Can AI extract data from narrative text and not just tables?

Direct answer

AI extraction is useful when the value is described in a sentence instead of sitting in a neat grid. The field can be defined as a question or business concept, and the workflow can search the document for the best supporting value.

This matters for proposal documents, case notes, applications, medical records, claim files, contracts, and other documents where key facts are embedded in prose.

What the workflow should include

A narrative extraction workflow should define each field clearly, include examples of acceptable answers, and specify what should happen when the answer is not present.

It should also distinguish exact extraction from interpretation: some fields are copied directly, while others may need normalization, categorization, or human review.

How Lido helps

Lido lets teams define extraction columns for narrative fields, add instructions, and review extracted results before they move into a spreadsheet or business system.

That makes it possible to automate work that traditional table extraction or zone-based OCR would usually send to manual data entry.

Example workflow

1. Write each desired output field as a clear business question or column name.
2. Provide examples for fields that may be worded differently across documents.
3. Flag missing, conflicting, or inferred values for review.
4. Export approved fields into the same schema every time.