Unstract
It handled the four-page statement well: the header fields, balances, transaction rows, and transaction types were captured correctly, but the summary transaction count was wrong.
unstract-unstract-bank-statement-output-f0bd422ffa9e.json
Best AI Tools for Extracting Structured Data from PDFs and Business Documents
If you need to turn invoices and bank statements into clean, nested JSON for review, export, or downstream automation, the key questions are schema fidelity, row completeness, numeric accuracy, and auditability. We tested eight document-intelligence platforms on a multi-page bank statement and a two-page broadcast invoice with matching custom schemas to see which ones reconstructed fields, tables, and summary values most reliably.
Tested June 20268 tools4 decisive checks128 findings11 min read
Our pick
Unstract
$499/mo4.54 of 4 checks
The available evidence shows good bank-statement structure and correct transaction-type classification, but the summary count was wrong and the invoice-side evidence in the supplied slice is incomplete.
Catch
It was very accurate on the main header and money fields, but one bank summary field was wrong and the invoice carried a repeatable identifier spacing error, so this is strong but not perfect.
Pick something else if…
The scoreboard
We rank on the 4 checks that decide whether a tool does this job: Extraction Accuracy, Schema Adherence, Semantic Field Enrichment, Table & Record Completeness. A check only carries a score when we recorded a finding for it, and a tool has to be measured on all of them to take the top spot. We also checked Structural Clean Output — compared for you, but not part of the ranking.
Tool4 decisive checksScoreWhere it lands
Columns, left to right: Extraction Accuracy · Schema Adherence · Semantic Field Enrichment · Table & Record Completeness
Compare
Pick the tools you care about, then compare what they returned or how they scored.
Tools8 of 8 selected
The output#2
Retab
It produced a well-structured bank-statement JSON with accurate account details, balances, disclaimers, and transaction typing, but the transaction count was overreported, so the result is strong with one notable completeness issue.
7335c86f94a340cf8443685a9fca91ce.png
The output#3
Landing AI
It handled the bank statement well overall: the JSON structure was right, the balances and other key values were accurate, and all 51 transactions came through. The main drawback was that it replaced the source transaction IDs with simple sequence numbers.
research-media-landing-ai-extracted-bank-statement-c4910d90b9f3.png
The output#4
Extend AI
It returned a structured bank-statement JSON with branch, account, rewards, balances, summary, and transaction rows, but the transaction total was off, identifier fields were incomplete, and the section order did not match the authored layout.
research-media-extend-ai-bank-statement-summary-a4a38bf7978f.png
The output#5
LlamaParse
It produced a clean nested bank-statement JSON, but the transaction layer had real problems: some dates were missing, transaction IDs and types stayed blank, and the transaction counts did not agree.
research-media-llamaparse-bank-statement-extracted-deta-1c069bb9dc7a.png
The output#6
Datalab
It produced a schema-shaped bank-statement JSON with correct document details, but the transaction section was shaky: rows were merged or split badly, one row landed on the wrong date, transaction IDs stayed blank, and some types were wrong.
71f9443de6a54eb6b5500ddc4edef9c5.png
Nanonets
It took the PDF directly and returned schema-shaped JSON/CSV, but the transaction table was badly damaged: several dates were blank, some rows were merged, transaction_type stayed null, and the total count was wrong.
research-media-docstrange-nanonets-bank-statement-outpu-e65996ec6ef1.json
The output#8
Reducto
It produced nested JSON and source-linked fields, but the bank run had a wrong transaction total, extra or missing transaction rows, and missing derived transaction fields, so it was not ready for reconciliation without cleanup.
research-media-reducto-bank-statement-summary-341218e79b80.png
The evidence
All 5 recorded checks per tool. Open a tool to inspect every finding.
Why this score
It was very accurate on the main header and money fields, but one bank summary field was wrong and the invoice carried a repeatable identifier spacing error, so this is strong but not perfect.
When we tried: Bank Statement PDF
Copies header and balance values exactly, including the account holder block, account number, statement date, and the 114,453.65 opening / 116,149.46 closing balances.
permalink to this finding →In the input
What came back
When we tried: Invoice PDF
Introduces a stray OCR-style space into every Ad-ID, so the identifier text is not preserved exactly as printed.
permalink to this finding →In the input
What came back
When we tried: Invoice PDF
Matches the printed financial summary exactly, including 8 aired spots, $29,750.00 gross, $4,462.50 agency commission, $25,287.50 net due, and 30-day payment terms.
permalink to this finding →In the input
What came back
When we tried: Bank Statement PDF
Derives an incorrect summary transaction count, reporting 43 when the statement actually contains 51 rows.
permalink to this finding →In the input
What came back
Across all tests
It mostly preserved the printed financial and header values exactly, but it miscounted rows in one bank statement and introduced stray OCR-style spaces into every Ad-ID.
permalink to this finding →Final Take
Unstract is the page’s winner and the safest overall pick here: it combines top Schema Adherence and Table & Record Completeness with solid Extraction Accuracy and Semantic Field Enrichment, which is exactly what the ranking policy rewards. The main caveat is that a few derived summary and pattern fields need a second look, so it is not flawless on enrichment detail. Retab is the closest alternative if you care more about semantic enrichment than perfect row completeness, since it scores higher there but gives up a point on Table & Record Completeness and still has some cleanup issues in totals, labels, and key order. Landing AI is another strong option when preserving document structure and full tables matters, but its semantic enrichment is weaker. Below that, Extend AI and LlamaParse keep schema shape reasonably well but lose ground on enrichment, completeness, or structural cleanliness, while Datalab and Nanonets are more fragile on transaction rows. Reducto is useful for nested extraction and citations, but its schema and completeness trail the top tools.
Tested as of June 2026 · Will be re-verified monthly
Similar Tools
The tools we tested for this use case — each card opens its full tested review.
Unstract
Prompt-driven schema extraction for invoices and bank statements, with strong row-level capture and a few derived-field caveats.
AI TOOLRetab
Schema-first PDF extraction for finance documents that returns nested JSON with minimal setup.
AI TOOLLanding AI
Schema-guided PDF extraction for bank statements and invoices, with strong row capture and a few identifier QA caveats.
AI TOOLExtend AI
Schema-driven extraction for finance PDFs that reconstructs nested JSON well, but still needs review for ordering, IDs, and a few scalar values.
AI TOOLLlamaParse
Versatile PDF parsing for Markdown and structured JSON, with strong recovery but some fidelity drift
AI TOOLDatalab
Schema-paste extraction for bank statements and invoices, with cited JSON output and fast-mode recovery when schemas get large.
AI TOOLNanonets
Schema-first PDF extraction that produces usable exports, but dense table rows still need review.
AI TOOLReducto
Structured PDF extraction with nested JSON, citations, and row-preserving tables; invoices worked cleanly, while bank statements still need review.
AI TOOL Built by FutureSmart AI — the team behind AI Demos
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