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Graded 8 October 2026

Can Landing AI answer a document question that isn't in the extraction schema?

Landing AI answered a document question outside the extraction schema and tied the answer to the source document. In the invoice test, it returned 06 Jul 2020 for the due date and showed a visual reference to the row containing that value. A second wording returned the same attributed answer.

1 of 1 test case passed

Every test case in this scenario has a result.

Pass rate100%1 of 1 with a result
Coverage1 of 1test cases with a result
1PassDid everything it was expected to do.
0FailNo test case failed.
0Not gradableEvery result here could be graded.
0UntestedEvery test case in this scenario has a result.
The pass rate is a summary. The evidence is the test case below: what we sent, what we checked, what the tool returned, and the proof.

The test case

Each test case is judged on its own: Pass, Fail, Not gradable, or Untested. The scenario result above counts this row.

Answer outside the schema, attributedPassEvidence
What we sent

Input 1: “What is the due date on this invoice?”

Input 2: “By which date does this invoice have to be paid?”

InputDocument (PDF)
Two-page invoice PDF showing the due date on page 1.
Open the PDF ↗
What the tool returned

The tool's reply is shown in the screenshots under Proofs.

Expected vs. Found
✓Expected: It distinguishes between extracted data and the document, either saying the field is absent or answering from the document with attribution.Found: It answered from the document, returned 06 Jul 2020, and showed a visual reference to the invoice row containing that date.
Supporting proof
Proof 1Screenshot
Chat view with the first due-date answer and its citation.
Open original ↗
Proof 2Screenshot
Same chat after the citation is clicked, with the invoice row highlighted and a second answer shown.
Open original ↗
Proof 3Captured file (text)
Typed chat transcript listing the schema and two due-date turns.
Text22 lines
{
  "surface": "Tool > Chat (labelled Experimental; works with this file only, not available as an API)",
  "file": "main_invoice_multipage_blackwood.pdf (in the 3-file project)",
  "schema_in_project": [
    "invoice_reference",
    "total_due",
    "total_due_all_documents"
  ],
  "turns": [
    {
      "question": "What is the due date on this invoice?",
      "answer": "The due date on the invoice is 06 Jul 2020.",
      "visual_reference": "5. table"
    },
    {
      "question": "By which date does this invoice have to be paid?",
      "answer": "The invoice has to be paid by 06 Jul 2020.",
      "visual_reference": "5. table"
    }
  ],
  "note": "The first question was asked three times in total across the day; the answer was the same each time."
}
Open the text file ↗
Why this result

It checked whether the answer was treated as schema data or tied to the document. The product returned 06 Jul 2020 and showed a visual reference to the invoice row with that date; a second wording produced the same attributed answer.

Also observed on this row

Chat limitation note. The chat view says it works with this file only and is not available as an API.

Tested by Rugved nichite · evidence dated 8 October 2026

Configuration and setup

How this tool was set up for the run and what the test needed in place. Each row is a fact from the run's records; a fact the records do not hold is left out, not guessed.

Software that produced the output
Landing AI
Build or version
DPT-3 Pro (parse model; app version not exposed)
Plan or tier
Explore (free credits, no subscription)
Surface
Web app
Set up before the run
The page needed a fact clearly stated in the document but not present in the extraction schema. The question asked directly for that fact.
Mode
Tool > Chat
Project extraction schema
invoice_reference, total_due and total_due_all_documents
Tested
8 October 2026 · Rugved nichite

How this scenario is graded

How we decide Pass, Fail and Not gradable. The same rules apply to every tool tested on this scenario.

How results are decided

Each test case gets one result per tool: Pass, Fail or Not gradable. A test case we haven't run yet shows Untested. There are no partial results.

The rules
  • Pass: the tool did everything the test expected, and nothing it said contradicts the correct answer.
  • Fail: at least one expected behaviour clearly didn't happen; the row says which and quotes the tool.
  • Not gradable: our evidence couldn't settle the outcome (for example a record we needed is missing). It is never counted as a fail, and the row says what's missing.

Where this sits in the benchmark

This page is one cell of a larger study: one tool, one scenario. Only this benchmark's frame appears here.

LevelNameScope
BenchmarkStructured Document Extraction →19 scenarios · 10 tools
CapabilityQuerying →
ScenarioThe answer was never in the schema →
ToolLanding AI →

History of this result

What has happened to this result since it was first published. Runs and grades are never overwritten: a retest or a re-grade publishes a new result and keeps the earlier one readable.

from the publication record
9 October 2026First publishedStructured Document Extraction v1

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This matches what I see

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This does not match

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Point out an issue

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Vendor notice →
Filed against this evidenceNothing yet. Challenges, counter-evidence and fix notices appear here with their outcome, and stay on the page after they are resolved.
Cite this result
aidemos.com/benchmarks/structured-document-extraction/results/landing-ai/the-answer-was-never-in-the-schema · 1 pass · 0 fail · coverage 1/1 · graded 2026-10-08

The same record is available as structured data through the AI Demos MCP server, with the counts, the coverage and every per-test-case reason carried as fields.

Verify the proof files

These files support this result. Open a file to inspect the original evidence.

Input · Document (PDF) ↗application/pdf · 82 KB
Proof 1 · Screenshot ↗image/png · 116 KB
Proof 2 · Screenshot ↗image/png · 138 KB
Proof 3 · Captured file (text) ↗text/plain; charset=utf-8 · 1 KB
File fingerprints (SHA-256)

A fingerprint identifies the exact file used for this result.

Input · Document (PDF)d1f3282702260730267278b1071ee30cf79918fa8489d9411c98d8f6e5209f18
Proof 1 · Screenshot2b9c4716233911cc16718be9e00c2d5cd8163f4b7763ad91f2d6dbbff7faee8e
Proof 2 · Screenshotb4cfed1048b34c008f494b12b2665cd6c31ca42cc50fdbf5d094f3cbf6920496
Proof 3 · Captured file (text)5b380d97d803ce83208b3b57033fb9fd913ebc7cde13add6e96d794dfbdaabda