Evidence · first-party tested/Best AI Tools to Convert Complex PDFs into Clean Markdown with an API

Reconstructs a multilevel segment-results table with grouped previous/present quarter headers, year-over-year change columns, six segment rows, and a total row.

✓ Worked🧾 artifact-verifiedinput + output shownTest date not recordedTensorlake →
What was measured
Table Preservation

Preserves complex table structures, including rows, columns, multi-row headers, and merged-cell relationships in markdown.

decisive for this rankingtransformation

Accurate Markdown conversion of complex PDFs depends on keeping table structure intact, not flattening it into plain text. (3 of 3 judges)

What was given, what came back

Test input: Financial Report - Table Heavy · pdf · group: financial-report-table-heavy
Input — what we sent
Input file 1 — as supplied
Input file 1 — as supplied
Input file 2 — as supplied
doc.pdf
Financial Report - Table Heavy

A table-heavy corporate financial report used to test extraction of dense, hierarchical financial statements with grouped columns, multi-row headers, segment-reporting tables, and narrative disclosures.

Why this input is hard
  • · Multi-page financial statement extraction
  • · Hierarchical table reconstruction
  • · Grouped columns and multi-row headers
  • · Reading order in a report with mixed narrative and tables
  • · Document structure retention
  • · Markdown usability
Output — unretouched
image
Provenance
Observation
e86e674e-4666-4abc-a7fb-84d1f4bf1ed7
Evidence run
6e3160de-fe46-4b45-b071-72560b5c5d0e
Study
Convert a Complex PDF into Clean Markdown with an API
Research task
86b9h7t37
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: "tensorlake",
  scenario: "financial-report-table-heavy"
})
MCP · mcp.aidemos.com/api/mcp
JSON · ai.aidemos.com/v1/observations/e86e674e-4666-4abc-a7fb-84d1f4bf1ed7
Free with attribution.
Same input, same check — 8 other tools
measured on Table Preservation
Adobe API✗ FailedFlattens a dual-header financial table, losing the distinct row-header versus column-header roles in the extracted version.Extend AI◐ MixedKeeps the body rows aligned in a multilevel table, but fails to split compound header cells cleanly, so header roles are partially flattened.Landing AI✓ WorkedReconstructs a multi-column segment comparison table with previous-year, present-year, and year-over-year change columns aligned across the rows.LlamaParse✓ WorkedPreserves a multi-level segment-results table with grouped headers and year-over-year columns, keeping the hierarchy legible in the extracted output.Mistral AI✓ WorkedThe multilevel segment table is preserved with its hierarchical headers intact, maintaining the relationships between the grouped columns and their values.Nutrient.io✗ FailedLoses multi-level header organization in hierarchical tables, so parent-child column relationships are not reconstructed in the markdown output.Reducto✓ WorkedPreserves the balance-sheet hierarchy and values, including Assets > Current assets > Cash and deposits, and also reconstructs the later 9-column segment table with all values correct.Upstage AI✗ FailedMisaligns column headers with data regions in dense financial tables, producing a structurally inconsistent balance-sheet extraction rather than a clean header-to-row mapping.
This evidence is published in
Best AI Tools to Convert Complex PDFs into Clean Markdown with an API →
Real inputs and real outputs, no retouching · every cell queryable via API & MCP · aidemos.com