AI Grounding · Live
The grounded data foundation for AI.
An AI without grounded data guesses. An AI with the Corthos layer cites. Same model, different floor — feed it entities, definitions, and source-linked facts and watch hallucination shrink to a footnote.
Why grounding wins
General-purpose LLMs reason on whatever the open web happened to say. That's enough for chat, but not for an AI product that has to be right about a real institution, a real employer, or a real place. The model doesn't know where the number came from, whether it's current, or what it should be compared to. So it makes up plausible answers — and your team becomes the verification layer.
Grounded AI flips that. The model isn't guessing because it can't — every claim has a source pointer, every comparison has a defined cohort, every metric has a definition the model can read. Trust isn't a feeling, it's a citation.
What grounding looks like in the layer
The AI-facing surface of the Corthos data foundation.
| Capability | What it means |
|---|---|
| Entities, not strings | Every record resolved to a real entity with a stable identity, friendly name, and defined relationships. Your model reasons about institutions, employers, and places — not about lookups it could fail. |
| Source lineage on every fact | Each value carries pointers to its source, definition, and last refresh. Your AI can answer "where does this number come from?" honestly, every time. |
| Retrieval surfaces designed for AI | API and MCP endpoints shaped for grounded retrieval — definitions exposed alongside data, structured so an LLM can pick the right slice without scanning a warehouse. |
| Cohort context built in | Comparisons require the right peer set. Cohort definitions and per-entity rankings come with the data so your model can answer "compared to whom?" without reinventing the math. |
| Time series, not snapshots | History is part of the layer. Your AI can reason about change, trend, and trajectory — and trace any answer back to a specific period. |
| Continuous freshness | The layer updates as sources do. The data your AI reasons on next month is the data we maintain, not the extract someone shipped at launch. |
What you can build on top
Vertical AI assistants
Counselor, advisor, and analyst-facing assistants that answer real questions about real entities — institutions, employers, places — and cite the source on demand.
Agents with reliable retrieval
Agents (via API or MCP) that pull grounded data at inference time, follow relationships, and walk hierarchies — instead of synthesizing answers from training data.
Internal AI tools
Internal copilots for research, analytics, and reporting that operate on a defendable foundation. Your team stops chasing down "where did this number come from?"
Decision-support products
Products that present AI-generated recommendations with the data and lineage attached — explainable enough to put in front of a user, an auditor, or a regulator.
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.
Stop your AI from guessing.
Get in touch to discuss how the Corthos domain-intelligence layer fits your data, your domain, and the AI products you're building.