Employment · Live
The domain-intelligence layer for employment.
Employers, occupations, wages, and workforce trends — pulled together from fragmented public sources, reconciled into a clean entity model, layered with cohorts and projections, and kept current. Your AI products and analyses get to start from a foundation, not from a data project.
Why employment is a great fit
Employment data is dense, structured, and constantly moving. Hundreds of thousands of employers, thousands of standardized occupations, decades of wage and workforce history, and a tangle of classifications that determine which numbers are even comparable. Most of it is technically public, and almost none of it is usable without serious work.
We do that work. Employers as real entities, occupations resolved to a clean taxonomy, wages and projections wired to the right cohorts, every fact carrying its source. What used to be a multi-quarter ingestion-and-mapping project becomes a few API calls — with citations.
What's in the employment layer
The Corthos capabilities, applied to employment data.
| Capability | What it means |
|---|---|
| Employer graph | Companies and organizations as first-class entities, with industry classifications, locations, hierarchies, and the relationships between them resolved across sources. |
| Occupation taxonomy | Standardized occupations and their relationships to industries, skills, education paths, and career ladders — queryable by name, not by code. |
| Wage & earnings time series | Multi-year wage data by occupation, industry, and geography. Trends, distributions, and projections available alongside the underlying records. |
| Regional & industry cohorts | Cohort definitions that combine geography, industry, employer size, and occupation so each entity is compared against the right peer set. |
| Workforce projections | Forward-looking estimates for occupation demand, employment growth, and labor-market shifts — kept aligned with each refresh of the underlying data. |
| AI-ready surfaces | Friendly names, defined relationships, and lineage exposed through APIs and grounded retrieval — so an LLM can answer wage and occupation questions with citations. |
| Continuously maintained | Wage releases, classification updates, and source revisions land in the layer continuously. Your team never inherits a stale extract. |
What you can build on top
Career & occupation-fit AI
Assistants that answer wage, demand, and career-ladder questions with citations and route candidates to occupations that fit their skills, location, and goals.
Workforce planning & talent intelligence
Peer comparisons across employers, regions, and industries — supply-and-demand signals, wage benchmarks, and cohort scoring without the spreadsheet ritual.
Labor-market reporting & content
Trend pieces, rankings, and regional dashboards powered by source-grounded data — every chart traceable back to the underlying release.
Recruiting & HR agents
Grounded retrieval over a real employer + occupation entity model so your agent can reason about pay bands, demand, and equivalent roles — and cite, not guess.
Enterprise Engagement Model
Our proven three-phase approach to enterprise transformation
- 1
Assessment
We evaluate current data assets, flows, and decision points.
- 2
Blueprint
We design your Corthos OS integration map, guarantees, and SLAs.
- 3
Build
We implement, validate, and operationalize the enterprise rollout.
Skip the data project. Start with the layer.
Get in touch to discuss how the Corthos domain-intelligence layer fits your data, your domain, and the AI products you're building.