Can document automation sort, merge, and normalize extracted results?

Direct answer

Document automation should often include post-extraction steps such as sorting by date, merging related outputs, normalizing names or amounts, aggregating rows, and preparing data for import.

Those steps matter when documents are classified into different extractors but the business still needs one clean downstream output.

What to define

Define the target output order, merge keys, normalization rules, date and currency formats, duplicate handling, and which fields should be aggregated versus kept as separate rows.

Post-extraction rules should run after validation so bad or ambiguous values do not get merged into clean records unnoticed.

How Lido helps

Lido supports workflow steps around extraction so teams can classify, extract, sort, merge, transform, and export data in the shape their downstream process needs.

That makes the automation useful beyond the first extraction table.

Example workflow

1. Define what the final output should look like.
2. Extract from each document type into the right intermediate schema.
3. Normalize dates, amounts, names, and identifiers.
4. Merge, sort, and export only records that pass review and validation.