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ZeroTrain Data InputsZeroTrain evaluates structured business data using deterministic rule-based logic. Both InputSource (runtime data) and ModelSource (rules and configuration) accept the same structured formats.
Important:
ZeroTrain is format-agnostic at the boundary. All supported input formats are internally normalized
into a consistent tabular representation (rows and columns) before deterministic evaluation.
Supported Data Formats
Example: JSON Object
{
"InputSource": {
"CustomerTenureMonths": 48,
"AccountStatus": "Active",
"SentimentScore": -0.62
}
}
Example: JSON Array (Tabular)
{
"InputSource": [
{ "CustomerId": 1, "Spend": 200, "Status": "Active" },
{ "CustomerId": 2, "Spend": 450, "Status": "Active" }
]
}
Example: Tabular Result Set (Rows & Columns)Columns: CustomerId | Spend | Status Rows: 1 | 200 | Active 2 | 450 | Active Note: ZeroTrain does not ingest SQL query statements. It ingests the resulting structured records (rows and columns). Example: Dataverse RecordsDataverse Table: Accounts Columns: AccountId | Revenue | Status Rows: A001 | 150000 | Active A002 | 85000 | Inactive Example: CSVCustomerId,Spend,Status 1,200,Active 2,450,Active Example: XML
<Customers>
<Customer>
<CustomerId>1</CustomerId>
<Spend>200</Spend>
<Status>Active</Status>
</Customer>
<Customer>
<CustomerId>2</CustomerId>
<Spend>450</Spend>
<Status>Active</Status>
</Customer>
</Customers>
Example: Base64 EncodedQ3VzdG9tZXJJZCxTcGVuZCxTdGF0dXM= (Base64 representation of CSV or JSON payload) Core Design PrincipleRegardless of origin or format, all supported data types are normalized into a deterministic internal tabular model prior to rule evaluation. This ensures:
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