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

Exports 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.

◐ Mixed🧾 artifact-verifiedinput + output shownTest date not recordedReducto →
What was measured
Structural Clean Output

Is the JSON directly consumable by a downstream AI pipeline or system without requiring a structural transformation layer?

context, not decisivetransformation

Directly consumable JSON is valuable for workflow convenience, but it is a delivery/integration concern rather than the core measure of extraction quality itself. (3 of 3 judges)

What was given, what came back

Test input: Bank Statement PDF · pdf · group: financial-document-extraction
Input — what we sent
Input file 1 — as supplied
Research media bank statement 2 jul.png
Research media bank statement 2 jul.png
Input file 2 — as supplied
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
Output 1
Output 1
Output 2
research-media-reducto-output-1-bf8098db170a.json
Loading file...
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.Semantic Field Enrichment✗ FailedLeaves derived transaction metadata incomplete, with transaction_type and transaction_id missing across transaction rows.Table & Record Completeness✗ FailedDoes not preserve the repeating transaction table faithfully, returning 53 transaction records for a statement that contains 51 transactions.
Provenance
Observation
2d9d828a-e002-45f9-9dfe-93aef402a5de
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
JSON · ai.aidemos.com/v1/observations/2d9d828a-e002-45f9-9dfe-93aef402a5de
Free with attribution.
Same input, same check — 1 other tool
measured on Structural Clean Output
Extend AI✗ FailedIt does not preserve the authored top-level schema sequence, so consumers expecting the original field order need a transformation step.
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