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

The tool infers transaction direction from free text, classifying an ATM entry as `Withdrawal` and populating `withdrawal_amount: 1000`.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedExtend AI →

What was measured

Semantic Field Enrichment

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

decisive for this rankingtransformation

For this ranking, correctly identifying derived business fields is part of producing usable structured data, not just a nice extra. (3 of 3 judges)

What was given, what came back

Test input: Bank Statement PDF · pdf · group: business-document-extraction

Input — what we sent

Bank Statement PDF.pdf

Bank Statement PDF

A four-page bank statement PDF with dense transaction tables, balance-forward bridges, account metadata, rewards data, and disclaimer text. It was used to stress schema-driven extraction, multi-page continuity, row completeness, and financial numerical accuracy.

Why this input is hard

· Table extraction across 50+ transaction rows
· Multi-page continuity with balance-forward bridges
· Parsing structured account metadata alongside unstructured transaction descriptions
· Numerical accuracy for deposits, withdrawals, running balances, and summaries
· Extraction of nested rewards and disclaimer sections

Output — unretouched

Also checked on this input — same tool, 3 other criteria

Extraction Accuracy✗ FailedThe summary aggregation is incorrect: `total_transactions` is 49 in the output, while the report says the expected count after exclusions is 40.Schema Adherence✓ WorkedThe tool reconstructs the bank-statement hierarchy into nested JSON with branch, account, rewards, metadata, balances, summary, and transaction-related objects present in the requested layout.Structural Clean Output✗ FailedThe output reorders top-level schema objects instead of preserving the declared sequence, so consumers that depend on the original order need an extra transformation step.

Provenance

Observation: ca67652c-8a96-456d-92ef-c2817c381589
Evidence run: a061b9e7-a9c5-443d-a171-b296aaf51b8c
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: "extend-ai",
  scenario: "business-document-extraction"
})

MCP · mcp.aidemos.com/api/mcp

JSON · ai.aidemos.com/v1/observations/ca67652c-8a96-456d-92ef-c2817c381589

Free with attribution.

Same input, same check — 7 other tools

measured on Semantic Field Enrichment

Datalab◐ MixedClassifies bank-statement transaction_type inconsistently on merged rows: the 28 Jun record is labeled Deposit even though the visible row shows only a 399 withdrawal amount, and another merged row is labeled Deposit/Withdrawal.Landing AI✓ WorkedClassifies extracted bank rows with semantic transaction_type labels such as Withdrawal and Deposit, showing derived type tagging beyond raw transaction text.LlamaParse✗ FailedThe tool does not reliably enrich bank transactions with derived identifiers or types: transaction_id and transaction_type are left empty across extracted records even when the descriptions contain transaction clues.Nanonets✗ FailedDoes not derive transaction_type on the bank statement at all; the report states that transaction_type is null for every extracted transaction.Reducto✗ FailedDoes not consistently enrich transaction rows with schema-level fields such as transaction_type and transaction_id; the visible extracted rows remain partially populated and omit those fields.Retab✓ WorkedDerives transaction_type and transaction_id from the transaction narration, labeling the sample record as UPI with transaction_id 917615251879 instead of leaving only raw description text.Unstract✓ WorkedCorrectly enriches bank rows with transaction_type classification, with the report stating Withdrawal/Deposit was assigned correctly on all 51 transaction rows.

This evidence is published in

Best AI Tools for Extracting Structured Data from Business Documents →docsumo →

Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com