Sheaf vs Instabase
Instabase starts with sample files, a schema and sometimes Python. Sheaf starts with the bundle you already have.
The short answer
Instabase rewards configuration: define classes and fields, upload representative samples, write Python for the hard boundaries. Sheaf removes the configuration step entirely, which is what you want when the documents are not yours to standardise.
Sheaf vs Instabase at a glance
| Requirement | Sheaf | Instabase |
|---|---|---|
| Split a mixed bundle | Determined boundaries that tile the file end to end | Model classification or custom Python boundaries |
| Setup before first result | None — upload and go | Sample files, plus a class and field schema |
| Custom code to maintain | None | Python boundary functions |
| Unanticipated document types | Read on what the document says about itself | Outside the defined classes |
| Grounding | File, page and span, supplied by the reader | Field-level review interface |
| 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 | Per-file processing |
| Duplicate and superseded versions | Detected and flagged | Per-file processing |
| Straight-through processing | Per-requirement thresholds you control | Platform rules |
| Reviewer corrections | Become rules scoped to your organization | Edit fields and classes |
Where Sheaf wins
- No schema project Instabase asks you to upload files representing the document types you will process, then define a schema of classes and fields before the first result. Sheaf has no per-type setup at all, so the first useful output is minutes away rather than weeks.
- No Python to maintain Custom boundary functions are powerful, and they are also code someone owns forever, keyed to the exact wording of forms other companies revise without telling you. Sheaf determines boundaries without any of it.
- The long tail pays off immediately A document seen four times a year never justifies its own class in a configured platform. In Sheaf it is just another document, read the same way as everything else, from the first time it appears.
- Requirements, completeness and consistency Sheaf states what a complete file looks like once, at the level of the requirement, rather than per layout for every counterparty who sends the same instrument in a different font — then names what is missing and flags where the documents disagree.
- Duplicates caught before a person sees them The same document arriving twice, or a superseded version arriving after the current one, is detected and flagged rather than filed twice and reconciled later.
Where Instabase is strong
- Precision on configured types Once a type is set up, extraction is accurate, cheap and identical every time. On a fixed set of forms, that arithmetic is hard to beat.
- An escape hatch to code Custom Python means an unusual splitting rule is always expressible, which matters for teams with engineers and a genuinely strange edge case.
- Breadth A broad automation platform rather than a document tool alone, which suits organisations standardising on one vendor.
Choose Sheaf if
- Documents arrive from counterparties who change their forms without telling you.
- You cannot afford a configuration project before the first result.
- Completeness and filing matter as much as extraction accuracy.
Choose Instabase if
- You own the document types and they do not change.
- You are standardising broad automation on a single platform.
The bottom line
Instabase is strong where the document set is known and stable. Sheaf is built for the problem underneath it: the twenty per cent nobody configured is the twenty per cent consuming your team.
Sheaf