Zavvis Financial Monitoring
durch Zavvis
Transaction-level financial observability that detects material changes and traces their causes.
Zavvis is a financial observability engine for corporate finance. Zavvis connects to QuickBooks (more integrations on the way) and analyzes financial activity against the company’s historical behavior. It surfaces material changes, process failures, and control breaks across invoices, bills, payments, expenses, lines, journal entries, customers, vendors, departments, chart of accounts, helping finance teams identify conditions that might otherwise remain hidden until reporting or close.
Each signal includes its financial impact, historical baseline, supporting evidence and traceability to the underlying transactions. Finance users can investigate findings, examine likely root causes, ask follow-up questions and create evidence-based reports.
What you get:
- Automated ingestion and normalization of core QuickBooks Online entities, including invoices, bills, payments, deposits, journal entries, customers, vendors, and accounts.
- A canonical financial data model built on a Bronze–Silver–Gold architecture, preserving lineage from source transactions through calculated metrics and detected signals.
- Prebuilt financial metrics covering revenue and expense trends, accounts-receivable aging and DSO, accounts-payable aging and DPO, cash-basis performance, customer concentration and vendor-cost behavior.
- Governed detection that compares current financial activity with historical baselines and applies eligibility, persistence and materiality controls.
- Evidence-based investigation with financial impact, likely root causes, confidence, recommended actions and links to supporting transactions.
- Scheduled data-processing pipelines that keep financial metrics and signals current as QuickBooks data changes.
- Unity Catalog integration, tenant-aware authorization and access controls for governance and data protection.
- Conversational investigation and report generation to help finance teams move from detection to understanding and action.