Document AI use cases

Rankings by document workflow

Inside each Yardstick Lab category, the best tool depends on the task. In document AI, that means the files, schema, rows, and production edge cases behind the workflow.

Buying paths

Use-case rankings being tracked

Each category can produce a different winner from the overall leaderboard, especially when the task involves repeated rows or difficult scans.

Use case 01

Structured extraction

For teams that need predictable JSON from documents, not plausible text or summaries.

Use case 02

Tables and repeated rows

For workflows where line items, transactions, and tabular fields have to stay aligned.

Use case 03

Hard production documents

For multilingual files, rotated scans, photos, dense layouts, and nested structures.

Current evidence

Early leaders by slice

The current slice data comes from the 50-document structured extraction run. More vendor rows will be added after each tool has matching outputs.

Structured extraction

Overall JSON extraction

Best for schema-driven workflows.

DocuPipe high settingcurrent leader

97.56%

DocuPipe standard settingsame run

96.31%

Direct model baseline Cmodel-only run

95.80%

Tables

Arrays and reconciliation

Best for repeated rows and totals.

DocuPipe high setting30 array-heavy docs

97.41%

Extendsame array subset

91.98%

DocuPipe high setting24 reconciliation docs

96.89%

Hard documents

Language and layout stress

Best for production edge cases.

DocuPipe high settingright-to-left subset

90.72%

Extendright-to-left subset

76.07%

DocuPipe high settingCJK subset

99.12%

Selection rules

What to check before choosing a tool

A high overall score matters, but the right buying decision depends on the document families that match your workflow.

Match your schema

Prefer the rank tied to the output shape you need: flat fields, nested data, or repeated rows.

Check failure modes

Look for wrong-row errors, missing line items, unsupported extra values, and hallucinated fields.

Use dated runs

Compare tools on the same run date so product changes do not mix old and new behavior.

Watch the baselines

Direct model baselines show when a full extraction platform adds value beyond a raw model call.