Leaves the derived bank-statement transaction_id field null, including on the 18 Jun ATM withdrawal that visibly contains the embedded ID 916910098754.
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
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, 4 other criteria
Extraction Accuracy⚠ StruggledMisplaces a bank-statement transaction across dates: a row that belongs to 18 Jun is attached to the 19 Jun record after merging, so date association is unreliable.Schema Adherence✓ WorkedCan emit the bank-statement extraction as nested schema-shaped JSON, with separate metadata, account_holder/account, branch, transactions, summary, rewards, and disclaimers objects instead of flat OCR text.Table & Record Completeness⚠ StruggledMerges adjacent bank-statement rows into one record; the 21 Jun crop shows two separate transactions, but the extracted description collapses them into one long UPI/merchant string.Table & Record Completeness✗ FailedOver-segments the bank-statement table: the report says 54 transaction records were extracted where 51 were expected, a +3 overcount caused by continuation rows and split records.
Provenance
- Observation
- f7dda2d7-aecd-4aa9-b323-a15764652e44
- 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: "datalab",
scenario: "business-document-extraction"
})MCP · mcp.aidemos.com/api/mcp
Free with attribution.
Same input, same check — 7 other tools
measured on Semantic Field Enrichment
Extend AI✓ WorkedThe tool infers transaction direction from free text, classifying an ATM entry as `Withdrawal` and populating `withdrawal_amount: 1000`.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
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com