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

It does not preserve the authored top-level schema sequence, so consumers expecting the original field order need a transformation step.

✗ Failed🧾 artifact-verifiedinput + output shownTest date not recordedExtend AI →
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
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
image
Also checked on this input — same tool, 5 other criteria
Extraction Accuracy⚠ StruggledIts bank summary aggregation is off: the report says `summary.total_transactions` is 49, while the expected count is 40 after excluding Balance Forward, tax, and charge entries.Schema Adherence✓ WorkedThe bank output is rebuilt as nested JSON rather than raw OCR, with branch, account, rewards, balances, summary, and metadata objects populated under the requested statement root.Semantic Field Enrichment✗ FailedIt leaves derived `transaction_id` values as `null` even when reference identifiers are present in the description, so identifier extraction does not generalize.Semantic Field Enrichment✓ WorkedIt classifies bank transactions into derived `transaction_type` values such as Withdrawal or Deposit from the description text.Table & Record Completeness✓ WorkedThe extractor keeps transaction rows as separate records, and the report says it captured all 51 transactions without merging adjacent rows.
Provenance
Observation
4d3907e7-7988-4063-b5ba-89a42de6eab6
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: "extend-ai",
  scenario: "financial-document-extraction"
})
MCP · mcp.aidemos.com/api/mcp
JSON · ai.aidemos.com/v1/observations/4d3907e7-7988-4063-b5ba-89a42de6eab6
Free with attribution.
Same input, same check — 1 other tool
measured on Structural Clean Output
Reducto◐ 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.
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