Nimble has launched its Agentic Search Platform, a system designed to transform the public web into trusted, decision-grade data for AI systems and business workflows, boasting 99% accuracy. The launch is supported by $47 million in Series B financing led by Norwest, with participation from Databricks Ventures and others, bringing the company’s total funding to $75 million.
The Agentic Search Platform addresses a fundamental bottleneck in the current AI era: while large language models (LLMs) are becoming more sophisticated, they often reason over incomplete or unverifiable external information. Nimble’s platform aims to eliminate this “guesswork gap” by providing a governed data layer that searches, navigates, and validates live internet data in real time. The core of Nimble’s solution is a proprietary distributed architecture that orchestrates specialized agents to perform tasks traditionally handled by human researchers or brittle web scrapers. According to the company’s infrastructure documentation, the process is broken down into five distinct layers: headless browser and browsing agents, parsing agents, data processing agents, validation agents, and the final step involves verifying the results to ensure accuracy and completeness before delivery.
Nimble co-founder and CEO Uri Knorovich reflected on the early skepticism regarding his vision of a machine-centric internet. “Whenever we started this company, and the first time I went to investors, I told them the web is built for humans, but machines are going to be the first citizens of the web,” Knorovich recalled. He noted that while initial reactions labeled him as “too visionary,” the current reality of AI adoption has validated his thesis. Knorovich points out that the scale of AI interaction with the web is fundamentally different from human behavior. The platform is built to deliver data with greater than 99% accuracy and a latency of 1-2 milliseconds per request. It integrates natively with major data environments, allowing users to stream clean data directly into Databricks, Snowflake, S3, or Microsoft Fabric.
The Agentic Search Platform is delivered through two primary interfaces designed for enterprise scalability: web search agents and web tools SDK. The platform is built to be model-agnostic, working seamlessly with state-of-the-art models from OpenAI, Anthropic, and Google’s Gemini. Nimble differentiates itself from legacy scraping tools through a rigorous focus on governance and trust. The platform is “compliant-by-design,” holding certifications for SOC2 Type II, GDPR, CCPA, and HIPAA. Pricing is structured to support both experimental startups and high-scale enterprise operations, aligned with the volume and depth of data retrieved.
The transition to agentic search has already been operationalized by several Fortune 500 companies and AI-native startups. The $47 million Series B funding announced alongside the platform will be used to accelerate research in multi-agent web search and further develop the governed data layer. Target Global, Square Peg, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures, and InvestInData participated in the round. Andrew Ferguson, VP of Databricks Ventures, noted that Nimble complements their Data Intelligence Platform by providing a “real-time web data layer” that extends workflows beyond internal sources.
The future of the web belongs to programmatic interaction, according to Knorovich. “Programmatic web search is where we are building towards,” he concluded. By moving away from legacy data vendors and brittle scrapers, Nimble aims to provide the real-time structure needed for AI to act with confidence in the real world. With the launch of the Agentic Search Platform, Nimble is poised to revolutionize the way enterprises interact with the web, providing a new paradigm for machine-centric internet search.
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