Sheaf
Sheaf vs Hyperscience
Hyperscience splits with page-count and regex rules you write, and trains classification on ten or more pages per layout. Sheaf needs neither a rule nor a training set.
The short answer
Hyperscience is a supervised platform: rules to write, layouts to train, and a review queue to staff. Sheaf removes all three. Boundaries are determined, requirements are derived, and the routine file clears automatically under thresholds a reviewer controls.
Sheaf vs Hyperscience at a glance
| Requirement | Sheaf | Hyperscience |
|---|---|---|
| Split a mixed bundle | Determined boundaries that tile the file end to end | Rules: fixed page counts or regex |
| Setup before first result | None — upload and go | Splitting rules to author, or layouts to train |
| Rules to write and maintain | None | Page-count and regex logic per case |
| Classification training | None | Ten page examples per layout, 120 advised |
| Unanticipated documents | Read on what the document says about itself | Outside the trained layouts |
| Grounding | File, page and span, supplied by the reader | Field locations in the review screen |
| Missing documents | Named by requirement, recomputed live | Per-file processing |
| Portfolio completeness | One dashboard across every open application | Platform reporting |
| Cross-document consistency | Names, dates and amounts checked automatically | Rule failure handling |
| Straight-through processing | Per-requirement thresholds you control | Confidence threshold routing |
| Reviewer corrections | Become rules scoped to your organization | Supervision retrains the models |
Where Sheaf wins
- No rules to write Splitting on fixed page counts or regular expressions works until a counterparty adds a page. Sheaf determines boundaries from the documents themselves, so a revised form is not a broken rule, an escalation and a ticket.
- No training set per layout Classification training starts at ten page examples per layout, with well over a hundred advised for a robust model. Multiply that by every form every counterparty sends you and the collection effort is the project. Sheaf classifies with zero examples of anything.
- The long tail costs nothing A document seen four times a year will never justify collecting a training set or hand-writing a splitting rule, so on a supervised platform it stays manual permanently — by economics rather than difficulty. In Sheaf it is read like everything else.
- Completeness, not just extraction Sheaf names the requirement nothing satisfied and checks that names, dates and amounts agree across every filed document. Accurate field capture on each document individually does not answer either question.
- Supervision is the exception, not the design Both products route on confidence. The difference is the baseline: Sheaf is built to clear the file automatically and escalate what genuinely needs a person, rather than treating a staffed review queue as the normal path.
Where Hyperscience is strong
- Rule-based splitting needs no model If your bundles genuinely are a fixed page count every time, a rule is fast, cheap and completely predictable.
- A mature supervision workflow Manual review for classification, boundary validation and rule failures is well developed, and that surface is harder to build well than it looks.
- Confidence-driven automation rates Thresholds decide what processes automatically and what goes to a person, which is the right control to expose. Sheaf exposes the same control per requirement.
Choose Sheaf if
- Your documents come from outside your organisation and change without notice.
- You cannot fund a training-data collection effort per layout.
- The question holding files up is completeness and consistency, not field accuracy.
Choose Hyperscience if
- Your bundles have a genuinely fixed structure a page-count rule can express.
- You have a large, stable set of layouts and staff to build and maintain the training data.
The bottom line
Hyperscience is a supervised platform that rewards investment in rules and training data. Sheaf gets you to a higher automation rate without either — which matters most when the documents were never yours to standardise.