Nimble raises $47M to give AI agents access to real-time web data

Nimble, a New York-based web search startup, has secured $47 million in Series B funding led by Norwest Venture Partners to solve a critical data problem for enterprise AI. The company's core platform employs AI agents that perform real-time web searches, verify and validate the information they find, and crucially, structure the results into neat, queryable tables. This structured output is designed to plug directly into enterprise data environments like Databricks, Snowflake, AWS, and Microsoft Azure, allowing live web data to be treated as an extension of a company's internal database.

The technical approach addresses major pain points in production AI: unreliable plain-text outputs, hallucinations, and the difficulty of using web-sourced data at scale. By controlling what agents can search and structuring the results, Nimble aims to increase trust in AI systems. CEO Uri Knorovich emphasized that most AI failures stem from data issues, not model capability, positioning reliable web search as a prerequisite for broader enterprise AI adoption. The platform also remembers search constraints and integrates with customer data to provide context, keeping all data within the customer's own infrastructure for security and compliance.

With over 100 customers, including Fortune 500 retailers, hedge funds, and banks, Nimble is targeting high-stakes use cases like competitor analysis, pricing research, KYC processes, and financial analysis. The funding, which included participation from Databricks, will help streamline deployments. The startup's focus on turning the dynamic web into a structured, trustworthy data source represents a significant shift in how enterprises can leverage external information for AI-driven decision making.