{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_tabs","slug":"tabs","name":"Tabs","type":"product","url":"https://tabs.inc","page_url":"https://unfragile.ai/tabs","categories":["data-analysis"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_tabs__cap_0","uri":"capability://data.processing.analysis.ai.driven.document.layout.and.field.extraction.from.variable.format.b2b.documents","name":"ai-driven document layout and field extraction from variable-format b2b documents","description":"Tabs uses computer vision and machine learning models trained on B2B financial documents to automatically identify and extract key fields (contract terms, invoice line items, payment dates, amounts) from PDFs and scanned images with variable layouts, poor OCR quality, and non-standard formatting. The system likely employs layout analysis (detecting tables, headers, signatures) combined with named entity recognition and field classification to map unstructured document content into structured data schemas without manual template configuration.","intents":["Extract contract terms and obligations from hundreds of vendor agreements without manual review","Automatically populate invoice data into accounting systems from poorly scanned or non-standard supplier invoices","Reduce manual data entry time for accounts payable teams processing variable document formats","Handle edge cases like handwritten annotations, multiple languages, or degraded document quality"],"best_for":["Finance teams processing 100+ invoices/contracts monthly with high format variability","Enterprises with legacy vendor relationships producing non-standardized documents","Mid-to-large companies seeking to reduce AP manual labor costs"],"limitations":["Extraction accuracy likely degrades on highly unusual or proprietary document formats not in training data","No explicit information on handling multi-page document stitching or cross-reference resolution","Requires sufficient document volume to justify AR infrastructure investment","Performance on handwritten or heavily annotated documents unknown"],"requires":["PDF or image files (JPEG, PNG, TIFF) of contracts or invoices","AR-capable device for visualization (iOS/Android with ARKit/ARCore support)","Integration with accounting/ERP system for downstream data population"],"input_types":["PDF documents","Scanned images (JPEG, PNG, TIFF)","Multi-page document bundles"],"output_types":["Structured JSON with extracted fields","CSV/Excel export for bulk import","API responses for system integration"],"categories":["data-processing-analysis","document-intelligence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tabs__cap_1","uri":"capability://image.visual.ar.based.contract.and.invoice.visualization.with.interactive.term.highlighting","name":"ar-based contract and invoice visualization with interactive term highlighting","description":"Tabs renders extracted contract terms and invoice line items in augmented reality overlays on physical or digital documents, allowing users to tap, highlight, and navigate key obligations, payment terms, and line items in spatial context. The AR layer likely uses computer vision to track document position in real-time and maps extracted data fields to their original locations in the document, enabling intuitive visual comprehension without context-switching between PDFs and spreadsheets.","intents":["Visually compare contract obligations across multiple vendor agreements side-by-side in AR space","Quickly identify and highlight payment terms, discounts, and renewal dates without reading full contract text","Review invoice line items with cost breakdowns overlaid on the physical invoice","Train new finance team members on contract review workflows using interactive AR walkthroughs"],"best_for":["Finance teams with AR infrastructure and user training capacity","Organizations prioritizing document comprehension speed over pure automation","Enterprises with complex multi-party contracts requiring visual comparison"],"limitations":["AR requirement adds friction — most finance teams lack AR devices or training","Spatial tracking accuracy depends on document lighting, angle, and camera quality","AR visualization adds latency to extraction workflow (document capture → processing → AR render)","Limited to mobile/tablet AR platforms (iOS ARKit, Android ARCore) — no desktop AR support mentioned","Unclear if AR overlays persist across sessions or require real-time document tracking"],"requires":["AR-capable device (iPhone 6s+, iPad 2017+, Android 7.0+ with ARCore support)","Tabs mobile application installed","Physical document or high-resolution digital document image for tracking"],"input_types":["Physical documents (via device camera)","Digital document images (PDF, JPEG, PNG)"],"output_types":["AR overlay visualization","Annotated document screenshots","Highlighted term summaries"],"categories":["image-visual","user-interface"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tabs__cap_2","uri":"capability://data.processing.analysis.contract.obligation.and.risk.classification.with.semantic.understanding","name":"contract obligation and risk classification with semantic understanding","description":"Tabs applies NLP and machine learning classification to extracted contract terms to automatically