Sheaf
What the work requires

Scanned and photographed documents

Real document work arrives as a scan of a print of a fax, often photographed on somebody's desk.

The ordinary case

Most document tooling treats a clean digital PDF as the normal input and everything else as degraded. That is backwards for operational work. The clean PDF is the lucky case. The normal case is a scan with a slight rotation, a staple shadow down one edge, and a signature page photographed in portrait on a phone.

Sheaf is built for the normal case. A page with no text layer is not routed to a person, flagged as low quality, or read with a reduced set of capabilities. It is read.

A text layer says what a document is, not what it says

This is the least obvious finding in the whole pipeline, and it is measured rather than assumed. When a form is filled in, the printed chrome — the field labels, the form number, the footer — often lives in the text layer while the filled values do not. The text layer is therefore excellent evidence of which form this is and unreliable evidence of what was entered on it.

Treating those two questions as one is how systems end up confidently reading a blank form and reporting the labels as values.

The rule

Use the text layer to identify. Read the page to extract. Where the two disagree about whether a field was filled, the page wins.

Reading a page with nothing to read

Where there is no usable text layer, the page is rendered and read optically, and the resulting geometry becomes the same page geometry every citation is bound to. From that point on a scanned page behaves exactly like a digital one: values carry a file, a page and a span, and can be highlighted rather than described.

A tick is content

Checkboxes are where transcription and meaning part company. A ticked box and an empty box are a few pixels apart and mean opposite things, and plenty of pipelines drop the distinction entirely because it is not text. Sheaf reads checkbox state as content, because on a great many operational forms the ticks are the answer and the prose is packaging.

What still gives it trouble

Honest limits

Handwriting in free-text fields remains the hardest input in document work, and confident handwriting recognition is where we would rather abstain than guess. Severe skew, heavy shadow across a value, and pages photographed at an angle that hides part of the sheet will also produce an abstention rather than an answer. In each case the outcome is a flagged gap you can see, not a number you cannot check.

Put real document mess to the test. Send the bundle that currently ruins someone's afternoon, exactly as it arrived.

The rest of the site

Everything Sheaf claims, in writing

Document work is bought on specifics, so the specifics are on their own pages: what we do that a neighbouring category does not, one page at a time.

Against the alternatives

Sheaf vs LandingAI ADE

Excellent composable primitives with real visual grounding. Sheaf is the finished desk those primitives would need to be assembled into.

Sheaf vs Reducto

Agentic Deep Split and grounded citations. Sheaf adds the application, the approval and the coverage arithmetic on top.

Sheaf vs V7 Go

The other product that goes at completeness. Theirs checks a checklist you curate; Sheaf derives the requirements and matches documents as they land.

All comparisons

Ten head-to-head pages and one table — LandingAI, Reducto, V7 Go, Azure, Google, Textract, Instabase, Hyperscience, Rossum and the general models.