Rillet has reached a $1 billion valuation after raising $100 million in a Series C round, giving the AI-native accounting startup fresh capital as it targets the legacy enterprise software market and seeks to automate increasingly complex finance functions.
The New York-based company, which emerged from stealth two years ago, said the financing came together in just 48 hours after its management team shared its latest growth figures with investors. Rillet was not actively seeking new funding when the round was initiated, co-founder and CEO Nicolas Kopp said.
The latest financing brings Rillet’s total funding to $200 million from investors including Iconiq, Andreessen Horowitz and Sequoia Capital. Iconiq led the latest round, with general partner Seth Pierrepont joining Rillet’s board.
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The rapid fundraising reflects investors’ growing interest in AI-native enterprise software that can challenge established platforms rather than simply add AI features to existing products.
TechCrunch reported Rillet saying it now has about 600 customers, many of which are replacing legacy enterprise resource planning and accounting systems rather than merely experimenting with Rillet alongside their existing software.
Kopp said customers are removing systems from Oracle, NetSuite, Intuit and other established providers and replacing them with Rillet. About 50% of Rillet’s customers previously used Intuit products, 30% came from NetSuite and Sage Intacct, while the remaining 20% came from Oracle, SAP, Workday and Microsoft products.
The company’s growth has accelerated sharply since its $70 million Series B last summer. At a recent board meeting, Rillet showed investors that its annualized revenue rate had doubled in the latest quarter. The company has also added new customers, including several public companies, and formed an alliance with EY to introduce AI tools to the global auditing firm.
For investors, the significance of the business extends beyond accounting software.
“Rillet’s initial wedge is accounting, but ultimately they are reinventing the entire finance function,” Julien Bek, Sequoia’s lead investor on the deal, told TechCrunch. He said agentic finance could become “one of the largest application software opportunities of the AI era.”
The company’s strategy is based on building accounting software around AI agents rather than adapting conventional software designed primarily for human users. Those agents can perform bookkeeping and other multi-step financial workflows while employees supervise the process. Rillet’s customers range from small businesses such as laundromats to a major sports franchise, according to Kopp.
That approach puts Rillet among a growing group of AI-native startups seeking to challenge established enterprise software companies whose products have dominated corporate workflows for decades.
Kopp argues that generative AI is creating a fundamental opening for those challengers because businesses now have alternatives to software architectures built around human operators.
“AI is going to come hard at these legacy players,” Kopp said, arguing that the technology is giving customers compelling alternatives.
The threat would have much bearing on incumbent enterprise software vendors because accounting and ERP systems are deeply embedded in corporate operations. Companies typically rely on these systems for financial records, reporting, payroll, procurement and other critical functions, making them difficult and expensive to replace.
Rillet’s ability to persuade customers to remove incumbent systems rather than simply add another software layer is therefore an important measure of its competitive position. Security and governance are central to that proposition because accounting systems contain some of the most sensitive information held by companies.
Rillet has built model-routing capabilities that allow customers to direct AI requests to the underlying model provider of their choice, including OpenAI or Anthropic. Kopp said Rillet’s system prevents those foundation models from training on customers’ data.
The company also maintains separate customer data environments, meaning information from one customer is not used to train or improve the system for another customer. Its AI agents can retain historical information about actions they have taken, allowing them to use previous decisions and workflows in subsequent processes. That capability creates another challenge: the more autonomy an AI agent receives, the more important it becomes for companies to understand and audit what the system is doing.
Rillet introduced a governance feature about three months ago that allows accountants to review and audit individual decisions made by its AI agents. Users can see the numbers an agent used and how it arrived at its calculations. Building that system required Rillet to convert large amounts of information generated during agentic workflows into a format that human accountants could understand, Kopp said.
The need for such oversight has become more pressing as AI agents have improved. Newer systems can execute multi-step workflows over longer periods, increasing their usefulness but also creating more opportunities for errors to propagate through a financial process.
For public companies, the regulatory environment remains another constraint on automation. Kopp said current rules require transactions made by AI agents to receive human approval, limiting the extent to which companies can delegate financial decisions entirely to autonomous systems.
He expects regulators to gradually adapt as businesses and auditors become more familiar with agentic technology.
“It’s a very normal process,” Kopp said. “Similar to when the cloud came, of just getting everybody familiar with what’s going on and how it helps the profession.”
The technology is arriving at a time when the accounting profession is already facing a structural labor shortage. The number of people graduating with accounting degrees in the United States has been declining since at least 2010, while employers have struggled to recruit qualified finance and accounting professionals. The Controllers Council Organization has reported that 61% of finance leaders struggled to find finance, accounting, and CPA talent during the past year.
The shortage reflects several longstanding problems in the profession, including long working hours, demanding career paths and compensation that some workers consider inadequate relative to the workload.
That labor shortage could strengthen the business case for AI accounting systems. Instead of eliminating the need for accountants, companies can use AI to handle repetitive bookkeeping and data-processing tasks, allowing professionals to concentrate on analysis, financial planning and advising management.
The U.S. Bureau of Labor Statistics expects employment in accounting and auditing to grow 5% through 2034, with about 72,800 additional jobs projected over the period. The agency has also said AI-driven automation is unlikely to eliminate demand for accountants, explaining that automating routine work such as data entry should allow accountants to spend more time on advisory and analytical responsibilities.
Kopp takes a similar view.
“I just don’t see people losing their job anytime soon,” he said, noting that accountants enter the profession to help businesses make better financial decisions and that AI can enable them to focus more heavily on that role.
The broader implications for enterprise software could be significant if Rillet’s model proves scalable.
For decades, ERP and accounting vendors benefited from high switching costs, complex implementations, and the difficulty companies faced in replacing systems that sit at the center of their financial operations. AI-native platforms are now attempting to challenge that model by offering software built around autonomous agents from the outset.
The risk for startups is that established vendors have enormous customer bases, financial resources, and access to the same rapidly advancing AI models. Oracle, Intuit, Microsoft, SAP and other incumbents can integrate agentic capabilities into products that companies already use, potentially reducing the incentive to switch.
Rillet’s response is to argue that AI-native architecture gives it an advantage that cannot easily be reproduced by adding AI features to older systems.