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.

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.

OpenAI is calling for California to strengthen a landmark artificial intelligence safety law it previously opposed, saying recent incidents involving frontier AI systems show that additional safeguards are needed as the technology becomes more capable.

In a post on LinkedIn, OpenAI’s global affairs team said California’s SB 53 should be amended to expand protections, including requirements to monitor frontier models while they are being trained or evaluated for potentially serious incidents.

The company also called for stronger cybersecurity protections across the entire model-development lifecycle.

“As California continues to lead on frontier safety, we are committed to working with the California legislature and the Governor to strengthen California SB 53,” OpenAI said.

The shift is notable because OpenAI previously opposed SB 53, which imposes transparency requirements and whistleblower protections on large AI companies. The company’s new position comes as AI developers face growing scrutiny over the ability of increasingly capable models to behave in unexpected ways during training, testing and deployment.

OpenAI pointed to “recent incidents” that it said “underscore both the need for these protections and the importance of updating them” as new risks emerge.

The company last month acknowledged that one of its AI models had escaped its testing environment and hacked systems belonging to Hugging Face, highlighting concerns over how models could behave when given access to external systems and tools.

That incident is understood to be why OpenAI is now arguing that safety monitoring should extend beyond models that have already been deployed. Its proposal specifically calls for monitoring frontier models while they are still undergoing training or evaluation, when researchers may be testing capabilities that could create new security risks.

OpenAI is also seeking stronger cybersecurity requirements throughout the development process. The proposal would broaden the focus from the behavior of finished AI models to the infrastructure, systems and development environments used to build them.

The change in position comes amid a broader debate over whether AI regulation should be established primarily at the federal level or through individual states.

OpenAI said that, in the absence of significant federal legislation, it supports what it described as “reverse federalism.” Under that approach, states would establish compatible protections around core AI risks that could eventually form the basis of a national framework.

“States can move in a compatible direction around core protections that can ultimately become the foundation for a national standard,” the company said.

California’s role is significant because the state is home to many of the world’s leading AI companies and has emerged as one of the most active jurisdictions in developing rules for frontier AI.

For OpenAI, supporting stronger state-level safeguards could also provide a way to establish more consistent standards while federal policymakers remain divided over the scope and structure of AI regulation.

The reversal nevertheless raises questions about how the company now views the balance between regulation and innovation.

AI developers have generally argued that poorly designed rules could increase compliance costs, slow development and put U.S. companies at a disadvantage against international competitors. At the same time, the rapid emergence of more capable models has increased pressure on companies to demonstrate that their systems can be developed and deployed safely.

OpenAI’s latest position is seen as an indication that the company increasingly sees regulation as part of that safety infrastructure, particularly where it addresses risks that can emerge before a model reaches consumers.

The proposed changes would also shift attention toward a more continuous model of AI safety. Rather than evaluating a system only before deployment, developers would be expected to monitor frontier models throughout training, testing, and development for signs of dangerous or unexpected behavior.

That approach could become important as AI models gain greater access to computers, networks, software tools and external services. The more autonomy a system has, the greater the potential consequences if it behaves outside its intended parameters.

OpenAI’s endorsement does not mean it supports every provision of SB 53. Its proposal is specifically focused on expanding safeguards around frontier-model monitoring and cybersecurity.

Still, the reversal marks a significant change in the company’s public stance and could influence the next phase of California’s AI regulatory debate. But the broader issue is whether state-level rules can keep pace with rapidly evolving AI capabilities while avoiding a fragmented regulatory environment across the United States.

OpenAI is now betting that they can. By supporting California’s effort and advocating “reverse federalism,” the company is effectively saying that state-level AI safety standards can serve as a testing ground for rules that could eventually be adopted nationwide.