Evidence · first-party tested/Best AI Tools for Extracting Structured Data from PDFs and Business Documents

Leaves several station-level fields null even though the station is identified, with call_letters "KMSP" populated but address, city, state, postal_code, main_phone, and billing_phone all null.

◐ Mixed🧾 artifact-verifiedoutput onlyTest date not recordedLanding AI →
What was measured
Extraction Accuracy

Are field values correct, complete, and free of OCR or parsing errors? Includes numerical precision on financial fields.

decisive for this rankingtransformation

The whole point is to pull the right values from documents; wrong or incomplete field values mean the tool failed at the job. (3 of 3 judges)

What was given, what came back

Test input: Invoice PDF · pdf · group: financial-document-extraction
Input — what we sent
Input not captured
This run recorded no prompt or input file for the test, so we cannot show you what produced the result below. Capture gaps are tracked, not hidden.

A 2-page broadcast advertising invoice PDF with 8 line items, complex time/day fields, large dollar amounts, and compliance text, used to test hierarchical line-item extraction and financial validation.

Why this input is hard
  • · Nested line-item hierarchy extraction
  • · Multi-page line-item continuity across a page break
  • · Large dollar amount precision and total validation
  • · Parsing time slots, day patterns, and air dates
  • · Extraction of alphanumeric ad IDs and reference codes
  • · Structured metadata mapping for advertiser, station, billing, and remit sections
  • · Political advertising and FCC compliance text recognition
Output — unretouched
image
Provenance
Observation
184fde87-39d4-4fe1-928e-d94082d4127f
Evidence run
ec4d736d-95f9-4c88-884c-e280435f7b7b
Study
Extract and query structured data from documents using natural language
Research task
86b9y25e5
Tested at
not recorded
Source
first-party
Evidence state
verified
Proof shown
output only
Cost / latency
not captured
Repeat run
not captured
Tester
not captured

The last three rows are honest blanks, not placeholders — our capture has no field for them yet.

Query this
get_evidence({
  tool: "landing-ai",
  scenario: "financial-document-extraction"
})
MCP · mcp.aidemos.com/api/mcp
JSON · ai.aidemos.com/v1/observations/184fde87-39d4-4fe1-928e-d94082d4127f
Free with attribution.
Same input, same check — 7 other tools
measured on Extraction Accuracy
Datalab✓ WorkedExtracts the invoice metadata and monetary values accurately, including invoice 4064621-1, invoice date 10/28/12, gross_total 29750, agency_commission 4462.5, net_amount_due 25287.5, and payment_terms 30 Days.Extend AI✓ WorkedIts invoice totals match the source document, including 8 aired spots, gross_total 29750, agency_commission 4462.5, and net_amount_due 25287.5.LlamaParse✓ WorkedMatches the source invoice totals and payment terms: aired_spots 8, gross_total 29750, agency_commission 4462.5, net_amount_due 25287.5, and payment_terms "30 Days".Nanonets◐ MixedIt concatenates program-description text without a separator, so labels such as "Political Issue Rates" are appended directly onto the show title in fields like "Fox 9 AM News at 4:30amPolitical Issue Rates".Reducto✓ WorkedExtracts the invoice summary amounts accurately; the report says the financial totals and aired_spots match the source document.Retab◐ MixedRetains the source label in payment_terms, returning Payment Terms 30 Days instead of only the requested value, so a cleanup step is needed.Unstract⚠ StruggledIntroduces a stray OCR-style space into every Ad-ID, so the identifier text is not preserved exactly as printed.
This evidence is published in
Best AI Tools for Extracting Structured Data from PDFs and Business Documents →unstract →
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com