Evidence · first-party tested/Best AI Tools for Extracting Structured Data from PDFs and Business Documents
Leaves derived transaction metadata incomplete, with transaction_type and transaction_id missing across transaction rows.
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
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
Also checked on this input — same tool, 5 other criteria
Extraction Accuracy◐ 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.Extraction Accuracy✓ WorkedThe tool correctly extracts the bank statement currency as INR and includes citation metadata for that field.Schema Adherence✓ WorkedReconstructs the bank statement into a nested JSON structure aligned to the requested schema, with document metadata, account, branch, statement period, transactions, summary, and rewards-style sections instead of flat OCR text.Structural Clean Output◐ MixedExports JSON directly, but the bank workflow is not cleanly consumable end-to-end because the report says the result still needs reconciliation and correction before use.Table & Record Completeness✗ FailedDoes not preserve the repeating transaction table faithfully, returning 53 transaction records for a statement that contains 51 transactions.
Provenance
- Observation
- e2f47c60-3f78-4b14-aa96-2e76d8e2483a
- 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: "reducto",
scenario: "financial-document-extraction"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 7 other tools
measured on Semantic Field Enrichment
Datalab✗ FailedDoes not populate schema-derived transaction identifiers at all: the 18 Jun withdrawal keeps transaction_id = null even though the identifier is visible in the source row.Extend AI✓ WorkedIt classifies bank transactions into derived `transaction_type` values such as Withdrawal or Deposit from the description text.Landing AI✓ WorkedAdds 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.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.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
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com