The 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.
What was measured
Schema Adherence
Does the output follow the supplied JSON schema hierarchy exactly, with correct nesting, field names, and data types?
decisive for this rankingtransformation
If the tool does not follow the supplied JSON schema exactly, the extracted data cannot be reliably used for structured querying or downstream automation. (3 of 3 judges)
What was given, what came back
Test input: Bank Statement PDF · pdf · group: business-document-extraction
Input — what we sent
Research media bank statement 2 jul.png
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
Loading file...
Also checked on this input — same tool, 4 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.Semantic Field Enrichment✓ WorkedThe tool infers transaction direction from free text, classifying an ATM entry as `Withdrawal` and populating `withdrawal_amount: 1000`.Semantic Field Enrichment✗ FailedIt leaves `transaction_id` null even when the description contains embedded reference numbers, so identifier extraction is not recovered from the transaction text.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
- b1ccd11a-19d7-4af9-93c2-1ba1086296e5
- 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
Free with attribution.
Same input, same check — 7 other tools
measured on Schema Adherence
Datalab✓ 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.Landing AI✓ WorkedReconstructs the supplied bank-statement hierarchy instead of flat OCR, with nested statement.metadata, account_holder.address, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers objects present in the extracted JSON flow.LlamaParse✓ WorkedThe bank-statement output follows the requested nested schema closely, reconstructing metadata, account_holder, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers as structured objects rather than flat OCR text.Nanonets✓ WorkedPreserves the supplied bank-statement hierarchy in structured JSON, with nested statement.metadata, account_holder, account, balances, transactions, summary, rewards, and disclaimers objects instead of flattening the document into OCR text.Reducto✓ WorkedPreserves a nested, schema-shaped JSON structure for the bank statement instead of flattening the document into raw OCR, including top-level objects like metadata, account_holder, account, branch, statement_period, transactions, and summary.Retab✓ WorkedReconstructs the supplied bank-statement schema into a nested JSON object with separate statement.metadata, account_holder, account, branch, balances, transactions, summary, rewards, and disclaimers sections instead of flattening the document into OCR text.Unstract✓ WorkedFollows the requested nested bank-statement schema instead of flattening the document, populating structured objects such as metadata, account_holder, account, branch, balances, transactions, and summary.
This evidence is published in
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com