Document Understanding or IXP? A decision table by document type

UiPath now has two routes for documents, and the choice often starts in the wrong place: with “which one is newer?” A better starting point is the document itself. Two questions follow. How much generative AI does your organisation allow, and where will the system run?

At BOTTEQ we build document-processing lines by combining Document Understanding with Base64.ai’s OCR/ICR engines. The table below doesn’t come from that work, though. It comes from UiPath’s own documentation, with the sources at the end. The commentary is mine.

The two routes in brief

Document Understanding (DU) is for structured and semi-structured documents: forms, purchase orders, applications, invoices. It works with pre-trained machine-learning models on documents whose layout is the same or very close. Its generative features can be governed through the organisation’s AI Trust Layer policies. DU continues as a standalone service and remains available on Automation Suite and Standalone deployments.

IXP is the umbrella under which UiPath brought DU and Communications Mining together and added a new prompt-driven capability: generative extraction for unstructured and complex documents. UiPath describes it for annual reports, contracts and lease agreements. Complex tables, charts, checkboxes, signatures and handwriting, values that have to be inferred from context or stitched together across sections: that’s this route’s territory. Generative AI is mandatory here.

Decision table

Three questions the table can’t answer

Mixed bundles. When ID, proof of address and bank statements arrive in a single file, as with a loan application, the real work starts before extraction: splitting the documents and identifying their types. Whichever route you pick, settle where and how that splitting happens during the pilot. The division of labour between rules, a model and a person here can matter more than the extraction itself.

Project type. DU has a choice inside it too. Modern projects give faster, guided model development. Classic projects allow exporting models and datasets and cross-tenant use, but run only on Automation Cloud. AI Center-based projects give the most control and also support Standalone deployments, but have no Cloud API support. If you must run on-premises, that alone can decide the matter.

The cost unit. Since July 2026, IXP extraction from unstructured and complex documents is metered per operation rather than at a flat rate per page, and IXP and DU licences can be used together without extra per-page cost. If you have an old business case built on page counts, re-run it on a real sample of documents.

Two traps when moving to IXP

The label you confirm is what counts as right. IXP scores a model against the labels you confirmed. Confirm a wrong value while labelling and it becomes the reference, and the score goes up. A high score doesn’t prove the values are correct. Treat labelling as part of acceptance testing, not a chore to finish quickly.

Don’t mistake a format difference for an error. Built-in data types such as dates and monetary amounts come back normalised. If “21-JUN-22” on the page and the ISO date the model returns are the same value, that isn’t a mistake. What deserves your attention is a change in meaning, such as a misread decimal separator.

My default

If most of the document set is invoices, forms or orders with a known layout, I start with Document Understanding. I gather the documents that take the most validation time, the ones with scattered layouts or values that depend on context, into a separate queue, and I try IXP on that queue first. The two routes can run side by side in the same line. I think it’s healthier to treat the choice as a decision per document type than as a one-off platform decision.