Zenskar Raises $15 Million for Agentic-Powered Revenue Automation

Billing and revenue automation platform Zenskar has raised $15 million in new funding.

The company’s Series A round, announced Friday (April 17), will allow it to expand its agentic artificial intelligence (AI) capabilities, including its Agents Marketplace. Zenskar describes this as a library of agents that finance teams can “create, customize, chain and deploy across the order-to-cash cycle” with no need for engineering involvement.

“Finance teams aren’t struggling because they lack AI tools. They’re struggling because the systems underneath those tools were built for a simpler world,” Apurv Bansal, Zenskar’s co-founder and CEO, said in a news release.

“Bolting AI onto these broken foundations preserves their limitations, so we built an entirely new architecture, one that can truly free finance from their operational grunt work so they can focus on strategic work.”

The release added that, thanks to things like usage-based tiers, prepaid credits, multiple entities and multiple currencies, workarounds can lead to escalating risks, such as revenue leakage, delayed collections and audit and compliance concerns.

“Finance teams have lagged in AI adoption because accuracy, auditability, and compliance are non-negotiable,” said Sai Araveti, investment advisor for Susquehanna Venture Capital, which led the Series A.

“Legacy systems treat complexity as an exception, not the norm — forcing teams into costly, error-prone workarounds and making any AI built on top of them unreliable. Zenskar’s flexible foundation is what lets AI deliver on its promise.”

PYMNTS wrote last week about the benefits finance teams can realize when they automate their operations via AI, as well as the places where this practice still needs some work.

Research from PYMNTS Intelligence has shown that 83% of companies have yet to fully automate their accounts receivable operations, with data fragmentation a key limitation.

Companies manage an average of three ERP systems, leading to data silos that make it tough to get a unified view of customer behavior, payment history and dispute patterns, Billtrust Chief Product Officer Lee An Schommer said in a recent interview.

“Without consolidated data, predictive models produce unreliable outputs. The intelligence layer is only as useful as the infrastructure beneath it,” the report said.

Among North American mid-market firms, U.S. companies are still relying more heavily on traditional payment methods than their Canadian counterparts, and where card acceptance is more broadly used as a receivables strategy, revenue losses tend to be lower, research from PYMNTS and Visa has shown.

“The gap is driven not by customer behavior but by firm-level choices about payment infrastructure,” the report added.