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

Adds meaningful transaction_type labels to extracted rows, classifying the sample 18 Jun records as Withdrawal, Withdrawal, and Deposit instead of leaving the field as raw OCR text.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedLanding AI →
What was measured
Semantic Field Enrichment

Are derived fields — transaction_type, transaction_id, cheque_number, day patterns, ad codes — correctly classified or extracted beyond raw OCR?

decisive for this rankingtransformation

This ranking is not just about copying OCR text; it also depends on whether the tool can correctly infer or classify document-specific fields needed for useful structured output. (3 of 3 judges)

What was given, what came back

Test input: Bank Statement PDF · pdf · group: financial-document-extraction
Input — what we sent
Bank Statement PDF.pdf
Bank Statement PDF

A 4-page bank statement PDF with 51 transactions, balances, rewards, and disclaimer text, used to test schema-driven extraction of dense financial tables and multi-page continuity.

Why this input is hard
  • · Table extraction across 50+ transaction rows
  • · Multi-page continuity with BALANCE FORWARD bridges
  • · Structured metadata vs. free-text transaction descriptions
  • · Numerical accuracy for balances, deposits, withdrawals, and summaries
  • · Nested schema population for account, branch, balances, rewards, and disclaimers
Output — unretouched
image
Provenance
Observation
a2a1f6f5-e278-415c-afcd-a43a2ec3e91b
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
input + output shown
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/a2a1f6f5-e278-415c-afcd-a43a2ec3e91b
Free with attribution.
Same input, same check — 7 other tools
measured on Semantic Field Enrichment
Datalab◐ MixedTransaction typing is inconsistent on merged rows, with one 28 Jun entry labeled Deposit despite showing a 399 withdrawal amount and another 19 Jun merged row labeled Deposit/Withdrawal.Extend AI✓ WorkedIt classifies bank transactions into derived `transaction_type` values such as Withdrawal or Deposit from the description text.LlamaParse✗ FailedFails to derive transaction-level fields, leaving transaction_id and transaction_type empty even for descriptions that encode ATM, UPI, and CRADJ cues.Nanonets✗ FailedIt does not populate the derived transaction_type field, leaving it null across the statement instead of classifying deposits and withdrawals.Reducto✗ FailedLeaves derived transaction metadata incomplete, with transaction_type and transaction_id missing across transaction rows.Retab✓ WorkedDerives transaction_type and transaction_id on transaction rows, classifying one record as UPI with transaction_id 917615251879 and cheque_number left empty.Unstract✓ WorkedClassifies transaction_type correctly across the transaction array, using Deposit and Withdrawal labels rather than raw OCR text.
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