Ore is a usage-based API that uses a recursive self-improving agent to generate structured tabular data. Define a schema. The agent generates, critiques, and refines — producing data your models will actually train on.
Declare field names, types, value distributions, and constraints. JSON or a future SDK.
The agent produces an initial batch of records that satisfy your schema.
It audits its own output for statistical coherence, distributional drift, and constraint violations.
Failing records are regenerated and the critique loop repeats. Higher quality_passes = higher fidelity.
Declare field types, value distributions, and constraints. Ore enforces them across every row — and then critiques the output against statistical coherence before delivering the dataset.
{
"schema": {
"fields": [
{ "name": "user_id", "type": "uuid" },
{ "name": "age", "type": "integer", "min": 18, "max": 80 },
{ "name": "plan", "type": "enum",
"values": ["free", "pro", "enterprise"],
"weights": [0.70, 0.25, 0.05] },
{ "name": "ltv_usd", "type": "float", "min": 0, "max": 5000,
"distribution": "log-normal" }
]
},
"rows": 50000,
"quality_passes": 3
}Pay for what you generate.