productivity

Retab

Schema-first PDF extraction for finance documents that returns nested JSON with minimal setup.

Direct PDF uploadCustom JSON schemaBank statement rowsInvoice line items

Strong fit for schema-based document extraction

Retab accepted direct PDF uploads, let the tester paste custom JSON schemas, and returned copyable nested JSON for both a bank statement and an invoice with no manual intervention. It was especially strong at reconstructing line items and typed financial fields, but the bank summary count was off, one invoice field kept its source label text, and schema key order was not preserved.

Walkthrough of Retab extracting a bank statement into structured JSON with a pasted schema.

In-Depth Review

Our detailed analysis of Retab — features, performance, and real-world testing.

AI Demos Team

Expert Reviewer

Verified Review

Feature-by-Feature Breakdown

Schema-Driven PDF Extraction

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Test Summary

Feature tested: Schema-Driven PDF Extraction

Result: Passed

Retab accepts direct PDF uploads and a custom JSON schema, then returns nested structured JSON instead of flat OCR text. In the bank statement and invoice tests, it preserved the expected section hierarchy in the extracted output.

pdf

Bank Statement PDF.pdf

Source bank statement used in the extraction test.

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json

retab-bank-statement-output.json

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Structured bank statement JSON with nested statement data, transactions, summary, rewards, and disclaimers; the summary count later needed validation.

pdf

Invoice PDF.pdf

Source invoice used in the extraction test.

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json

retab-invoice-output.json

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Structured invoice JSON with nested invoice metadata, advertiser, billing, line items, and summary fields.

Bottom Line

This was the core strength of the tool: zero-friction PDF intake plus schema-matched JSON output on both financial document types.

Row-Level Table Reconstruction

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Test Summary

Feature tested: Row-Level Table Reconstruction

Result: Passed

Retab turns dense document tables into structured row arrays. On the bank statement it produced a transactions array, and on the invoice it extracted eight line items with scheduling, rate, and campaign identifier fields intact.

pdf

Bank Statement PDF.pdf

Source bank statement used to test transaction-row reconstruction.

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json

retab-bank-statement-output.json

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The bank statement output included a transactions array with 51 extracted rows.

pdf

Invoice PDF.pdf

Source invoice used to test line-item reconstruction.

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file

retab-invoice-output.json

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The JSON tree view shows the line_items array expanded into numbered entries, matching the report’s eight extracted line items.

Bottom Line

The row extraction itself was complete in both samples, but the bank statement’s summary aggregation did not reconcile cleanly against the expected transaction count.

Financial Field Normalization and Classification

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Test Summary

Feature tested: Financial Field Normalization and Classification

Result: Passed

Retab normalizes finance-specific fields into machine-readable values such as dates, amounts, totals, payment terms, and transaction types. In the bank statement it classified a row as UPI, and in the invoice summary it surfaced gross total, commission, and net amount due.

pdf

Bank Statement PDF.pdf

Source bank statement used to test transaction-type classification.

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image

Output artifact for "Financial Field Normalization and Classification" test: The extracted transaction row was classified as transaction_type UPI and preserved the debit amount, value date, and running balance fields., retab-bank-statement-extracted-transaction-type.png

The extracted transaction row was classified as transaction_type UPI and preserved the debit amount, value date, and running balance fields.

pdf

Invoice PDF.pdf

Source invoice used to test summary-field normalization.

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image

Output artifact for "Financial Field Normalization and Classification" test: The invoice summary returned agency_commission, aired_spots, gross_total, net_amount_due, and payment_terms as structured fields., retab-invoice-summary.png

The invoice summary returned agency_commission, aired_spots, gross_total, net_amount_due, and payment_terms as structured fields.

Bottom Line

Normalization was solid for the core numeric and date fields, but validation is still needed for the bank transaction-count summary and the invoice payment_terms cleanup.

Reported pricing

Credit-based plans were listed in the research notes.

Free

$0/month

1,000 credits included

Starter

$300/month

30,000 credits included

Custom

Custom Pricing

Custom credit allocation

Pricing was reported from the evaluation and was not independently tested.

✓ Use This If

●You want to paste a custom JSON schema and extract nested structured data from PDFs without coding.

●You need bank statement or invoice data in machine-readable JSON for downstream reconciliation.

●You can validate a few finance fields after export to catch summary or label-cleanup quirks.

✕ Skip This If

●You need a built-in review or correction UI before export.

●You need exact bank-statement summary counts with no follow-up validation.

●You need schema key order preserved exactly as authored.

productivitypdf-toolstext

Yes. In the report, direct PDF upload worked without preprocessing for both the bank statement and the invoice.

Yes. The schema was pasted into the extraction node and the workflow ran without validation errors or setup friction.

It returned structured JSON with nested objects and arrays, not generic OCR text summaries.

The bank statement extracted 51 transactions, and the invoice extracted 8 line items.

Yes. The bank statement summary reported 43 total transactions even though the expected count was 40 after exclusions. The invoice payment_terms field kept the source label text, and the output did not preserve the exact schema key order.

The report listed Free at $0/month with 1,000 credits, Starter at $300/month with 30,000 credits, and Custom pricing with custom credit allocation.