--- title: "Datalab" type: "AI Tool" url: "https://aidemos.com/tools/datalab" description: "We pasted invoice and bank-statement PDFs into Datalab and got cited nested JSON; bank rows merged, and Fast mode still hit schema-size errors." category: "productivity" website: "https://www.datalab.to/?via=aidemos" published: "2026-07-01T10:51:09.763857+00:00" updated: "2026-07-18T11:52:58.222921+00:00" --- # Datalab Schema-paste extraction for bank statements and invoices, with cited JSON output and fast-mode recovery when schemas get large. `Schema paste` · `Cited JSON` · `Bank statements` · `Invoice line items` **Website:** [Visit Datalab](https://www.datalab.to/?via=aidemos) ## Cross-input findings Invoice extraction was the cleaner path; bank statements needed more review. - **1** Invoice PDF — Fast-mode extraction produced eight line items, metadata, and summary totals in a clean schema-shaped output. - **2** Bank Statement PDF — Nested JSON and citations were good, but 54 transactions, null IDs, merged rows, and misclassifications meant the result needed human review. > **Strong structure, but bank-statement transactions need review** > > Datalab handled schema-guided extraction well: both PDFs came back as nested JSON, and the citations made the results easy to audit. The invoice path looked clean, with eight line items and summary totals extracted in a review-friendly structure. The bank-statement path was much rougher: rows were merged, transaction IDs stayed null, the count was inflated, and some rows were misclassified or misdated. Balanced mode also hit a schema-size error, but Fast mode recovered successfully, so the workflow is usable if a human can validate the financial rows before downstream use. ## Demo Recording [Video: Datalab demo recording](https://d3epheqghktydj.cloudfront.net/datalab-datalab-bank-statement-tool-demo-c1a51e72f3c2.mp4) *Video — Screen recording of the schema-paste extraction flow and mode switching used in the bank-statement test.* ## Feature-by-Feature Breakdown ### Schema-Guided Nested Extraction **Verdict:** Strong overall, with cleaner invoice output than bank-statement rows. Datalab can take a pasted JSON schema and extract business PDFs into nested structured output. The bank statement run reconstructed metadata, account, branch, balances, transactions, summary, and disclaimers, and the same schema-shaped approach was used on the invoice. **Input:** > **Pdf** **Output:** > **Json** **Input:** > **Pdf** **Output:** > **Json** **Bottom line:** Good schema adherence across both documents, but the bank statement still needed transaction-level cleanup before downstream use. ### Field Citations and Provenance **Verdict:** A clear strength for audit and review workflows. Datalab can attach extracted values back to source locations such as pages, tables, and text spans. In the bank statement and invoice runs, citations were present on metadata, account, transaction, line-item, totals, and disclaimer fields. **Input:** > **Pdf** **Output:** > **Image** **Input:** > **Pdf** **Output:** > **Image** **Bottom line:** Traceability is one of Datalab's strongest capabilities, but provenance does not fix extraction mistakes on messy transaction rows. ### Table and Transaction Row Reconstruction **Verdict:** Excellent on invoice rows; inconsistent on bank-statement transactions. Datalab can turn tables and repeating transaction rows into structured arrays. The invoice produced eight clean line items, while the bank statement showed mixed row segmentation quality on several transaction entries. **Input:** > **Image** **Output:** > **Image** **Input:** > **Image** **Output:** > **Image** **Input:** > **Image** **Output:** > **Image** **Input:** > **Pdf** **Output:** > **Image** **Input:** > **Pdf** **Output:** > **Image** **Bottom line:** Invoice rows were reconstructed cleanly, but bank-statement segmentation was not reliable enough to trust without review. ## Reported plans | Plan | Price | Notes | | --- | --- | --- | | Free | Pay-as-you-go | Includes a free monthly API allowance, followed by usage-based pricing. | | Team | $400/month | Higher rate limits, production-ready access, and enhanced support. | | Enterprise | Custom pricing | Hosted or self-managed deployments with custom infrastructure, commercial terms, and dedicated support. | *Pricing was stated in the task report; no plan limits or quotas were verified in this test.* ## Is It Right For You? **Use it if** - You want to paste a JSON schema and get nested JSON back without writing code. - You need extracted values to carry page and table citations for audit and review. - You can review bank-statement rows before downstream use, especially when the document is dense. **Skip it if** - You need exact bank-statement row boundaries or transaction IDs without manual cleanup. - You need the output to preserve your schema field order exactly as written. - You need natural-language querying over the extracted data, because this research only demonstrated extraction and review. ## Classification - **Category:** productivity - **Subcategory:** pdf-tools - **Type:** text ## Frequently Asked Questions **Q: What document types did Datalab handle in this test?** It handled a bank statement PDF and an invoice PDF. **Q: Did Datalab support custom JSON schemas?** Yes. The report says JSON schema paste worked successfully, and the extracted output followed the uploaded schema structure. **Q: Did the extracted fields include source citations?** Yes. The bank-statement and invoice outputs both included citation paths back to page, table, or text locations. **Q: How well did it extract bank-statement transactions?** The bank-statement output was usable but imperfect: it returned 54 transactions instead of the expected 51, merged some rows, left transaction_id values null, and misclassified at least one row. **Q: How well did it extract invoice line items?** The invoice extraction was much cleaner. The report says all eight line items were extracted as individual structured records, and the summary values matched the source. **Q: Did the output preserve the exact schema order?** No. The report says the invoice output did not preserve the supplied schema order exactly, even though the extracted values were correct. **Q: What happened in Balanced mode?** Balanced mode returned a schema-size error on the bank-statement run and recommended switching to Fast mode. Fast mode then completed successfully. **Q: What pricing was reported?** The report listed three plans: Free at pay-as-you-go pricing with a free monthly API allowance, Team at $400/month, and Enterprise with custom pricing. ## Similar Tools AI tools similar to Datalab: - [Nanonets](https://aidemos.com/tools/nanonets) — Schema-first PDF extraction that produces usable exports, but dense table rows still need review. - [Retab](https://aidemos.com/tools/retab) — Schema-first PDF extraction for finance documents that returns nested JSON with minimal setup. - [Landing AI](https://aidemos.com/tools/landing-ai) — A capable PDF-to-markdown API for complex financial and scanned PDFs, with strong table and chart extraction but inconsistent heading semantics. - [Reducto](https://aidemos.com/tools/reducto) — Structured PDF extraction with nested JSON, citations, and row-preserving tables; invoices worked cleanly, while bank statements still need review. - [LlamaParse](https://aidemos.com/tools/llamaparse) — LlamaParse Review: AI Resume Parser & Schema Extraction Tested (2026) - [Extend AI](https://aidemos.com/tools/extend-ai) — Schema-driven extraction for finance PDFs that reconstructs nested JSON well, but still needs review for ordering, IDs, and a few scalar values.