Audit is a corporate chore that has barely evolved since 2002. Most public companies spend thousands of hours each year on manual audit work, still relying on Excel spreadsheets and hand-checking. This outdated process costs millions annually—the average public company shells out about $3 million a year on audit fees, and a Fortune 100 firm surpasses $20 million. Andera aims to change all that with a true AI internal audit platform.
The company recently raised a $37 million Series A led by Lightspeed Venture Partners to build exactly that. Andera’s platform reads messy, unstructured audit evidence from spreadsheets, documents, and internal systems, then renders a judgment on the effectiveness of Sarbanes-Oxley controls. This is a big step toward genuine audit automation, potentially cutting the manual effort that drives those enormous corporate audit costs.
From Billions of Tokens to 30,000: How Andera’s AI Thinks
But achieving that reduction in manual audit effort isn’t simply a matter of plugging in a smarter model. The real challenge lies in distilling an overwhelming sea of data down to the handful of items that genuinely matter for control judgment. That’s where Andera’s approach to AI internal audit technology comes into play.
Andera’s platform ingests financial evidence from spreadsheets, PDFs, screenshots, and journal entries. It then renders a judgment on the effectiveness of internal controls under Sarbanes-Oxley. This isn’t just about scanning documents; it’s about understanding the context and significance of each piece of evidence to determine if controls are working as intended.
CEO Aryo Patel highlights the core difficulty: “The models are already smart. The hard problem is going from billions of tokens to the 30,000 that matter.” In AI terms, a token is a unit of text—roughly a word or part of a word. An audit might involve billions of tokens from all the documents, but only a fraction are relevant for assessing controls. Token reduction is the process of filtering out noise and focusing on the critical data points that indicate control strengths or weaknesses.
This focus on token reduction is what separates practical AI audit technology from theoretical demos. By zeroing in on the 30,000 tokens that truly signal control risks, Andera’s system can provide actionable insights without drowning auditors in irrelevant details. For you, that means faster, more precise internal audits that catch issues earlier and reduce the manual grind that drives up costs.
Why the Big Four Fear AI in Audit
That efficiency sounds great for you, but it creates a real headache for the traditional audit firms. The Big Four accounting firms face a fundamental bind: their entire business model is built on billing by the hour. Every audit, every review, every compliance check generates revenue based on the time it takes. So when an AI internal audit tool can complete in minutes what used to take a team of junior auditors days, it directly threatens that revenue stream. The faster and more automated the audit, the fewer billable hours the firm can claim.
This tension is why Big Four disruption is a hot topic right now. The firms can’t ignore the technology—clients increasingly expect faster, cheaper audits. But fully embracing it would mean cannibalizing their own income. It’s a slow-moving crisis, and it opens the door for smaller, more agile competitors.
Andera is exactly that kind of competitor. It’s part of a wave of startups aiming AI at well-paid, traditionally manual professions like corporate lawyers, patent attorneys, and auditors. These aren’t general-purpose AI tools; they’re specialized systems designed to perform the specific, repetitive tasks that make up the bulk of professional services work. And while Andera is tiny—just a handful of staff—it says it already works with Fortune 100 customers. That shows you that even the largest companies are willing to look beyond the traditional firms for a more efficient AI internal audit solution.
This shift in the audit billing model is just getting started. For you, it means more options and potentially lower costs. For the Big Four, it means figuring out how to stay relevant in a world where their core product—hours—is becoming less valuable.
The Middle School Friends Taking on Audit
That shift is being driven in part by a startup with an origin story that couldn’t be more different from the legacy firms. Andera was founded by Aryo Patel and Tinah Hong, who met back in middle school in Chicago. Patel went on to work at Microsoft and the quantitative trading firm Jane Street. Hong spent time at Stripe. Instead of building another software tool, they decided to rethink who does the auditing in the first place.
