Press release: Accel, Google back Dodge to fix maintain enterprise software
Dodge AI raises $2.65 million to automate enterprise software maintenance
Dodge AI has raised $2.65 million to expand software designed to help companies diagnose and resolve problems in heavily customized enterprise applications, including SAP, Salesforce and Microsoft Dynamics.
Accel and Google’s venture arm led the funding round, Dodge AI said, with New Build Venture Capital, Antler, Schema Ventures and individual investors from the SAP ecosystem also participating.
Large organizations often modify enterprise resource planning and customer-management systems over years of use, creating company-specific rules, configurations and workarounds that can be difficult to trace when systems fail. Dodge AI says its platform connects information from business processes, IT service-management tools and legacy configurations to identify likely causes of incidents and recommend fixes.
The company said it is working with more than a dozen enterprise customers, about half of which are publicly listed companies. Its software handles hundreds of user queries per hour, it said.
Dodge AI cited examples including a warehouse issue involving incorrect Goods Receipt Note information, which it said the platform traced across SAP, Kinaxis and internal warehouse systems. In another case, the company said it helped modernize an inventory-planning process that had been run overnight because of SAP stability problems, cutting processing time by a claimed factor of 132 and improving order allocation by eight hours.
The company aims to use maintenance work as a route into broader enterprise-system modernization. Its approach centers on documenting exceptions and customized business logic that may otherwise be dispersed among support tickets, consultants and internal staff.
“Application maintenance is one of the largest and least modernized categories in enterprise technology,” Accel investor Prayank Swaroop said in a statement. He said the company’s work could create the operational context needed before AI agents can be used more broadly in production systems.