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

Extracts key statement values as typed fields, including account holder MR SEENIVASAN, account number 42710540422, and opening and closing balances 114453.65 and 116149.46.

✓ Workedinput + output shownTest date not recordedRetab →
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: 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
Download video: 84e1a6224746446ab5abbd3712c635b8.mp4?v=1
Provenance
Observation
bb804c81-06cb-4d05-82a9-804c000f4321
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
observed
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: "retab",
  scenario: "financial-document-extraction"
})
MCP · mcp.aidemos.com/api/mcp
JSON · ai.aidemos.com/v1/observations/bb804c81-06cb-4d05-82a9-804c000f4321
Free with attribution.
Same input, same check — 7 other tools
measured on Extraction Accuracy
Datalab✓ WorkedExtracts the visible document-level values correctly, including State Bank of India, 16 Jul 2019, INR, account number 42710540422, account type SMART BANKING SAVINGS ACCOUNT, branch Rajaji Salai, MICR 600036005, IFSC SCBL0036078, and phone 25349005.Extend AI⚠ StruggledIts bank summary aggregation is off: the report says `summary.total_transactions` is 49, while the expected count is 40 after excluding Balance Forward, tax, and charge entries.Landing AI✓ WorkedExtracts the statement’s top-level values with the correct content and precision, including bank_name "Standard Chartered", statement_date "16 Jul 2019", currency "INR", account number "42710540422", opening balance 114453.65, and closing balance 116149.46.LlamaParse⚠ StruggledLeaves some transaction value_date fields blank even where the statement shows dates in that column, so transaction metadata is incomplete.Nanonets✓ WorkedIt accurately fills core statement metadata and balances, including account number 42710540422, opening balance 114453.65, and closing balance 116149.46.Reducto◐ MixedGets some bank values right but misstates a core aggregate: summary.total_transactions is reported as 70 even though the report says the correct count is 40.Unstract✗ FailedDerives an incorrect summary transaction count, reporting 43 when the statement actually contains 51 rows.
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