Nimble Secures $47M to Scale Agentic Web Search Platform for Enterprise AI

Nimble, the enterprise AI startup formerly known as Thhe Data Company Technologies Inc., has secured $47 million in a Series B funding round led by Norwest Venture Partners to scale its agentic web search platform, according to Business Insider. The investment, announced on February 24, 2026, will accelerate the development of its multi-agent research infrastructure and expand its governed real-time web data pipeline — a critical enabler for enterprise-grade artificial intelligence systems that demand accuracy, timeliness, and regulatory compliance.

Founded in 2021, Nimble has positioned itself at the intersection of web crawling, agent-based reasoning, and enterprise data governance. Unlike traditional search engines or static knowledge bases, Nimble’s platform deploys autonomous AI agents that dynamically navigate, verify, and synthesize information from the live web. These agents operate within configurable governance frameworks, ensuring data provenance, bias mitigation, and compliance with industry-specific regulations — a necessity for financial institutions, healthcare providers, and legal firms deploying AI for decision-making.

According to TechTarget, the company’s technology is already being piloted by Fortune 500 enterprises seeking to replace brittle, manually curated knowledge graphs with adaptive, real-time intelligence. One anonymous enterprise client reported a 68% reduction in AI hallucinations during risk assessment workflows after integrating Nimble’s platform, citing its ability to cross-reference live financial filings, regulatory updates, and news sources in near real time.

The funding round also signals growing investor confidence in agentic AI — systems where multiple AI agents collaborate to perform complex tasks, akin to a research team operating autonomously. Nimble’s architecture allows agents to delegate tasks, resolve contradictions, and trace conclusions back to source documents, creating auditable reasoning chains. This is a stark departure from monolithic LLMs that generate responses without transparent sourcing.

While competitors such as Perplexity and You.com focus on consumer-facing search, Nimble targets the enterprise vertical, where data integrity is non-negotiable. The company plans to use the capital to hire engineers specializing in distributed systems, expand its data governance compliance team, and build partnerships with cloud providers to embed its search layer into existing AI stacks on AWS, Azure, and Google Cloud.

Notably, the announcement coincides with broader market trends. In a separate development, ChipAgents raised $74 million to apply agentic AI to semiconductor design, underscoring the expanding application of agent-based systems beyond search — from chip fabrication to enterprise intelligence. However, Nimble’s focus on the open web as a living data source distinguishes it as a foundational layer for AI that must remain current, not just trained on static datasets.

"The live web is the most dynamic and comprehensive knowledge repository on the planet," said Nimble CEO Rachel Lin in a statement. "But for AI to trust it, we must build the infrastructure to verify, contextualize, and govern it. This funding allows us to turn the chaos of the open web into a reliable, auditable data stream for mission-critical AI."

Analysts suggest Nimble’s model could become as essential to enterprise AI as databases were to traditional software. With regulatory scrutiny on AI transparency rising globally, platforms like Nimble may soon be seen not as optional tools, but as compliance necessities.

Investors, including Norwest’s partner David Kim, emphasized the long-term potential: "Nimble isn’t just building a better search engine — it’s building the data layer for the next generation of enterprise AI. The market will demand trustworthy, real-time intelligence, and Nimble is the first to deliver it at scale."

As enterprises race to deploy generative AI responsibly, Nimble’s $47 million infusion marks a pivotal moment in the evolution from static models to dynamic, verifiable, agent-driven intelligence.