{"observation":{"id":"ad656fd6-a7c6-4174-a1a5-71f32b26d964","tool":"extend-ai","tool_name":"Extend AI","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":"invoice-pdf","scenario_name":"Invoice PDF","group_tag":"financial-document-extraction","scenario_description":"A 2-page broadcast advertising invoice PDF with 8 line items, complex time/day fields, large dollar amounts, and compliance text, used to test hierarchical line-item extraction and financial validation.","modality":"pdf","input_text":null,"input_artifact_refs":[{"alt":null,"url":null,"role":null,"filename":"Invoice PDF.pdf"}],"stresses":["Nested line-item hierarchy extraction","Multi-page line-item continuity across a page break","Large dollar amount precision and total validation","Parsing time slots, day patterns, and air dates","Extraction of alphanumeric ad IDs and reference codes","Structured metadata mapping for advertiser, station, billing, and remit sections","Political advertising and FCC compliance text recognition"],"verdict":"worked","score":null,"score_total":null,"note":"The invoice output is reconstructed as nested JSON sections — invoice_metadata, advertiser, station, addresses, flight_dates, line_items, and summary — instead of raw OCR.","evidence_state":"verified","source":null,"artifacts":[{"url":"https://d3epheqghktydj.cloudfront.net/research-media-extendai-invoice-output-9eaac6626f6c.json","role":"output","alt":null},{"url":"https://d3epheqghktydj.cloudfront.net/research-media-extend-invoice-metadata-26a17634e0dd.png","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":"output-only","input":{"state":"not-captured","text":null,"files":[],"modality":"pdf","stresses":["Nested line-item hierarchy extraction","Multi-page line-item continuity across a page break","Large dollar amount precision and total validation","Parsing time slots, day patterns, and air dates","Extraction of alphanumeric ad IDs and reference codes","Structured metadata mapping for advertiser, station, billing, and remit sections","Political advertising and FCC compliance text recognition"]},"tool_page_slug":"extend-ai","tool_url":"https://aidemos.com/tools/extend-ai","permalink":"https://aidemos.com/evidence/ad656fd6-a7c6-4174-a1a5-71f32b26d964","api_url":"https://ai.aidemos.com/v1/observations/ad656fd6-a7c6-4174-a1a5-71f32b26d964"},"peers":[{"id":"c01474f8-d601-4535-b19c-9ab5759d7f35","tool":"datalab","tool_name":"Datalab","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the invoice into nested JSON objects and arrays that follow the supplied schema, including invoice_metadata and line_items rather than returning generic OCR text.","artifact_count":5,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-datalab-invoice-metadat-1c39e0c1c45e.png","evidence_url":"https://aidemos.com/evidence/c01474f8-d601-4535-b19c-9ab5759d7f35"},{"id":"e21e3065-67a9-44b0-a7d5-4534cc545780","tool":"landing-ai","tool_name":"Landing AI","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the requested invoice hierarchy in structured JSON, filling invoice_metadata, advertiser, station, line_items, and summary objects instead of returning a flat extraction.","artifact_count":4,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-landing-ai-extracted-invoice-metadata-57a291c39e3d.png","evidence_url":"https://aidemos.com/evidence/e21e3065-67a9-44b0-a7d5-4534cc545780"},{"id":"0b3ba3e3-dd88-4377-97f7-2da41c08c915","tool":"llamaparse","tool_name":"LlamaParse","verdict":"worked","score":null,"score_total":null,"note":"Keeps the invoice hierarchy intact, emitting invoice_metadata, advertiser, station, line_items, and summary objects rather than flat OCR text.","artifact_count":2,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-llamaparse-invoice-metadata-af804b493598.png","evidence_url":"https://aidemos.com/evidence/0b3ba3e3-dd88-4377-97f7-2da41c08c915"},{"id":"d1acf6ae-95ec-460a-bac2-aed663d491c5","tool":"nanonets","tool_name":"Nanonets","verdict":"worked","score":null,"score_total":null,"note":"It preserves the invoice hierarchy exactly as requested, filling dedicated objects for invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary.","artifact_count":2,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-docstrange-nanonets-invoice-output-9dc0f476da4c.json","evidence_url":"https://aidemos.com/evidence/d1acf6ae-95ec-460a-bac2-aed663d491c5"},{"id":"3d48880c-53de-43f1-b4b6-4da534758c2b","tool":"reducto","tool_name":"Reducto","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the invoice into the requested nested JSON hierarchy, populating invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary sections.","artifact_count":3,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-reducto-invoice-metadata-c12acb4fc069.png","evidence_url":"https://aidemos.com/evidence/3d48880c-53de-43f1-b4b6-4da534758c2b"},{"id":"38fc2de5-4406-486b-9a50-fa39c4b5e88a","tool":"retab","tool_name":"Retab","verdict":"worked","score":null,"score_total":null,"note":"Builds the requested invoice hierarchy with separate invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary objects