AI in data processing: extraction and review

Interpret incoming information

AI can help when input does not fit a ready-made form: a customer email, a scanned document or a written request. Give it a specific task, such as finding an order number, extracting line items or classifying a message. For example, Document AI describes document processing that extracts text and structure. This illustrates a tool, without implying that it is connected to your workflow.

Before choosing a model, identify the fields the next step actually needs. Keeping the source passage alongside each field helps a reviewer compare the proposed value with the original document.

Give AI a bounded role

Consider an order received by email. AI proposes field values; business logic then checks required information, order number format and allowed status. An unknown value remains unknown. A plausible address or amount must not replace missing information.

One practical design is to keep proposed and confirmed values separate, show their sources and send disputed fields for review. System rules determine who may approve an order. A model response does not grant that permission.

Test the exceptions

Use examples you are permitted to process: incomplete emails, several orders in one message and unclear scans. Agree what makes an output usable, who corrects errors and when ordinary rules are sufficient without AI.

Longer extraction work can run through background workers. The server preserves workflow state so a reviewer can find pending work and continue it.

Discuss your workflow

Explore AI with human review and the Dent-Picks project as a separate example of an operational system. Describe your input and required output to discuss automation boundaries and review.