Use cases
Questions about replacing brittle OCR templates and coordinate-based extraction rules with AI extraction workflows.
Variable formats
Use AI extraction and document classification around a consistent output schema so new sender layouts do not require a full OCR-template rebuild every time.
OCR replacement
Move from coordinate-only OCR templates to an AI extraction workflow that understands field meaning, supports review, and can be updated with instructions when formats change.
Extraction accuracy
Prevent false positives and false negatives by defining fields clearly, validating expected formats, testing real edge cases, and routing uncertain outputs to human review.
Custom instructions
Yes. Custom instructions can capture sender-specific rules and edge cases while preserving a common extraction schema, so teams do not need a separate template for every small variation.
OCR modernization
Move the workflow from fixed OCR templates and custom scripts to a managed process with AI extraction, classification, validation, review, monitoring, and downstream exports.
New senders
Handle new senders by testing their documents against the existing schema, routing unknown formats to review, and updating instructions or workflows only when needed.
Changing wording
Use field definitions and examples that describe the business meaning of each value, then validate and review outputs when senders use different phrasing.
Sender rules
Use a shared schema for the document type, then add sender-specific instructions only for recurring, meaningful differences that affect extraction or validation.