Sheaf
Sheaf vs Azure Document Intelligence
Azure splits documents once you have trained a custom classifier. Sheaf splits the first bundle you upload, then files it, checks it and closes it.
The short answer
Azure Document Intelligence is infrastructure with a setup cost: a custom classifier trained on at least two classes with five samples each. Sheaf needs none of it, and keeps going where Azure stops — filing, completeness, cross-document validation and an audit trail, with SSO and role-based access for the enterprise checklist.
Sheaf vs Azure Document Intelligence at a glance
| Requirement | Sheaf | Azure Document Intelligence |
|---|---|---|
| Split a mixed bundle | Determined boundaries that tile the file end to end | Custom classifier splits into types and page ranges |
| Setup before first result | None — upload and go | Two classes minimum, five samples each |
| Unanticipated document types | Read on what the document says about itself | Falls outside the trained classes |
| Grounding | File, page and span, supplied by the reader | Bounding regions |
| Missing documents | Named by requirement, recomputed live | Per-file analysis |
| Portfolio completeness | One dashboard across every open application | Yours to build |
| Cross-document consistency | Names, dates and amounts checked automatically | Per-file analysis |
| Straight-through processing | Per-requirement thresholds you control | Confidence scores |
| Filing and approval | Ships as the product | Platform and studio |
| SSO and role-based access | Built in | Azure AD |
| Audit trail | Every outcome walks back to a page and a person | Yours to build |
Where Sheaf wins
- Zero setup, first result today No classes to define, no samples to collect, no training run. The gap between signing up and getting separated documents out of a real bundle is one upload.
- The long tail is the ordinary case A trained classifier knows the classes it was trained on. The type that shows up four times a year never justifies its own class, so it stays manual by economics. Sheaf reads it like any other document.
- Requirements, not just classification A class label on a page range is not filing. Sheaf decides that this document satisfies that requirement, checks it against every other document in the file, records who approved it, and lets the approval be withdrawn.
- The complete-file answer Reading a file does not establish what was supposed to be in it. Sheaf names the requirement nothing satisfied, and rolls completeness up across every open application.
- Enterprise controls without the cloud lock-in SSO and SAML, role-based access control and regional hosting come with Sheaf, so consolidating on one cloud is no longer the reason to accept a setup project.
Where Azure Document Intelligence is strong
- Transcription at scale Skew, multiple columns, mixed scripts and low-resolution scans. This category is solved and Azure is very good at it.
- Incremental training New classes can be added to an existing classifier as they appear, which softens the maintenance cost — though it does not remove it.
- You are already on Azure Identity, networking and billing are solved. A real advantage, and the main one Sheaf has to answer.
Choose Sheaf if
- Your document types are not a stable, known set worth training on.
- You need completeness, filing and approval, not just classification and text.
- You want value on the first bundle rather than after a labelling project.
Choose Azure Document Intelligence if
- Your document types genuinely are stable and worth training a classifier on once.
- You need text and coordinates, and a system downstream already knows what each document is.
The bottom line
Azure rewards a stable document set and punishes a changing one. Sheaf is built for the opposite: documents you did not choose, arriving in a shape nobody configured — with the enterprise controls to match.