AI Demos Research — the structured-intelligence platform. Every verdict on these pages opens to the execution behind it.
Graded 29 October 2026

Can Landing AI filter records across extracted documents?

Landing AI failed to filter records across extracted documents. Chat refused the request, and the interface only showed one document at a time with no filter or query control; the receipts themselves were extracted correctly, including two above 150 and one below.

1 of 1 test case failed

Every test case in this scenario has a result.

Pass rate0%0 of 1 gradable
Coverage1 of 1test case with a result
0PassNo test case passed.
1FailMissed at least one thing it was expected to do; the reason and proof are on the row.
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.

Filter across the extracted setFailEvidence
What we sent

“Show documents with total due greater than 150”

Input 1Document (PDF)
Input PDF: baseline receipt with total due
Open the PDF ↗
Input 2Document (PDF)
Input PDF: alternative-label invoice with amount due
Open the PDF ↗
Input 3Document (PDF)
Input PDF: alternative-layout receipt with USD totals
Open the PDF ↗
What the tool returned

I cannot find the answer in the provided document.

Transcribed from Proof 4 · Screenshot

Expected vs. Found
✗Expected: Only the documents whose extracted total is above 150.Found: Chat refused the request, and the product showed only one document at a time; the matching documents were not returned as a set.
Supporting proof
Proof 1Screenshot
Results screenshot: baseline receipt extraction
Open original ↗
Proof 2Screenshot
Results screenshot: alternative-label invoice extraction
Open original ↗
Proof 3Screenshot
Results screenshot: alternative-layout receipt extraction
Open original ↗
Proof 4Screenshot
Chat screenshot: whole-project view, refusal, and scope line
Open original ↗
Proof 5Captured file (JSON)
Chat note: question, answer, and scope line
Text6 lines
Chat tab, file Baseline_receipt (project of 3 receipts)

Question: Show documents with total due greater than 150
Answer: I cannot find the answer in the provided document.

Footer on screen: "Chat works with this file only and isn't available as an API."
Open the JSON file ↗
Why this result

The product was asked to return matching documents from a set, but Chat refused and the view only exposed one document at a time. The extraction was correct, so the failure is in querying across records, not in reading the PDFs.

Also observed on this row

Correct extraction. Each receipt's extracted values matched its PDF, including the two variant layouts.

Tested by Rugved Nichite · evidence dated 27 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)
Model
DPT-3 Pro
Surface
Web app
Set up before the run
Several same-kind documents, each with one comparable extracted value, and a whole-set query that should separate matching documents from non-matching ones.
Extraction schema fields
identifier_number, subtotal, tax, total_due
Schema version
v1
Tested
27 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 →
ScenarioFilter records across documents →
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
30 October 2026Updated — retested
The result is unchanged: 0 pass · 1 fail. The earlier result stays readable. Read the earlier result →

Act on this result

Nothing filed here edits the run or the grade. A challenge opens a review, and a review can produce a new run or a re-grade — which becomes the current result and leaves this one in the history.

This matches what I see

You run the same kind of test against your own setup and get the same behaviour.

Agree →
This does not match

Yours behaves differently. Tell us what you got, with a screenshot if you have one.

Disagree →
Point out an issue

Something here is wrong — a reference value, a transcription, a grade.

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Request a retest

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We have fixed this

Tell us what changed and we schedule a rerun of the failing test case. The old result stays as history.

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/filter-records-across-documents · 0 pass · 1 fail · coverage 1/1 · graded 2026-10-29

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 1 · Document (PDF) ↗application/pdf · 54 KB
Input 2 · Document (PDF) ↗application/pdf · 8 KB
Input 3 · Document (PDF) ↗application/pdf · 7 KB
Proof 1 · Screenshot ↗image/png · 57 KB
Proof 2 · Screenshot ↗image/png · 54 KB
Proof 3 · Screenshot ↗image/png · 57 KB
Proof 4 · Screenshot ↗image/png · 107 KB
Proof 5 · Captured file (JSON) ↗text/plain; charset=utf-8 · 0 KB
File fingerprints (SHA-256)

A fingerprint identifies the exact file used for this result.

Input 1 · Document (PDF)a96bbe1c5b22336e7b54169baf72b85e45eabf1312cd293a1821017b52fa85f7
Input 2 · Document (PDF)4a0b1754da9f52ce35a783d58f8e8a1b5e1353b5b992d5553122886f1cd4a638
Input 3 · Document (PDF)0276d246d69482340b53797f4eaa21831977cede22c8c8f14fc48dbf84757660
Proof 1 · Screenshot4c86ab7961ac0fae48b4168dbca7ec74d017fc9038f44bca6f831275719b798a
Proof 2 · Screenshot308d83c6fd9be86ae36d53bef0fc8fa07da2872e428f71a55d17a0d7d9b3cbd7
Proof 3 · Screenshotb691c0b9a50d1f44b81caa7aae68fec4d294e03784917377b06f27ff8c027b28
Proof 4 · Screenshot2ecbbc90d41623ef14b3c76b248fa550d472cebae5b551226bb2ada11415b996
Proof 5 · Captured file (JSON)33f39741c05cb2c014c690a7f39c3b7d1365ff04a4dba04608513d5cb402ba85