Nimble raises $47M to bring structured web data to AI agents

Nimble, a web search startup, has raised a $47 million Series B round—a stage of growth-stage funding—led by venture capital firm Norwest. The investment brings the company’s total funding to $75 million. Returning investors in the round included Target Global, Square Peg, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures, and InvestInData, with participation also coming from partner Databricks.

Based in New York, Nimble uses AI agents—autonomous software programs—to search the web in real time. The platform verifies and validates these search results, structuring the information into tables. This structured format allows enterprises to query the web data like a database. The startup integrates this data directly into enterprise data warehouses and data lakes, which are centralized repositories used by businesses to store and analyze data. To streamline these deployments, Nimble has partnered with technology providers, including Databricks, Snowflake, AWS, and Microsoft.

The integration addresses a core challenge in enterprise artificial intelligence: the quality of the data feeding the models. While large language models are capable of analyzing information, they often return web search results in unstructured plain text, which can lead to hallucinations or the use of unreliable sources. Uri Knorovich, CEO and co-founder of Nimble, argues that the primary bottleneck for enterprise AI is not the capability of the models themselves, but the reliability of the data they access. “Models can do a lot of things, but most production AI fails aren’t because the models are not good enough — it’s because of a data failure,” Knorovich said. According to Knorovich, businesses do not need more AI, but rather AI equipped with reliable web search that operates within defined constraints.

Nimble currently serves more than 100 customers. Its client base includes large enterprises, Fortune 500 companies, hedge funds, and banks. Assaf Harel, a partner at Norwest, stated that the startup is addressing a long-standing problem that has lacked a proper solution and is now becoming critically urgent. The startup plans to use the newly secured capital to expand its research and development in multi-agent web search and to build a governed data layer designed to process and validate search results.

Why it matters

Enterprises are struggling with production AI failures caused by poor data quality rather than model limitations. Nimble’s ability to provide reliable, structured, real-time web data provides a critical infrastructure layer that allows businesses to trust and deploy autonomous software for critical decisions.