Export · On request
The layer, as files.
Bulk data dumps in CSV, Parquet, or JSON for offline analysis, warehouse loads, and editorial work. The same entity model and lineage as the live API — just packaged for batch.
Who it's for
Analysts who live in a notebook. Editorial teams who want to load the data into their own pipeline. Data engineering teams who want the layer in their warehouse next to everything else they own. Researchers who need a reproducible snapshot for an analysis they\'ll publish.
If your workflow is "pull the data once, work with it locally, ship the result later," Export is the surface that fits — and the data you get is the same data the API serves, with the same definitions and lineage attached.
What you get with Export
The same layer, in formats your stack already speaks.
| Capability | What it means |
|---|---|
| Standard formats | CSV, Parquet, and JSON. Use what your stack already speaks. Schemas are stable across releases; column definitions are documented and versioned alongside the data. |
| Same entity model as the API | The exports are the API surface, materialized. An institution row in your warehouse matches the same institution returned from a live API call. No translation layer required. |
| Lineage and metadata included | Companion files describe where each column came from, how it was computed, what it means, and when it was last refreshed. Your data team gets a manifest, not a mystery. |
| Versioned snapshots | Every release is timestamped and addressable. Reproduce last quarter's analysis exactly, or always pull the latest. Both supported. |
| Scheduled refreshes | Subscribe to a delivery cadence — weekly, monthly, or aligned with source releases — and exports land where you want them. |
| Delivery to your stack | Direct download, signed URLs, or delivery to your S3 / GCS bucket. Whatever fits your warehouse-load workflow. |
What you can build on top
Notebook & editorial analysis
Pull a snapshot, work in pandas / DuckDB / SQL locally, produce charts and stories with full lineage attached.
Warehouse loads
Drop the layer into Snowflake, BigQuery, or your warehouse of choice. Join it against your own data without giving up source-grounding.
Research & reproducibility
Pin a versioned snapshot for a published analysis. Anyone can reproduce your numbers from the same files, years later.
Internal datasets & ML training
Use the layer as feature data for in-house models or as a reference dataset for evaluation — with definitions traveling alongside the rows.
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.
Want the layer as files?
Get in touch to discuss how the Corthos domain-intelligence layer fits your data, your domain, and the AI products you're building.