Domain-Intelligence Layer
The domain-intelligence layer for AI.
Corthos turns raw source data into a clean, connected, domain-focused AI-ready intelligence foundation.
What is a domain-intelligence layer, and why does your AI need one?
AI is only as good as the data it reasons on. Most enterprise data is fragmented across systems, inconsistently named, missing context, and constantly drifting out of date. Pointing a model at it produces confident answers that no one can trust.
A domain-intelligence layer fixes that upstream. It turns raw source data into a connected, self-describing, insight-rich foundation — so the AI sitting on top can reason with confidence and explain itself.
Source, not scraped
LLMs learn from the diluted, second-hand text of the open internet. We expose the source data directly — entities, facts, and relationships — in a form both humans and AI models can reason over.
The 80% nobody wants to do
Ingest, clean, merge, model, document, and keep current. The data plumbing that consumes most of an AI roadmap. We do that work so you can build on top of it.
A friendly, self-describing data model
Human-readable names, deep metadata, and defined relationships. Readable by analysts, queryable by code, groundable for LLMs.
An insights layer, not just data
Historical series, trend analysis, cohort definitions, and per-entity ranking and scoring against each cohort. Context that turns facts into judgments.
Built for verticals
Starting with higher education, designed to extend to healthcare and other rule- and data-heavy domains.
The core wedge
Source data, not scraped text.
General-purpose LLMs work in one direction: they ingest billions of pages of text, extract patterns, and generate language back. The signal they reason on is whatever the open web happened to publish — diluted, unverified, and unstructured. We work the other direction. We start at the source, identify the patterns and relationships in the data itself, and expose them in a form AI models can use directly. Skip training on watered-down summaries of your domain. Feed your AI the underlying truth.
Generic LLMs
Text → patterns → intelligence
- 1 Web text
- 2 Pattern extraction
- 3 Generated answer
- • Reasons on second-hand summaries
- • Hallucinates when the source is thin
- • No lineage back to the underlying record
- • Stale the moment it's trained
Corthos
Source → patterns → content
- 1 Source data
- 2 Domain-intelligence layer
- 3 Grounded AI output
- • Reasons on the underlying records, not commentary
- • Every answer traces back to a source
- • Cohort context built in for ranking and scoring
- • Continuously maintained, never stale
What the layer does
The work we take off your roadmap so your team can focus on the product, not the plumbing.
| Capability | What it means |
|---|---|
| Ingestion | Bring in large public datasets — and proprietary sources when needed — across files, APIs, and feeds. Built for evolving formats, not one-time ETL. |
| Cleaning & standardization | Resolve inconsistent values, fill the obvious gaps, and standardize source data into a common, predictable shape that downstream code and AI can rely on. |
| Merging & reconciliation | Stitch many overlapping datasets into one coherent record per entity, keeping the lineage back to every source so nothing is invented. |
| Friendly data model | A self-describing schema with human-readable names and clear relationships — readable by analysts, queryable by code, groundable by LLMs. |
| Metadata & lineage | Every field carries definitions, source pointers, units, valid ranges, and update history. AI can cite where each fact came from. |
| Historical & trend analysis | Time series for every fact about every entity, with derived trends and projections — the past and the trajectory available out of the box. |
| Cohort definition | Configurable cohorts (peer groups, segments, classifications) so every entity can be compared against the right reference set, not the whole population. |
| Cohort ranking & scoring | For each numerical fact, where the entity stands within each of its cohorts. Context that turns a number into a judgment. |
| AI-ready surfaces | APIs, vector indexes, and grounded retrieval surfaces designed for LLM and agent use — with definitions and lineage exposed alongside the data. |
| Continuous maintenance | Keeping the data fresh, the schema honest, and the lineage intact. The work that's just as hard as building the layer the first time, owned by us. |
Built for domains, not the warehouse
We start where the data is dense, the rules are real, and the cohorts matter — then expand.
Education
LiveInstitutions, programs, outcomes, and the cohorts they belong to — modeled, ranked, and kept current. Higher ed today, with K-12 and continuing-ed on the roadmap.
Learn more →Employment
LiveEmployers, occupations, wages, and workforce trends — connected entities, regional and industry cohorts, projections, and source-grounded freshness.
Learn more →Geography
LiveEvery place as a first-class entity, with hierarchies, demographics, time series, and the cohort definitions that make comparisons honest.
Learn more →Healthcare
NextProvider, payer, plan, claim, and outcome data — same playbook, applied to a domain where rules, codes, and cohorts dominate. Engagements opening selectively.
Learn more →Other rule-heavy domains
On requestCompliance, insurance, public sector, manufacturing quality, and similar domains where context, lineage, and cohort comparison matter more than raw rows.
Learn more →Built on the Core
What you can do with the data layer.
AI Grounding
Feed your AI a foundation it can defend. Source-grounded data with definitions, lineage, and cohort context attached — so your model cites instead of hallucinates.
Learn more →Data Journalism
The same data layer applied to editorial. Rankings, comparisons, and longitudinal pieces grounded in source — with optional content tools if you also want help with the output.
Learn more →Four ways to plug into the data layer
Same data foundation. Different surfaces, different audiences.
API
AvailableFor engineering teams building custom apps and AI products
The api.corthodex.ai REST endpoints. Query entities by name, get source-grounded responses, and follow lineage back to every fact. Friendly schemas, definitions exposed.
Learn more →Export
On requestFor analysts, journalists, warehouse and notebook workflows
Bulk data dumps in standard formats — CSV, Parquet, JSON — for offline analysis, warehouse loads, and editorial work. Same entity model, same lineage, served as files.
Learn more →MCP
In private betaFor builders of AI agents — Claude, Cursor, Copilot, in-house
A Model Context Protocol server that exposes the layer as MCP tools. Your agent pulls grounded data at inference time, with definitions and source pointers attached to every response.
Learn more →Custom
On requestFor enterprise teams who want to fold their proprietary data in
Extend the curated layer with your own data. Your records get merged into the same entity model, ranked against the same cohorts, and served back through the same surfaces — a private, integrated foundation only your team sees.
Learn more →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.
Build your AI on a foundation that can defend its answers.
Get in touch to discuss how the Corthos domain-intelligence layer fits your data, your domain, and the AI products you're building.