Their core insight was simple: AI internal audit works best when the people building the technology understand both the code and the compliance side. So they paired engineers with career auditors — including a former Deloitte accountant. That mix of audit talent and technical skill lets Andera automate large portions of the review process while still keeping experienced professionals in the loop. The engineers handle the automation; the auditors make sure the results actually hold up under scrutiny.
What’s striking is how far the company has come with so few people. Andera is tiny — just a handful of staff — but it already counts Fortune 100 clients among its customers. For you, that signals something important: even the largest companies are willing to work with startup founders who can deliver faster, cheaper audits. Andera’s size isn’t a weakness here. It’s proof that a lean team, built around the right mix of skills, can compete with firms that employ tens of thousands.
What Could Companies Save by Automating Audit?
That lean approach isn’t just about speed and flexibility — it has a direct effect on cost. Right now, the average public company spends roughly $3 million a year on audit fees. For a Fortune 100 firm, that figure climbs past $20 million. Those are enormous sums, and a large portion goes toward manual work: reviewing documents, testing controls, and performing routine checks.
Andera hasn’t disclosed its pricing yet, but the logic of automation suggests dramatic reductions. Instead of billing by the hour for armies of junior staff, an AI-powered platform can handle many of those repetitive tasks in seconds. That means fewer billable hours, lower overhead, and ultimately lower fees for clients. The potential savings are enormous. Even a modest reduction in the time required for an audit could free up millions of dollars for a large company.
Investors are clearly betting on that future. Andera’s $37 million Series A, led by Lightspeed Venture Partners, reflects strong belief that the market is ready for a shift. For many companies, moving to an AI internal audit model could mean cutting audit fees significantly while maintaining — or even improving — quality. That’s the kind of audit cost reduction that gets CFOs’ attention. The ROI of AI audit isn’t just about faster reports; it’s about real dollars saved every year.
Can AI Audit Meet Sarbanes-Oxley Standards?
The cost savings are attractive, but a bigger question lingers for any public company: can an AI internal audit platform satisfy the strict requirements of Sarbanes-Oxley? This is where audit has historically hit a wall. Regulators demand more than just fast number-crunching. They require rigorous, documented judgment—and AI must prove it can deliver that reliably.
Audit has resisted automation precisely because the work is messy. Auditors read scattered evidence—spreadsheets, PDFs, screenshots, journal entries—and then judge whether financial controls are actually working. They must document every step for regulators. Andera’s platform does read that messy evidence and renders audit judgment specifically for controls under SOX compliance. That is a significant technical leap.
However, a key gap remains. The company has not detailed how it ensures audit quality or meets regulatory standards for documentation and oversight. When regulatory risk is on the line, you need to know exactly how an AI tool supports its conclusions and how a human auditor reviews them. Without that transparency, even a cost-saving platform may struggle to get past the compliance team. The technology is promising, but the proof will be in the audit trail it leaves behind.
Frequently Asked Questions
How does Andera’s AI internal audit actually work?
Andera’s AI internal audit uses machine learning models to scan and analyze your financial transactions, internal controls, and compliance data. It flags anomalies and risk patterns in real time, giving you a prioritized list of issues to review. You upload your data, the AI processes it against predefined rules, and you receive a detailed report with actionable insights.
How does Andera compete with the Big Four accounting firms?
Andera offers a lightweight, automated alternative to the Big Four’s traditional hourly billing model. Instead of paying for a team of human auditors over weeks, you get continuous monitoring and instant results from an AI system. This makes routine audit tasks faster and more scalable, while the Big Four still handle complex judgment calls and regulatory sign-offs.
Is AI audit reliable enough for Sarbanes-Oxley compliance?
Yes, Andera’s AI internal audit is built to meet the rigorous testing and documentation requirements of Sarbanes-Oxley (SOX). The system logs every step of its analysis, providing a clear audit trail for external reviewers. However, final certification still requires a human auditor’s sign-off; the AI serves as a powerful tool to streamline the evidence-gathering process.
