Can I define expected data types for AI-extracted fields?

Direct answer

Expected data types are one of the most important safeguards in document extraction. If a field should be a date, amount, integer, decimal, ID, currency, or category, the workflow should check that before the record is accepted.

Data type checks help catch OCR mistakes, ambiguous text, missing values, and fields that were extracted into the wrong column.

What the workflow should include

Define each column's expected type, accepted formats, normalization rules, and whether failures should block export or simply create a warning.

For downstream imports, validation should match the destination system's requirements so the file or API payload does not fail later.

How Lido helps

Lido gives teams a spreadsheet-like place to define fields, review values, and add validation or workflow rules before the data moves downstream.

That makes it easier to enforce clean, import-ready outputs from messy documents.

Example workflow

1. List each extraction column and its expected data type.
2. Normalize dates, currencies, decimals, IDs, and categories into the destination format.
3. Flag type mismatches for review.
4. Export only records that pass the required validation checks.