FirmOS

Automatically classifies incoming accounting documents (invoices, receipts, bank statements, tax forms) using computer vision and OCR, then extracts structured financial data (amounts, dates, vendor info, line items) via pattern recognition and entity extraction models. Routes classified documents to appropriate workflow queues based on document type and content analysis.

Converts extracted transaction data into properly formatted journal entries following double-entry bookkeeping principles, then automatically posts entries to the firm's accounting system via API integration. Uses rule engines and templates to map transaction types to appropriate GL accounts based on firm-specific chart of accounts configuration.

Compares extracted transaction data against client-provided records or bank feeds to identify discrepancies, missing transactions, and reconciliation issues. Uses fuzzy matching on amounts and dates to handle timing differences, then flags unmatched items for investigation. Generates reconciliation reports showing matched vs unmatched transactions with variance explanations.

Manages parallel processing of accounting tasks across multiple client engagements, routing documents and transactions through appropriate workflows based on client type, engagement scope, and service level. Implements queue-based task distribution with priority handling, SLA tracking, and workload balancing across firm staff. Integrates with firm's resource management to assign tasks to appropriate team members based on skills and availability.

Maintains comprehensive audit logs of all automated transactions, data modifications, and system actions with immutable timestamps, user attribution, and change details. Implements role-based access controls to ensure only authorized personnel can review sensitive data or approve automated actions. Generates compliance reports for regulatory requirements (SOX, HIPAA, state accounting board rules) and internal audit procedures.

Identifies transactions or documents that don't match expected patterns or fail validation rules, then automatically escalates them to appropriate team members with context and suggested resolutions. Uses machine learning to learn from past exceptions and improve detection accuracy over time. Implements escalation workflows with priority levels and timeout-based re-escalation if not resolved.

Provides web-based portal where clients can upload documents, submit transaction data, and track processing status without direct firm interaction. Implements document validation on upload to catch errors early, provides real-time processing status updates, and enables clients to view extracted data and reconciliation results. Integrates with firm's accounting system to pull client-specific data for portal display.

Provides pre-built connectors to major accounting platforms (QuickBooks Online, Xero, NetSuite, Sage, Wave) and banking APIs (Plaid, Yodlee, direct bank connections) to pull transaction data, GL balances, and client information, and to post journal entries and reconciliation results. Handles authentication, data transformation, and error handling for each platform's specific API requirements.

Provides real-time dashboards showing automation metrics (documents processed, entries posted, exceptions flagged), firm performance indicators (utilization rates, SLA compliance, profitability by engagement), and client-specific analytics (transaction volumes, data quality scores, reconciliation status). Implements drill-down capabilities to investigate specific transactions or engagements, with export functionality for reporting.

Enables firm to configure client-specific rules, GL account mappings, document templates, and workflow variations without code changes. Implements configuration UI for non-technical staff to define rules, with validation to ensure configurations don't break downstream processes. Supports versioning and rollback of configurations, with audit logging of all changes.