Tabs

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.

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.

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.

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.

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.

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.

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).

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.