Nominal

Agentic AI that runs accounting for mid-market and enterprise

Nominal is a New York-based AI-native ERP platform that positions itself as a “system of intelligence” rather than a system of record, plugging into existing ERPs via a shadow general ledger without requiring migration. Founded by the team behind Cognigo and backed by Next47, Workday Ventures, and others ($30M raised as of July 2025), Nominal’s agents operate inside close management, transaction matching, and reporting workflows to autonomously detect errors, match transactions, generate journal entries, and surface exceptions — working on what’s already in the general ledger rather than only on the intake side of the workflow. It targets controllers and CFOs at mid-market to enterprise companies, including multi-entity and multi-currency operations. Customers publicly referenced include Jiffy Lube and GoGlobal Travel.

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Key Features

Shadow general ledger: Works alongside existing ERPs (NetSuite, QuickBooks, SAP) without migration or data disruption, adding an intelligence layer to the existing system of record.
Transaction Patrol (anomaly detection): AI agents continuously monitor the general ledger, detect misclassifications and unmatched transactions, and generate corrective recommendations before books are closed.
Transaction Matching Agent: Matches transactions across multiple ERPs and data sources simultaneously, normalizes account codes, and flags discrepancies rather than requiring manual reconciliation.
Close management with AI task agents: Embedded agents within the month-end close workflow that automate data gathering, draft entries, and surface exceptions — with controllers in a reviewer role rather than a data-prep role.
Multi-entity consolidation: Automates intercompany eliminations, proposes adjustment entries, and accelerates consolidation across complex entity structures.
Natural language financial queries: Controllers can describe business logic in plain language (e.g. “match inventory debits and credits for the same product”), and agents interpret intent rather than require rigid rule configuration.

Use Cases

For mid-market companies bridging the ERP gap: A manufacturer or services company running on an aging ERP layers Nominal on top to automate reconciliation and close without a costly ERP replacement project.
For multi-entity close acceleration: A company with subsidiaries across multiple currencies uses Nominal’s consolidation agents to automate intercompany eliminations and compress the group close cycle.
For controllers scaling without headcount: A finance team with growing transaction volume deploys Nominal to automate the first pass of reconciliation and exception detection, redirecting staff from data prep to analysis.