response02
ANALYZE
Bulk datasets
Bring analysis-ready job records into a warehouse, model, research project or data product.
See the sample package03
EXPLAIN
Market reports
Turn the same records into evidence-led views of places, occupations and employers.
Preview report formats04 / 11DATASET ANATOMY
FIELDS WITH A MEMORY
A record should explain where it came from.
Structured job data is more useful when preserved source values, normalized fields and observation states remain visibly distinct.
idintegerGeneratedStable public record identifier
titlestringPreservedTitle retained from the source listing
organizationstringPreservedEmployer attached to the original source
urlURITraceableCanonical public job page
date_createddate-timeObservedFirst successful source observation
locations_derivedstring[]NormalizedComparable job-location labels
employment_typestring[]PreservedEmployment values supplied by the source
ai_employment_typeenum[]ClassifiedNormalized work-type values when supported
COVERAGE TRUTH LAYERSTATUS / PUBLIC PLAN
Current launch market In development Long-term vision
05 / 11COVERAGE + FRESHNESS
THE EDGES ARE PART OF THE PRODUCT
See what is measured—and what is not.
Trust begins with scope. Every commercial snapshot should state the market, source definition, observation date, freshness rules and known exclusions.
Review current coverageNetherlands
CURRENT21,533 open-status records
United States
IN DEVELOPMENT1,030-record pilot snapshot
Global layer
PLANNEDLong-term platform vision
06 / 11FROM RECORD TO DECISION
BUYER OUTCOMES
One dataset. Different questions.
The same record layer can support workforce analytics, recruitment products, hiring signals and original market research.
Workforce analytics
Where is skilled demand changing?
Recruitment products
Which current records should power a search, alert or workflow?
Hiring signals
Which employers changed their hiring activity?
Market research
How do occupations, places and employers compare?
07 / 11MARKET REPORTS
DATA AS PUBLIC EVIDENCE
The same layer can explain a labor market.
Original reports will turn verified job market data into readable city and occupation stories. United States reports publish only after their coverage supports meaningful, unique analysis.
REPORT FORMAT / 001PUBLICATION STANDARD
FUTURE UNITED STATES SERIES
New York job market
Publish after verified source coverageHouston job market
US / PLANNEDPublishes after verified coverage
Seattle job market
US / PLANNEDPublishes after verified coverage
Chicago job market
US / PLANNEDPublishes after verified coverage
No thin city pages: each report needs a defined snapshot, unique findings, methodology and limitations.
Review the publication standard08 / 11METHODOLOGY
A VISIBLE PROVENANCE LEDGER
The method travels with the record.
A credible dataset explains collection, transformation and limitations alongside the output—not in a footnote after the sale.
Read the methodology01Source
Surface hard-to-find public vacancies with the proprietary web index.
02Observe
Retain the source route and observation times needed to describe provenance and lifecycle.
03Normalize
Turn inconsistent titles, locations and attributes into comparable fields while preserving source values.
04Track
Use repeated observations to distinguish first seen, last seen, changed and no longer observed states.
05Disclose
Publish market scope, snapshot date, definitions and known limitations alongside the data.
09 / 11SAMPLE PACKAGE
START WITH THE RECORDS
Know what you are evaluating.
Request representative records with a field dictionary, source example and dated coverage note. We'll scope the sample to the market and workflow you're evaluating.
Inspect sample dataNormalized records
Representative sample records
Field dictionary
Definitions + lineage
Provenance example
Source + observation
Coverage note
Scope + limitations
10 / 11FAQ
DATA BUYER QUESTIONS
What to know before you evaluate the data.
Evaluate the records against clear coverage, field and delivery definitions before you buy.
01What is blue-collar job data?
Structured records describing skilled-trade, frontline, field-service, logistics, manufacturing and related vacancies. The product turns many fragmented sources into a consistent format for analysis and software products.
02Where do the job listings come from?
A proprietary web-indexing system surfaces hard-to-find public blue-collar vacancies. Published samples retain source and observation context.
03Is this a job posting API or a tool for publishing jobs?
It supplies job-posting data for reading and analysis. It is not an API for sending vacancies to job boards. Public self-service access is still being prepared; scoped API evaluations and bulk datasets can be discussed now.
04Which markets are covered?
The dated snapshot contains 21,533 Netherlands records and a separately scoped 1,030-record United States pilot. Public coverage statements include the observation date and scope definition.
05Can I inspect the fields before buying?
Yes. The evaluation package is designed to include normalized sample records, a field dictionary, a provenance example and a current coverage note.
06How are duplicate and inactive records handled?
The methodology retains source identity and repeated observations. Exact deduplication and lifecycle definitions are documented with each commercial dataset so the rules match the market and delivery.
11 / 11NEXT STEP
EVALUATE THE LAYER
Start with the records, not the sales pitch.
Tell us the market, occupations and workflow you want to evaluate. We will use that context to shape the sample package and integration path.
SAMPLE PACKAGE / SCOPED TO YOU● REQUEST OPEN
- MARKET
- Netherlands + US pilot
- CONTENT
- Records + fields + provenance
- DELIVERY
- API or bulk evaluation
- STATUS
- Market + use case required