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

The generated JSON is structurally valid but not order-stable: line_items appears before other top-level sections that were defined earlier in the supplied schema.

◐ Mixed🧾 artifact-verifiedoutput onlyTest date not recordedDatalab →

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: Invoice PDF · pdf · group: financial-document-extraction

Input — what we sent

Input not captured

This run recorded no prompt or input file for the test, so we cannot show you what produced the result below. Capture gaps are tracked, not hidden.

A 2-page broadcast advertising invoice PDF with 8 line items, complex time/day fields, large dollar amounts, and compliance text, used to test hierarchical line-item extraction and financial validation.

Why this input is hard

· Nested line-item hierarchy extraction
· Multi-page line-item continuity across a page break
· Large dollar amount precision and total validation
· Parsing time slots, day patterns, and air dates
· Extraction of alphanumeric ad IDs and reference codes
· Structured metadata mapping for advertiser, station, billing, and remit sections
· Political advertising and FCC compliance text recognition

Output — unretouched

Also checked on this input — same tool, 4 other criteria

Extraction Accuracy✓ WorkedExtracts the invoice metadata and monetary values accurately, including invoice 4064621-1, invoice date 10/28/12, gross_total 29750, agency_commission 4462.5, net_amount_due 25287.5, and payment_terms 30 Days.Schema Adherence✓ WorkedReconstructs the invoice into nested JSON objects and arrays that follow the supplied schema, including invoice_metadata and line_items rather than returning generic OCR text.Semantic Field Enrichment✓ WorkedCorrectly extracts derived scheduling fields on line 2, including day_of_week Su, time_slot 9a-10a, air_time 9:38 AM, and ad_id NRCCWI071005.Table & Record Completeness✓ WorkedKeeps all 8 advertising spots as separate line-item records, with the JSON tree showing indices 0 through 7 and no adjacent-row merging.

Provenance

Observation: df4d4c76-2422-413b-9d07-a3489b53cb50
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: output only
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: "financial-document-extraction"
})

MCP · mcp.aidemos.com/api/mcp

JSON · ai.aidemos.com/v1/observations/df4d4c76-2422-413b-9d07-a3489b53cb50

Free with attribution.

Same input, same check — 2 other tools

measured on Structural Clean Output

Extend AI✗ FailedIt reorders several schema sections relative to the supplied schema, so the output is not consistently in the authored field order.Retab◐ MixedDoes not preserve the schema-defined property order, so consumers that rely on key order need an extra formatting pass.

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