{"observation":{"id":"7723ce9b-b9fc-4a93-ba5e-6a1782b61721","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 tool does not follow the supplied JSON schema exactly, the extracted data cannot be reliably used for structured querying or downstream automation. (3 of 3 judges)","scenario":"bank-statement-pdf","scenario_name":"Bank Statement PDF","group_tag":"business-document-extraction","scenario_description":"A four-page bank statement PDF with dense transaction tables, balance-forward bridges, account metadata, rewards data, and disclaimer text. It was used to stress schema-driven extraction, multi-page continuity, row completeness, and financial numerical accuracy.","modality":"pdf","input_text":null,"input_artifact_refs":[{"alt":null,"url":"https://cdn.futuresmart.ai/public/aidemos/a459a9de9c8a466d8f69b112e04ddad2.pdf?v=1","role":"input","filename":"Bank Statement PDF.pdf"}],"stresses":["Table extraction across 50+ transaction rows","Multi-page continuity with balance-forward bridges","Parsing structured account metadata alongside unstructured transaction descriptions","Numerical accuracy for deposits, withdrawals, running balances, and summaries","Extraction of nested rewards and disclaimer sections"],"verdict":"worked","score":null,"score_total":null,"note":"Follows the requested nested bank-statement schema instead of flattening the document, populating structured objects such as metadata, account_holder, account, branch, balances, transactions, and summary.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://d3epheqghktydj.cloudfront.net/research-media-unstract-bank-statement-output-f0bd422ffa9e.json","role":"output","alt":null},{"url":"https://cdn.futuresmart.ai/public/aidemos/80da7f39ff0c490eabd6ebb36fe5f15c.png?v=1","role":"input","alt":null},{"url":"https://cdn.futuresmart.ai/public/aidemos/b020f08625ab4e0398f1da1f80b4b8a4.png?v=1","role":"input","alt":null},{"url":"https://d3epheqghktydj.cloudfront.net/research-media-unstract-bank-statement-output-053e093910ec.json","role":"output","alt":null}],"run_id":"a061b9e7-a9c5-443d-a171-b296aaf51b8c","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://cdn.futuresmart.ai/public/aidemos/a459a9de9c8a466d8f69b112e04ddad2.pdf?v=1","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","Parsing structured account metadata alongside unstructured transaction descriptions","Numerical accuracy for deposits, withdrawals, running balances, and summaries","Extraction of nested rewards and disclaimer sections"]},"tool_page_slug":"unstract","tool_url":"https://aidemos.com/tools/unstract","permalink":"https://aidemos.com/evidence/7723ce9b-b9fc-4a93-ba5e-6a1782b61721","api_url":"https://ai.aidemos.com/v1/observations/7723ce9b-b9fc-4a93-ba5e-6a1782b61721"},"peers":[{"id":"62e6efd4-2d0b-4870-8159-8b09877da655","tool":"datalab","tool_name":"Datalab","verdict":"worked","score":null,"score_total":null,"note":"Can emit the bank-statement extraction as nested schema-shaped JSON, with separate metadata, account_holder/account, branch, transactions, summary, rewards, and disclaimers objects instead of flat OCR text.","artifact_count":7,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/62e6efd4-2d0b-4870-8159-8b09877da655"},{"id":"b1ccd11a-19d7-4af9-93c2-1ba1086296e5","tool":"extend-ai","tool_name":"Extend AI","verdict":"worked","score":null,"score_total":null,"note":"The tool reconstructs the bank-statement hierarchy into nested JSON with branch, account, rewards, metadata, balances, summary, and transaction-related objects present in the requested layout.","artifact_count":5,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/b1ccd11a-19d7-4af9-93c2-1ba1086296e5"},{"id":"253299f2-f4f9-4a12-ac12-278c46cc1d98","tool":"landing-ai","tool_name":"Landing AI","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the supplied bank-statement hierarchy instead of flat OCR, with nested statement.metadata, account_holder.address, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers objects present in the extracted JSON flow.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/48962ee031ec4496b0c08a3aacebe0d8.mp4?v=1","evidence_url":"https://aidemos.com/evidence/253299f2-f4f9-4a12-ac12-278c46cc1d98"},{"id":"8fb0c5fe-7b66-4d4c-bd2e-f63382a6c431","tool":"llamaparse","tool_name":"LlamaParse","verdict":"worked","score":null,"score_total":null,"note":"The bank-statement output follows the requested nested schema closely, reconstructing metadata, account_holder, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers as structured objects rather than flat OCR text.","artifact_count":5,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/8fb0c5fe-7b66-4d4c-bd2e-f63382a6c431"},{"id":"cb734437-1435-4fa1-a680-6250f75e51e4","tool":"nanonets","tool_name":"Nanonets","verdict":"worked","score":null,"score_total":null,"note":"Preserves the supplied bank-statement hierarchy in structured JSON, with nested statement.metadata, account_holder, account, balances, transactions, summary, rewards, and disclaimers objects 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/cb734437-1435-4fa1-a680-6250f75e51e4"},{"id":"8c64ae90-42e2-4970-a51d-5abbc49c12c4","tool":"reducto","tool_name":"Reducto","verdict":"worked","score":null,"score_total":null,"note":"Preserves a nested, schema-shaped JSON structure for the bank statement instead of flattening the document into raw OCR, including top-level objects