Geography · Live
The domain-intelligence layer for geography.
Every place as a first-class entity, with hierarchies, demographics, time series, and the cohorts that make comparisons honest. The geographic foundation underneath your AI products, content, and analyses — already built and kept current.
Why geography deserves a layer
"What place is this, and which places is it like?" is the question underneath a huge amount of AI and analytics work, and it's almost always answered badly. Names collide. Boundaries shift. Identifiers don't agree across systems. Comparing two cities at face value is usually misleading because the right peer set is something more specific.
The geography layer fixes that upstream. Every place has a stable identity, a hierarchy, neighbors, and the cohort definitions that determine *who it should be compared to*. Demographics, economy, and history come along with the entity. Your AI gets to reason about places instead of about strings.
What's in the geography layer
The Corthos capabilities, applied to places.
| Capability | What it means |
|---|---|
| Place graph | Cities, counties, MSAs, states, and nations as first-class entities, with names, identifiers, and the relationships between them resolved across sources. |
| Hierarchies & adjacency | Place hierarchies (city → county → MSA → state → nation) and adjacency relationships exposed directly — so an agent can walk from a city to its peers, parents, or neighbors in a single query. |
| Demographic layers | Population, income, education, housing, and other demographic measures attached to every place at the right granularity, with definitions and units carried with the data. |
| Time series & projections | Multi-year history for every place. Trends, year-over-year change, and forward estimates available alongside the underlying records. |
| Comparable-place cohorts | Cohort definitions that combine size, region, demographics, and economy so each place can be compared against peers that actually look like it — not against the whole country. |
| AI-ready surfaces | Friendly names, defined relationships, and lineage exposed through APIs and grounded retrieval — so an LLM can answer place-level questions with citations and consistent definitions. |
| Continuously maintained | Boundary changes, demographic releases, and identifier revisions land in the layer continuously. Your team never inherits a stale snapshot. |
What you can build on top
Location-grounded AI agents
Assistants that resolve place names, walk hierarchies and neighbors, and answer geographic questions with citations — without inventing boundaries.
Demographic & market-sizing products
Aggregations, peer comparisons, and trend analysis across the right cohort of places — with the definitions and lineage exposed.
Comparative city/region pages
Editorial and product pages that profile a place against its real peers, with rankings and time series ready to render.
Geo-aware journalism & policy work
Source-grounded data for stories and analyses about places — every claim citable, every comparison framed against the right cohort.
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.