Nimble raised $47 million in Series B funding led by Norwest to solve a critical bottleneck in enterprise AI agent deployment: reliable access to real-time, structured web data that agents can actually trust for business decisions.

The New York-based company addresses what CEO Uri Knorovich calls the core enterprise AI failure mode: “Most production AI fails aren’t because the models are not good enough — it’s because of a data failure.” While AI agents excel at web search and analysis, they typically return unstructured text prone to hallucinations and unreliable sourcing, creating an insurmountable trust gap for enterprise deployment.

Potpie AI raised $2.2 million to build structured context layers that allow AI agents to operate effectively across enterprise-scale codebases exceeding 40 million lines of code.

The infrastructure gap isn’t computational power—it’s organizational memory. While large language models excel at code generation, they struggle to maintain context across complex, interconnected systems where critical knowledge lives in senior engineers’ heads and context spans dozens of tools and millions of lines of legacy code.

Deepfake-related fraud caused $1.56 billion in losses in 2025 alone, with generative AI predicted to enable up to $40 billion in US fraud losses by 2027. As AI agents increasingly interact with multimedia content across enterprise workflows, synthetic content detection has become a critical infrastructure bottleneck blocking secure autonomous deployment.

Resemble AI’s $13 million strategic funding round, backed by Google’s AI Futures Fund, Sony Innovation Fund, and Okta Ventures, signals enterprise recognition that real-time deepfake detection infrastructure is no longer optional—it’s foundational for production AI agent security.