{"observation":{"id":"db903e38-5c91-4688-ac04-2930dc9e72ab","tool":"unstract","tool_name":"Unstract","criterion":"schema-adherence","criterion_name":"Schema Adherence","criterion_definition":"Does the output follow the supplied JSON schema hierarchy exactly, with correct nesting, field names, and data types?","criterion_evidence_type":"transformation","criterion_rank_role":"decisive","criterion_rank_role_reason":"If the output does not match the requested JSON schema exactly, the extracted data cannot be reliably consumed or queried, so this is core to the task. (3 of 3 judges)","scenario":"bank-statement-pdf","scenario_name":"Bank Statement PDF","group_tag":"financial-document-extraction","scenario_description":"A 4-page bank statement PDF with 51 transactions, balances, rewards, and disclaimer text, used to test schema-driven extraction of dense financial tables and multi-page continuity.","modality":"pdf","input_text":null,"input_artifact_refs":[{"alt":null,"url":"https://d3epheqghktydj.cloudfront.net/extract-and-query-structured-data-from-d-bank-statement-pdf-25ec532be6e1.pdf","role":"input","filename":"Bank Statement PDF.pdf"}],"stresses":["Table extraction across 50+ transaction rows","Multi-page continuity with BALANCE FORWARD bridges","Structured metadata vs. free-text transaction descriptions","Numerical accuracy for balances, deposits, withdrawals, and summaries","Nested schema population for account, branch, balances, rewards, and disclaimers"],"verdict":"worked","score":null,"score_total":null,"note":"Preserves the requested nested statement hierarchy instead of flattening it, with separate metadata, account_holder, account, branch, balances, transactions, summary, and other top-level sections.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://cdn.futuresmart.ai/public/aidemos/6495c5a868854823bcf3cd94c90bb6aa.pdf?v=1","role":"input","alt":"6495c5a868854823bcf3cd94c90bb6aa.pdf"},{"url":"https://d3epheqghktydj.cloudfront.net/unstract-unstract-bank-statement-output-f0bd422ffa9e.json","role":"output","alt":null}],"run_id":"ec4d736d-95f9-4c88-884c-e280435f7b7b","study_title":"Extract and query structured data from documents using natural language","study_kind":"generation","research_task":"86b9y25e5","tested_at":null,"completeness":"input-and-output","input":{"state":"files","text":null,"files":[{"url":"https://d3epheqghktydj.cloudfront.net/extract-and-query-structured-data-from-d-bank-statement-pdf-25ec532be6e1.pdf","filename":"Bank Statement PDF.pdf","alt":"Bank Statement PDF","role":"input"}],"modality":"pdf","stresses":["Table extraction across 50+ transaction rows","Multi-page continuity with BALANCE FORWARD bridges","Structured metadata vs. free-text transaction descriptions","Numerical accuracy for balances, deposits, withdrawals, and summaries","Nested schema population for account, branch, balances, rewards, and disclaimers"]},"tool_page_slug":"unstract","tool_url":"https://aidemos.com/tools/unstract","permalink":"https://aidemos.com/evidence/db903e38-5c91-4688-ac04-2930dc9e72ab","api_url":"https://ai.aidemos.com/v1/observations/db903e38-5c91-4688-ac04-2930dc9e72ab"},"peers":[{"id":"3741073d-f776-4beb-b189-4f22c0b5d11e","tool":"datalab","tool_name":"Datalab","verdict":"worked","score":null,"score_total":null,"note":"Maps the bank statement into the requested nested JSON hierarchy instead of flattening it into OCR text, and preserves field-level citation metadata on the extracted objects.","artifact_count":6,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/3741073d-f776-4beb-b189-4f22c0b5d11e"},{"id":"a72ab809-ef71-47eb-8461-d9518c96d28b","tool":"extend-ai","tool_name":"Extend AI","verdict":"worked","score":null,"score_total":null,"note":"The bank output is rebuilt as nested JSON rather than raw OCR, with branch, account, rewards, balances, summary, and metadata objects populated under the requested statement root.","artifact_count":4,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/a72ab809-ef71-47eb-8461-d9518c96d28b"},{"id":"91f210ef-e7ef-4d23-a3c8-badb7719e663","tool":"landing-ai","tool_name":"Landing AI","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the bank statement into the requested nested JSON hierarchy, with distinct statement.metadata, account_holder.address, account, branch, statement_period, and balances objects rather than flat OCR text.","artifact_count":3,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/91f210ef-e7ef-4d23-a3c8-badb7719e663"},{"id":"c7ef413a-1654-4252-a5fe-451fa0f92388","tool":"llamaparse","tool_name":"LlamaParse","verdict":"worked","score":null,"score_total":null,"note":"Keeps a nested statement schema intact, emitting separate metadata, account_holder, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers objects instead of flattening the document.","artifact_count":3,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/c7ef413a-1654-4252-a5fe-451fa0f92388"},{"id":"86ec5538-27d3-490e-88ab-a30bbd4479e8","tool":"nanonets","tool_name":"Nanonets","verdict":"worked","score":null,"score_total":null,"note":"It preserves the requested nested schema directly in the output, populating structured objects such as statement, account, balances, transactions, summary, rewards, and disclaimers instead of flattening the document into OCR