categorize obligations (payment terms, renewal clauses, liability limits, termination conditions) and flag potential risks (unfavorable payment windows, auto-renewal traps, unusual liability caps). The system likely uses domain-specific language models trained on B2B contract corpora to understand semantic meaning beyond keyword matching, enabling detection of obligation types even when phrased differently across documents.","intents":["Automatically flag high-risk contract clauses (e.g., unlimited liability, unfavorable payment terms) without manual review","Classify contract obligations by type (payment, renewal, termination, liability) for workflow routing","Compare risk profiles across vendor agreements to identify outliers or unfavorable terms","Generate contract summary reports highlighting key obligations and risks for stakeholder review"],"best_for":["Legal and finance teams managing 50+ vendor contracts with varying risk profiles","Enterprises seeking to standardize contract terms across suppliers","Organizations with limited contract management expertise needing automated risk detection"],"limitations":["Classification accuracy depends on training data — may miss novel or industry-specific obligation types","Risk flagging is heuristic-based and may produce false positives (e.g., flagging standard industry terms as risky)","No explicit information on handling multi-jurisdictional contracts with different legal frameworks","Unclear if system learns from user corrections to improve classification over time","May not detect implicit risks (e.g., missing clauses that should be present)"],"requires":["Extracted contract text with identified fields","Training data or configuration for domain-specific obligation types","Integration with contract management system for risk tracking"],"input_types":["Extracted contract text","Structured contract metadata (vendor, date, type)"],"output_types":["Risk classification scores","Obligation category tags","Risk summary reports","Flagged clause excerpts"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tabs__cap_3","uri":"capability://data.processing.analysis.multi.document.contract.comparison.and.obligation.mapping","name":"multi-document contract comparison and obligation mapping","description":"Tabs enables side-by-side comparison of extracted obligations across multiple contracts, automatically mapping equivalent terms across documents (e.g., 'Net 30 payment terms' vs '30-day payment window') and highlighting discrepancies. The system likely uses semantic similarity matching and field alignment algorithms to identify when different contracts express the same obligation using different language, enabling users to spot inconsistencies in vendor terms without manual cross-referencing.","intents":["Compare payment terms across 10+ vendor contracts to identify outliers or negotiate standardization","Map equivalent renewal clauses across contracts to identify inconsistent auto-renewal policies","Highlight discrepancies in liability limits or insurance requirements across similar vendor agreements","Generate contract comparison matrices for procurement negotiations"],"best_for":["Procurement teams managing large vendor portfolios seeking term standardization","Enterprises renegotiating vendor agreements and needing baseline comparisons","Finance teams auditing contract compliance across multiple suppliers"],"limitations":["Comparison accuracy depends on extraction quality — errors in field extraction compound in comparisons","Semantic matching may produce false equivalences if obligations have subtle but important differences","Unclear if system handles multi-language contracts or contracts with regional variations","Performance may degrade with very large contract sets (100+ documents) — no information on scaling","No explicit support for version tracking or contract amendment comparison"],"requires":["Multiple extracted contracts (minimum 2, typically 5+)","Consistent field extraction across documents","Semantic similarity model trained on contract language"],"input_types":["Multiple extracted contract datasets","Contract metadata (vendor, type, date)"],"output_types":["Comparison matrices (CSV, JSON)","Discrepancy reports with highlighted differences","Obligation mapping visualizations","Standardization recommendations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tabs__cap_4","uri":"capability://data.processing.analysis.invoice.line.item.extraction.and.cost.allocation.with.gl.account.mapping","name":"invoice line-item extraction and cost allocation with gl account mapping","description":"Tabs extracts individual line items from invoices (description, quantity, unit price, total, tax) and automatically maps them to general ledger accounts based on item description, vendor category, and historical allocation patterns. The system likely uses item classification models and GL account mapping rules to route costs to appropriate expense categories without manual coding, enabling direct integration with accounting systems.","intents":["Automatically populate invoice line items into accounting system without manual data entry","Route invoice costs to correct GL accounts based on item description and vendor type","Detect invoice discrepancies (e.g., quantity mismatches, price