ORO Labs closed a $100 million Series C led by Goldman Sachs Growth Equity and Brighton Park Capital to accelerate agentic procurement orchestration for global enterprises. The round, which brings total funding to $160 million, follows 300% revenue growth as Fortune 500 companies deploy ORO’s AI-powered platform to transform procurement workflows that currently involve 20 million manual touchpoints annually at large enterprises.

The funding validates a fundamental shift in enterprise software architecture: from systems that generate data to systems that orchestrate intelligent action. With procurement representing $13 trillion in annual global spend but remaining largely manual and fragmented, ORO’s platform addresses what CEO Sudhir Bhojwani calls the “orchestration bottleneck” - the gap between powerful enterprise systems and the intelligent workflows needed to operate them at machine speed.

Armadin emerged from stealth with $189.9 million in combined Seed and Series A funding—the largest early-stage cybersecurity round in history. Led by cybersecurity veteran Kevin Mandia, the company is building what it calls “agentic attacker swarms” to counter AI-powered “hyperattacks” that move faster than human defenders can respond.

The timing reflects a fundamental shift in enterprise security thinking. As AI-powered threats evolve to operate at machine speed, traditional human-led cybersecurity approaches are creating a dangerous response gap. Armadin’s infrastructure addresses this by deploying autonomous AI agents that think, plan, and adapt like nation-state attackers—24/7, across every network endpoint.

Eridu just raised over $200 million in Series A funding—an unusually large round that signals serious investor conviction that AI’s explosive growth has created a fundamental infrastructure crisis. The networking startup, led by serial entrepreneur Drew Perkins, emerged from stealth with backing from legendary investor John Doerr at Kleiner Perkins to tackle what the company calls the “network wall” throttling AI data centers.

The funding round’s size reflects both the capital intensity of hardware development and recognition that current data center networking wasn’t designed for AI’s massive communication demands. As training clusters scale to thousands of GPUs and models grow larger, the networks connecting them have become critical bottlenecks that billions in AI infrastructure investment can’t solve with software alone.