Reading a document is only the first step in automating the work around it. An invoice total must match its line items. A contract date must belong to the right version. A missing field must remain visible rather than being filled with a plausible guess. Intelligent document processing connects extraction with these business checks.
On September 23, 2026, UiPath announced platform updates that include coding agents for document extraction setup, prediction checks and field refinement, with people setting quality requirements and approving outcomes. The broader lesson is that document automation needs a repeatable quality process.
How is document processing different from OCR?
Optical character recognition turns an image into text. Intelligent document processing adds structure: which company issued the invoice, which amount is payable and which purchase order it references. A workflow then decides whether the extracted information is complete enough to use.
For documents with complex layouts, the relationship between a label and its value matters as much as recognizing the characters. Tables, footnotes and scanned attachments make that relationship harder to preserve.
Design the output before choosing the model
Start with the fields your downstream process actually needs. Define their formats, whether they are mandatory and which source page supports them. A useful extraction result includes the original value, its normalized form and a trace back to the document.
In an illustrative supplier onboarding process, a team might extract a company name, tax identifier and account details. It should distinguish a missing identifier from an unreadable one, because those exceptions require different responses.
Validate at field and document level
Field checks confirm that a date is valid or that a currency code is supported. Document checks establish whether totals reconcile, the expected pages are present and two files represent the same invoice.
Create a review queue for ambiguous cases. A model’s confidence score is useful only after testing how that score relates to actual mistakes on your documents. A high average accuracy can still conceal failures on the fields that matter most.
Our earlier guide to no-code document automation explains the wider workflow. The next step is making its quality requirements explicit.
Measure the full process
Compare extraction accuracy by field, correction time and the proportion of documents completed without repair. Include scans, unfamiliar supplier layouts and incomplete files in your evaluation set.
Also measure what happens after extraction. If employees copy the output into another application, the process still contains a manual handoff. A successful pilot should show that the data arrives in the right place with enough evidence for someone to trust it.
A practical document acceptance checklist
For each required field, write down its accepted format, source location, validation rule and response to a missing value. For a total amount, a useful check is agreement with line items and tax; for a supplier identifier, it is agreement with the approved supplier record.
Keep separate counts for wrong values, missing values and cases correctly sent to review. Combining them in one accuracy figure hides whether the system invents data or recognizes uncertainty. Use synthetic document scenarios to expand difficult cases, and compare multi-model routing only after fixing the acceptance rules.
Turn documents into usable information
Begin with one document family and a small set of valuable fields. Explore document extraction alongside the validation and review steps your team needs. Contact NextBrain AI to discuss a workflow grounded in your own document samples.