text.","artifact_count":3,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/86ec5538-27d3-490e-88ab-a30bbd4479e8"},{"id":"95071c07-2f7a-463b-b7c7-3738ddbd3651","tool":"reducto","tool_name":"Reducto","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the bank statement into a nested JSON structure aligned to the requested schema, with document metadata, account, branch, statement period, transactions, summary, and rewards-style sections instead of flat OCR text.","artifact_count":6,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/95071c07-2f7a-463b-b7c7-3738ddbd3651"},{"id":"18014a53-cc1f-4a5d-8552-fc026341e820","tool":"retab","tool_name":"Retab","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs dense statement OCR into the requested nested JSON hierarchy, populating separate statement, account_holder, account, branch, balances, transactions, rewards, and disclaimers objects instead of flattening everything into text.","artifact_count":4,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/18014a53-cc1f-4a5d-8552-fc026341e820"}],"other_criteria":[{"id":"fc237f3a-5e49-42f5-916c-ceda1f9fee8b","criterion":"extraction-accuracy","criterion_name":"Extraction Accuracy","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"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.","artifact_count":4,"evidence_url":"https://aidemos.com/evidence/fc237f3a-5e49-42f5-916c-ceda1f9fee8b"},{"id":"9256b7b3-d9fb-4b69-ba61-8ffde0fa3f6d","criterion":"extraction-accuracy","criterion_name":"Extraction Accuracy","rank_role":"decisive","verdict":"failed","score":null,"score_total":null,"note":"Derives an incorrect summary transaction count, reporting 43 when the statement actually contains 51 rows.","artifact_count":3,"evidence_url":"https://aidemos.com/evidence/9256b7b3-d9fb-4b69-ba61-8ffde0fa3f6d"},{"id":"9c65076c-303b-4d4c-a3f7-619f2667c82e","criterion":"semantic-field-enrichment","criterion_name":"Semantic Field Enrichment","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Classifies transaction_type correctly across the transaction array, using Deposit and Withdrawal labels rather than raw OCR text.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/9c65076c-303b-4d4c-a3f7-619f2667c82e"},{"id":"1355f0ad-f96e-476c-9a0d-ab7e11352c1e","criterion":"table-and-record-completeness","criterion_name":"Table & Record Completeness","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Keeps the transaction table intact across all four pages, with the full 51-row record set present and no merged or dropped rows.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/1355f0ad-f96e-476c-9a0d-ab7e11352c1e"}],"appears_in":[{"page_type":"ranking","slug":"document-extraction","title":"Best AI Tools for Extracting Structured Data from PDFs and Business Documents","url":"https://aidemos.com/best/document-extraction","binding":"run"},{"page_type":"tool","slug":"unstract","title":null,"url":"https://aidemos.com/tools/unstract","binding":"run"}],"same_scenario":[{"id":"3741073d-f776-4beb-b189-4f22c0b5d11e","tool":"datalab","tool_name":"Datalab","verdict":"worked","score":null,"score_total":null,"note":"Maps the bank statement into the requested nested JSON hierarchy instead of flattening it into OCR text, and preserves field-level citation metadata on the extracted objects."},{"id":"a72ab809-ef71-47eb-8461-d9518c96d28b","tool":"extend-ai","tool_name":"Extend AI","verdict":"worked","score":null,"score_total":null,"note":"The bank output is rebuilt as nested JSON rather than raw OCR, with branch, account, rewards, balances, summary, and metadata objects populated under the requested statement root."},{"id":"91f210ef-e7ef-4d23-a3c8-badb7719e663","tool":"landing-ai","tool_name":"Landing AI","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the bank statement into the requested nested JSON hierarchy, with distinct statement.metadata, account_holder.address, account, branch, statement_period, and balances objects rather than flat OCR text."},{"id":"c7ef413a-1654-4252-a5fe-451fa0f92388","tool":"llamaparse","tool_name":"LlamaParse","verdict":"worked","score":null,"score_total":null,"note":"Keeps a nested statement schema intact, emitting separate metadata, account_holder, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers objects instead of flattening the document."},{"id":"86ec5538-27d3-490e-88ab-a30bbd4479e8","tool":"nanonets","tool_name":"Nanonets","verdict":"worked","score":null,"score_total":null,"note":"It preserves the requested nested schema directly in the output, populating structured objects such as statement, account, balances, transactions, summary, rewards, and disclaimers instead of flattening the document into OCR text."},{"id":"95071c07-2f7a-463b-b7c7-3738ddbd3651","tool":"reducto","tool_name":"Reducto","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the bank statement into a nested JSON structure aligned to the requested schema, with document metadata, account, branch, statement period, transactions, summary, and rewards-style sections instead of flat OCR text."},{"id":"18014a53-cc1f-4a5d-8552-fc026341e820","tool":"retab","tool_name":"Retab","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs dense statement OCR into the requested nested JSON hierarchy, populating separate statement, account_holder, account, branch, balances, transactions, rewards, and disclaimers objects instead of flattening everything into text."}]}