Stork Delivery
Start document extraction with the exception pile
Evaluate document automation against the messy inputs that consume staff time, not just the clean examples that make a good demonstration.
The problem: clean document demos hide the cost of exceptions
A supplier sends a clean PDF with a familiar layout. The extraction tool finds every field. Then the next document arrives as a rotated scan with a handwritten correction and two different delivery addresses.
If that second document resembles the team's ordinary work, it belongs in the evaluation from the start.
Build the sample around the exceptions people actually handle.
A practical approach
Collect variety deliberately
Include the common document types and the cases that require intervention: missing pages, low-quality scans, revised quantities, unfamiliar layouts, and conflicting references.
Use approved material and control access to the collection. Preserve enough context for reviewers to know the intended result without exposing information unnecessarily.
For each sample, record which fields matter to the downstream process. Extracting a decorative heading correctly is less important than getting an item number or quantity right.
Validate against the business
A plausible value is not necessarily a valid value. Check extracted identifiers against the appropriate catalog or account records. Validate relationships and units where the workflow requires them.
For example, “12” may refer to individual units, cases, or a pack size. A number without its meaning can produce a neatly formatted mistake.
Keep the original document linked to the proposed record so someone can inspect the evidence.
Design a useful review
Present uncertain or conflicting fields with their relevant source context. Let the reviewer correct the proposal without re-entering the whole document.
Do not assume a confidence score is calibrated to the business consequence of an error. Evaluate actual outcomes on your sample and use rules appropriate to the task.
A missing optional note and a disputed bank-account instruction should not enter the same automatic path just because both have a numeric score.
Test the next system too
The job is not finished when the text is extracted. Verify that the validated result can be accepted by the application that needs it.
Include duplicate documents, corrections to an earlier submission, and interrupted transfers. Decide whether a revised document updates an existing record or creates a new one.
Measure the remaining review effort as well as extraction accuracy. A tool that finds many fields but makes the awkward cases harder to resolve may offer less value than its demo suggests.
Useful document automation combines extraction, validation, review, and reliable delivery into the business workflow. The exception pile tells you how well that combination works.
The result: validated records with a manageable review burden
Trial representative documents all the way into a test destination. Check critical fields, units, duplicates, corrections, and the time a reviewer spends resolving uncertainty. Agree on release criteria based on business consequences rather than a single extraction score. Start with the documents staff currently find hardest, and use their corrections to decide which validation and review improvements matter most.