Education · Live
The domain-intelligence layer for education.
We've already done the work most teams dread: pulling together fragmented public data on institutions, programs, and outcomes; reconciling it into a clean entity model; layering on history, cohorts, and rankings; and keeping it current. Build your AI products on top of that — instead of standing up the data factory yourself.
Today our depth is in higher education, with K-12 and continuing-ed segments on the roadmap.
Why education, starting with higher ed
Higher education is dense with the kind of data that breaks naive AI: thousands of institutions across many sectors, hundreds of thousands of programs, decades of outcome data, and a web of classifications that determine which numbers are even comparable. Most of that data is technically public, and almost none of it is usable without serious work.
That's the wedge. We take the raw, scattered sources and produce a single, opinionated, AI-ready foundation: every institution as a real entity, every fact carrying its definition and lineage, every numerical metric ranked against the cohorts that matter, all of it kept current. What used to be a multi-quarter data project becomes a few API calls.
Build a counselor-facing assistant, an analytics product for institutional research, an outcomes-comparison tool for parents, a research workbench, or an internal AI agent that has to answer hard questions about a real institution — and have it ground every answer in the underlying record.
What's in the education layer
The Corthos capabilities, applied to education — with depth in higher ed today.
| Capability | What it means |
|---|---|
| Institution graph | Every institution as a first-class entity, with its programs, locations, classifications, affiliations, and the relationships between them resolved across sources. |
| Program & outcome data | Programs, degrees, completions, costs, and student outcomes connected to the institutions and cohorts they belong to — not stranded in separate exports. |
| Time series, not snapshots | Multi-year history for every institution and program. Trends, year-over-year change, and projections available alongside the underlying records. |
| Configurable cohorts | Carnegie classifications, sector, size, region, selectivity, mission — combine cohort definitions to compare each institution against the right peer group, not the whole field. |
| Cohort ranking & scoring | For every numerical fact, where the institution stands in each of its cohorts. Percentiles, peer comparisons, and outliers exposed as data, not as charts. |
| AI-ready surfaces | Friendly names, defined relationships, and lineage exposed through APIs and grounded retrieval — so an LLM can answer institution-level questions with citations. |
| Continuously maintained | Sources change, schemas drift, definitions evolve. We keep the layer current so your team never inherits a stale extract. |
What you can build on top
Counselor & student-facing AI
Assistants that answer institution-specific questions with citations, compare programs apples-to-apples, and surface fit based on real cohort data.
Institutional research & benchmarking
Peer comparison, trend analysis, and cohort scoring without the spreadsheet ritual. The reference data is already loaded and always current.
Outcomes & ROI products
Cost, completion, earnings, and trajectory data wired together at the institution and program level — explainable enough to put in front of a parent.
Research workbenches & agents
Grounded retrieval over a real entity model, with metadata and lineage exposed alongside the data so your agent can 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.