like metadata, account_holder, account, branch, statement_period, transactions, and summary.","artifact_count":3,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-bank-statement-2-jul-a511cf7bd0d6.png","evidence_url":"https://aidemos.com/evidence/8c64ae90-42e2-4970-a51d-5abbc49c12c4"},{"id":"0a4f8a1a-6dec-4424-b76b-d0568f8dcd32","tool":"retab","tool_name":"Retab","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the supplied bank-statement schema into a nested JSON object with separate statement.metadata, account_holder, account, branch, balances, transactions, summary, rewards, and disclaimers sections 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/0a4f8a1a-6dec-4424-b76b-d0568f8dcd32"}],"other_criteria":[{"id":"44ed2bfd-2af0-4a49-81df-897e1fc3a7e7","criterion":"extraction-accuracy","criterion_name":"Extraction Accuracy","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"The opening and closing balance figures are extracted accurately and match the source statement values exactly.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/44ed2bfd-2af0-4a49-81df-897e1fc3a7e7"},{"id":"d859ffe7-484f-444c-adcf-a0fe358bcced","criterion":"extraction-accuracy","criterion_name":"Extraction Accuracy","rank_role":"decisive","verdict":"failed","score":null,"score_total":null,"note":"Understates the derived bank-summary count: summary.total_transactions is reported as 43 even though the PDF contains 51 transactions, a 16% undercount.","artifact_count":3,"evidence_url":"https://aidemos.com/evidence/d859ffe7-484f-444c-adcf-a0fe358bcced"},{"id":"5cf41e1e-0a8d-4000-a164-198ad5e3b579","criterion":"semantic-field-enrichment","criterion_name":"Semantic Field Enrichment","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Correctly enriches bank rows with transaction_type classification, with the report stating Withdrawal/Deposit was assigned correctly on all 51 transaction rows.","artifact_count":3,"evidence_url":"https://aidemos.com/evidence/5cf41e1e-0a8d-4000-a164-198ad5e3b579"},{"id":"661cd077-e5e0-47df-968b-3d030f1ed64d","criterion":"table-record-completeness","criterion_name":"Table & Record Completeness","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Keeps the bank transaction table complete across all 4 pages, with the report stating the transactions array contains all 51 entries and no rows were dropped or merged.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/661cd077-e5e0-47df-968b-3d030f1ed64d"}],"appears_in":[{"page_type":"ranking","slug":"document-extraction","title":"Best AI Tools for Extracting Structured Data from Business Documents","url":"https://aidemos.com/best/document-extraction","binding":"run"},{"page_type":"tool","slug":"docsumo","title":null,"url":"https://aidemos.com/tools/docsumo","binding":"run"}],"same_scenario":[{"id":"62e6efd4-2d0b-4870-8159-8b09877da655","tool":"datalab","tool_name":"Datalab","verdict":"worked","score":null,"score_total":null,"note":"Can emit the bank-statement extraction as nested schema-shaped JSON, with separate metadata, account_holder/account, branch, transactions, summary, rewards, and disclaimers objects instead of flat OCR text."},{"id":"b1ccd11a-19d7-4af9-93c2-1ba1086296e5","tool":"extend-ai","tool_name":"Extend AI","verdict":"worked","score":null,"score_total":null,"note":"The tool reconstructs the bank-statement hierarchy into nested JSON with branch, account, rewards, metadata, balances, summary, and transaction-related objects present in the requested layout."},{"id":"253299f2-f4f9-4a12-ac12-278c46cc1d98","tool":"landing-ai","tool_name":"Landing AI","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the supplied bank-statement hierarchy instead of flat OCR, with nested statement.metadata, account_holder.address, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers objects present in the extracted JSON flow."},{"id":"8fb0c5fe-7b66-4d4c-bd2e-f63382a6c431","tool":"llamaparse","tool_name":"LlamaParse","verdict":"worked","score":null,"score_total":null,"note":"The bank-statement output follows the requested nested schema closely, reconstructing metadata, account_holder, account, branch, statement_period, balances, transactions, summary, rewards, and disclaimers as structured objects rather than flat OCR text."},{"id":"cb734437-1435-4fa1-a680-6250f75e51e4","tool":"nanonets","tool_name":"Nanonets","verdict":"worked","score":null,"score_total":null,"note":"Preserves the supplied bank-statement hierarchy in structured JSON, with nested statement.metadata, account_holder, account, balances, transactions, summary, rewards, and disclaimers objects instead of flattening the document into OCR text."},{"id":"8c64ae90-42e2-4970-a51d-5abbc49c12c4","tool":"reducto","tool_name":"Reducto","verdict":"worked","score":null,"score_total":null,"note":"Preserves a nested, schema-shaped JSON structure for the bank statement instead of flattening the document into raw OCR, including top-level objects like metadata, account_holder, account, branch, statement_period, transactions, and summary."},{"id":"0a4f8a1a-6dec-4424-b76b-d0568f8dcd32","tool":"retab","tool_name":"Retab","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the supplied bank-statement schema into a nested JSON object with separate statement.metadata, account_holder, account, branch, balances, transactions, summary, rewards, and disclaimers sections instead of flattening the document into OCR text."}]}