overages) before payment","Generate cost allocation reports by GL account or cost center"],"best_for":["Finance teams processing 500+ invoices monthly with high manual data entry burden","Enterprises with complex GL structures requiring intelligent cost allocation","Organizations seeking to reduce accounts payable processing time and errors"],"limitations":["GL account mapping accuracy depends on training data — may misclassify novel item types","Extraction accuracy varies with invoice format — complex multi-page invoices with nested tables may fail","No explicit information on handling multi-currency invoices or tax calculation variations","Requires integration with specific accounting systems (SAP, Oracle, NetSuite, etc.) — unclear which are supported","May not handle split allocations (e.g., single line item split across multiple GL accounts)"],"requires":["Invoice PDF or image files","GL account mapping configuration or training data","Integration with accounting system (API or file import)","Historical invoice data for GL account pattern learning (optional)"],"input_types":["Invoice PDFs","Scanned invoice images","Multi-page invoice bundles"],"output_types":["Structured line-item JSON","GL account-coded invoice data","Accounting system import files (CSV, XML)","Cost allocation reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tabs__cap_5","uri":"capability://data.processing.analysis.invoice.duplicate.detection.and.fraud.prevention.with.multi.field.matching","name":"invoice duplicate detection and fraud prevention with multi-field matching","description":"Tabs applies multi-field matching algorithms to detect duplicate invoices (same vendor, amount, date within tolerance) and flag potential fraud indicators (duplicate payments, amount mismatches vs PO, unusual payment patterns). The system likely uses fuzzy matching on vendor name, invoice number, and amount to catch duplicates even with minor variations, and applies heuristic rules to flag anomalies like invoices from new vendors or unusual payment terms.","intents":["Prevent duplicate invoice payments by detecting invoices with matching vendor, amount, and date","Flag invoices with amounts that deviate significantly from corresponding purchase orders","Detect unusual payment patterns (e.g., invoices from new vendors, unusual payment terms) that may indicate fraud","Generate fraud risk reports for audit and compliance teams"],"best_for":["Finance teams processing high invoice volumes (1000+/month) with fraud risk concerns","Enterprises with decentralized purchasing lacking centralized invoice controls","Organizations seeking to reduce payment errors and fraud losses"],"limitations":["Duplicate detection relies on fuzzy matching thresholds — may miss duplicates with significant variations or produce false positives","Fraud detection is heuristic-based and may not catch sophisticated fraud schemes","Requires access to historical invoice data and PO data for pattern learning — unclear if system learns over time","No explicit information on handling legitimate duplicate invoices (e.g., split shipments, partial invoices)","May not detect fraud indicators outside invoice data (e.g., vendor account takeover)"],"requires":["Invoice data with vendor, amount, date, invoice number fields","Historical invoice dataset for duplicate detection (minimum 100+ invoices recommended)","Optional: PO data for amount variance detection","Optional: Vendor master data for new vendor detection"],"input_types":["Extracted invoice data (structured JSON or CSV)","Historical invoice records","PO data (optional)"],"output_types":["Duplicate invoice flags with confidence scores","Fraud risk alerts with reason codes","Variance reports (invoice vs PO)","Audit trail with flagged invoices"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tabs__cap_6","uri":"capability://automation.workflow.workflow.automation.and.approval.routing.based.on.extracted.contract.invoice.attributes","name":"workflow automation and approval routing based on extracted contract/invoice attributes","description":"Tabs automatically routes contracts and invoices to appropriate approvers based on extracted attributes (amount, vendor, contract type, risk classification) using configurable routing rules. The system likely implements a rules engine that evaluates extracted fields against approval thresholds and policies, enabling organizations to define approval workflows without manual intervention (e.g., invoices >$10k route to CFO, high-risk contracts route to legal).","intents":["Automatically route invoices to appropriate approvers based on amount, vendor, and GL account","Escalate high-risk contracts to legal team for review before signature","Implement approval workflows that enforce organizational policies (e.g., CFO approval for >$50k contracts)","Reduce approval cycle time by eliminating manual routing and follow-up"],"best_for":["Enterprises with complex approval hierarchies and policy requirements","Organizations seeking to reduce approval cycle time and manual routing overhead","Finance teams with decentralized approval authority needing centralized policy enforcement"],"limitations":["Routing accuracy depends on extraction quality — errors in field extraction lead to incorrect routing","Rules engine requires