instead of a flat OCR dump.","artifact_count":3,"thumbnail":"https://d3epheqghktydj.cloudfront.net/research-media-retab-extracted-invoice-metadata-2b6610b8f928.png","evidence_url":"https://aidemos.com/evidence/38fc2de5-4406-486b-9a50-fa39c4b5e88a"},{"id":"15ef12f7-5460-42d4-99d5-9ee4785c8552","tool":"unstract","tool_name":"Unstract","verdict":"worked","score":null,"score_total":null,"note":"Populates the requested invoice hierarchy into dedicated sections for invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary.","artifact_count":2,"thumbnail":"https://cdn.futuresmart.ai/public/aidemos/18ce6c9c9f314db694ae8439172b4041.png?v=1","evidence_url":"https://aidemos.com/evidence/15ef12f7-5460-42d4-99d5-9ee4785c8552"}],"other_criteria":[{"id":"21668a8b-96b6-4a98-ab20-6b71dc0db783","criterion":"extraction-accuracy","criterion_name":"Extraction Accuracy","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"Its invoice totals match the source document, including 8 aired spots, gross_total 29750, agency_commission 4462.5, and net_amount_due 25287.5.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/21668a8b-96b6-4a98-ab20-6b71dc0db783"},{"id":"c2e5a33b-c652-43eb-bfc8-746afb8b8020","criterion":"extraction-accuracy","criterion_name":"Extraction Accuracy","rank_role":"decisive","verdict":"struggled","score":null,"score_total":null,"note":"It sometimes carries source labels into values: `payment_terms` is returned as `Payment Terms 30 Days` rather than the bare value `30 Days`.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/c2e5a33b-c652-43eb-bfc8-746afb8b8020"},{"id":"b387cbf1-3dca-4b92-9a44-768b8a7f0926","criterion":"semantic-field-enrichment","criterion_name":"Semantic Field Enrichment","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"It derives non-OCR line-item fields such as `day_of_week`, `days_pattern`, `time_slot`, and `air_time`.","artifact_count":1,"evidence_url":"https://aidemos.com/evidence/b387cbf1-3dca-4b92-9a44-768b8a7f0926"},{"id":"be4e0664-3a37-4913-a8df-3cd6f8915087","criterion":"structural-clean-output","criterion_name":"Structural Clean Output","rank_role":"context","verdict":"failed","score":null,"score_total":null,"note":"It reorders several schema sections relative to the supplied schema, so the output is not consistently in the authored field order.","artifact_count":3,"evidence_url":"https://aidemos.com/evidence/be4e0664-3a37-4913-a8df-3cd6f8915087"},{"id":"6af0c5f8-49f9-4f6a-abc9-05b7feff51df","criterion":"table-and-record-completeness","criterion_name":"Table & Record Completeness","rank_role":"decisive","verdict":"worked","score":null,"score_total":null,"note":"It extracts all eight invoice line items as separate rows, preserving description, airtime, rate, flight period, reference number, and campaign IDs without merging records.","artifact_count":2,"evidence_url":"https://aidemos.com/evidence/6af0c5f8-49f9-4f6a-abc9-05b7feff51df"}],"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":"c01474f8-d601-4535-b19c-9ab5759d7f35","tool":"datalab","tool_name":"Datalab","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the invoice into nested JSON objects and arrays that follow the supplied schema, including invoice_metadata and line_items rather than returning generic OCR text."},{"id":"e21e3065-67a9-44b0-a7d5-4534cc545780","tool":"landing-ai","tool_name":"Landing AI","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the requested invoice hierarchy in structured JSON, filling invoice_metadata, advertiser, station, line_items, and summary objects instead of returning a flat extraction."},{"id":"0b3ba3e3-dd88-4377-97f7-2da41c08c915","tool":"llamaparse","tool_name":"LlamaParse","verdict":"worked","score":null,"score_total":null,"note":"Keeps the invoice hierarchy intact, emitting invoice_metadata, advertiser, station, line_items, and summary objects rather than flat OCR text."},{"id":"d1acf6ae-95ec-460a-bac2-aed663d491c5","tool":"nanonets","tool_name":"Nanonets","verdict":"worked","score":null,"score_total":null,"note":"It preserves the invoice hierarchy exactly as requested, filling dedicated objects for invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary."},{"id":"3d48880c-53de-43f1-b4b6-4da534758c2b","tool":"reducto","tool_name":"Reducto","verdict":"worked","score":null,"score_total":null,"note":"Reconstructs the invoice into the requested nested JSON hierarchy, populating invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary sections."},{"id":"38fc2de5-4406-486b-9a50-fa39c4b5e88a","tool":"retab","tool_name":"Retab","verdict":"worked","score":null,"score_total":null,"note":"Builds the requested invoice hierarchy with separate invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary objects instead of a flat OCR dump."},{"id":"15ef12f7-5460-42d4-99d5-9ee4785c8552","tool":"unstract","tool_name":"Unstract","verdict":"worked","score":null,"score_total":null,"note":"Populates the requested invoice hierarchy into dedicated sections for invoice_metadata, advertiser, station, account_details, billing_address, remit_address, flight_dates, line_items, and summary."}]}