Beyond Spreadsheets: Rillet's $70M Series B Signals the AI-Native ERP Revolution
AI Agent News
Traditional enterprise accounting still operates on spreadsheet-era assumptions, with finance teams waiting weeks for critical business metrics while managing complex revenue models across multiple entities. Rillet, an AI-native ERP platform built specifically for modern finance operations, just raised $70 million in Series B funding led by Andreessen Horowitz and ICONIQ, reaching a $500 million valuation after doubling its ARR in just 12 weeks.
This isn’t incremental software improvement—it’s evidence that enterprises are ready to replace 20th-century accounting infrastructure with AI-native systems designed for speed, automation, and real-time insights.
The Financial Operations Bottleneck
Most enterprise software discussions focus on customer-facing AI applications, but the back-office infrastructure powering these businesses remains stuck in the past. Finance teams at scaling companies face a familiar pattern: spreadsheet-heavy workflows, manual data entry across disconnected systems, and financial closes that stretch for weeks while business decisions wait for basic metrics.
Rillet CEO Nicolas Kopp experienced this firsthand as US CEO of N26: “My finance team was world-class, but simple requests took weeks because the systems were stuck in the past.” The problem isn’t personnel—it’s infrastructure designed for a different era of business complexity.
Traditional ERP systems were built for predictable, linear business models. Today’s enterprises manage subscription revenue, usage-based pricing, multi-entity structures, and rapid international expansion. Legacy platforms respond by adding more customization layers, third-party integrations, and manual workarounds that create the very bottlenecks they’re meant to solve.
AI-Native Architecture vs. Legacy Patches
Rillet’s approach reveals why retrofit AI solutions struggle with enterprise finance operations. Instead of adding AI features to traditional ERP architecture, they’ve built an AI-native platform where automation and intelligence are embedded at the infrastructure level.
The technical distinction matters for enterprise deployment. Traditional accounting platforms depend on external data sources, manual reconciliation, and batch processing that creates lag between business events and financial visibility. Rillet’s AI-native architecture processes transactions in real-time, automatically categorizes complex revenue streams, and maintains a single source of truth across multi-entity structures.
This infrastructure shift enables new operational patterns: Postscript, which generates over $100 million in ARR globally, now closes its books in just three days using Rillet’s platform. Windsurf operates its entire finance function with only two team members, handling complexities that would traditionally require larger finance organizations.
Enterprise Validation and Market Shift
The Series B follows a $25 million Series A round just ten weeks prior, bringing Rillet’s total funding to over $100 million in under twelve months. More significant than the funding pace is the adoption trajectory: over 200 customers since launch, with the platform credited with halving financial close times across diverse enterprise segments.
Partnerships with top-tier accounting firms including Armanino and Wiss signal broader industry recognition. These firms see AI-native ERP as infrastructure evolution, not software replacement—enabling their expertise to focus on strategic analysis rather than data manipulation.
The market timing reflects regulatory and operational pressures. Public companies face increasing disclosure requirements while managing more complex business models. Private companies preparing for eventual public market readiness need financial infrastructure that scales without exponential headcount growth.
The Collaborative AI Operations Model
Rillet’s roadmap extends beyond traditional ERP replacement toward what they call “collaborative platforms where human experts and AI agents work side-by-side.” This represents the next phase of enterprise AI deployment: not replacing human expertise, but augmenting it with AI-native infrastructure that handles routine operations at machine speed.
The approach aligns with broader enterprise AI adoption patterns. Rather than deploying general-purpose AI agents across business operations, leading enterprises are investing in purpose-built AI infrastructure for specific operational domains—finance, security, data operations—where automation provides clear ROI and risk profiles.
Looking Forward: The Next 12 Months
As enterprises recognize AI’s potential beyond customer-facing applications, the infrastructure layer supporting these deployments becomes critical. Rillet’s rapid growth trajectory suggests that financial operations represent an early proving ground for enterprise AI infrastructure adoption.
The next six to twelve months will likely reveal whether other operational domains—HR, legal, procurement—follow similar AI-native infrastructure patterns, or if financial operations represent unique requirements that drove early platform development.
The shift from traditional to AI-native enterprise infrastructure reflects broader changes in how businesses operate and scale. As companies deploy AI agents for customer-facing applications, the underlying operational infrastructure must evolve to support both automated decision-making and human oversight at enterprise scale.
Platforms like Overclock provide the orchestration layer that connects AI-native operational systems with broader enterprise workflows, enabling organizations to coordinate automated processes across multiple domains while maintaining the control and visibility required for enterprise deployment.