upfront configuration and maintenance — unclear if system provides templates or requires custom rules","No explicit information on handling exceptions or escalation paths when standard rules don't apply","Unclear if system integrates with existing approval workflow tools (Workday, SAP Ariba, etc.)","May not handle complex conditional logic (e.g., route based on combination of vendor + amount + GL account)"],"requires":["Extracted contract/invoice data with routing-relevant fields","Approval workflow configuration (rules, thresholds, approver assignments)","Integration with email or workflow system for approval notifications","User directory for approver assignment"],"input_types":["Extracted contract/invoice attributes","Approval workflow rules (JSON or UI-configured)"],"output_types":["Approval task assignments","Workflow status notifications","Approval audit trail","Workflow analytics (cycle time, approval rates)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tabs__cap_7","uri":"capability://tool.use.integration.integration.with.accounting.and.erp.systems.via.api.and.file.import","name":"integration with accounting and erp systems via api and file import","description":"Tabs provides API endpoints and file import capabilities (CSV, XML, JSON) to push extracted and processed contract/invoice data into downstream accounting systems (QuickBooks, Xero, SAP, Oracle, NetSuite). The system likely implements standard accounting data formats and field mappings to enable seamless integration without custom development, though specific supported systems and integration depth are unclear from available information.","intents":["Automatically populate extracted invoice data into accounting system without manual entry","Push GL-coded invoice data directly into ERP for posting","Export contract obligation data to contract management or procurement systems","Enable real-time data sync between Tabs and accounting systems"],"best_for":["Enterprises using major accounting/ERP systems (SAP, Oracle, NetSuite, Workday)","Organizations seeking end-to-end automation from invoice receipt to GL posting","Finance teams with technical resources to configure and maintain integrations"],"limitations":["Supported accounting systems unclear — integration may be limited to popular platforms","Integration depth varies — unclear if system supports real-time sync or only batch import","Requires API credentials and access to accounting system — may require IT approval and security review","Custom field mappings may be required for non-standard GL structures or accounting practices","No explicit information on error handling or reconciliation if integration fails"],"requires":["API credentials for target accounting system","Network access to accounting system (VPN or firewall rules)","GL account mapping configuration","Optional: Custom field mapping for non-standard accounting practices"],"input_types":["Extracted and processed contract/invoice data","GL account mappings","Accounting system field schemas"],"output_types":["Accounting system import files (CSV, XML, JSON)","API requests to accounting system","Integration status and error logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["PDF or image files (JPEG, PNG, TIFF) of contracts or invoices","AR-capable device for visualization (iOS/Android with ARKit/ARCore support)","Integration with accounting/ERP system for downstream data population","AR-capable device (iPhone 6s+, iPad 2017+, Android 7.0+ with ARCore support)","Tabs mobile application installed","Physical document or high-resolution digital document image for tracking","Extracted contract text with identified fields","Training data or configuration for domain-specific obligation types","Integration with contract management system for risk tracking","Multiple extracted contracts (minimum 2, typically 5+)"],"failure_modes":["Extraction accuracy likely degrades on highly unusual or proprietary document formats not in training data","No explicit information on handling multi-page document stitching or cross-reference resolution","Requires sufficient document volume to justify AR infrastructure investment","Performance on handwritten or heavily annotated documents unknown","AR requirement adds friction — most finance teams lack AR devices or training","Spatial tracking accuracy depends on document lighting, angle, and camera quality","AR visualization adds latency to extraction workflow (document capture → processing → AR render)","Limited to mobile/tablet AR platforms (iOS ARKit, Android ARCore) — no desktop AR support mentioned","Unclear if AR overlays persist across sessions or require real-time document tracking","Classification accuracy depends on training data — may miss novel or industry-specific obligation types","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.648Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tabs","compare_url":"https://unfragile.ai/compare?artifact=tabs"}},"signature":"2o7JSx75FhHgutUoI6BxFUf52iV1P7LI3Y3lzezS4Gof7ewjjIqGp3bJG2KyijT6vfcH8WBSl9Ma2dVRDeAdDQ==","signedAt":"2026-06-22T12:35:29.990Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tabs","artifact":"https://unfragile.ai/tabs","verify":"https://unfragile.ai/api/v1/verify?slug=tabs","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}