Nimble is building the live web data layer that enterprise AI agents need to actually work in production. As enterprises put AI agents to work on competitor analysis, pricing research, KYC, brand monitoring, deep research, and financial analysis, they are running into the same structural problem: models are good at searching the web, but they return results in plain text, pull from unreliable sources, and hallucinate on the way. The failure point is rarely the model itself. It is the data feeding the model. Nimble's platform uses AI agents to search the web in real time, verify and validate the results, and structure them into clean tables that behave like any other dataset in the enterprise. The company integrates directly with Databricks, Snowflake, AWS, and Microsoft, so customers can query live web data alongside their internal data and keep everything within their existing data infrastructure. Once connected, Nimble also remembers constraints (which sources to trust, how a given search should be run) so agents can operate inside governed boundaries rather than open-ended ones.

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The company is led by CEO and co-founder Uri Knorovich, and the team has built Nimble around a thesis that is increasingly hard to argue with: most production AI failures are data failures, and trusted live web data is becoming a prerequisite for AI agents making real business decisions. That thesis is playing out in the customer base. Nimble has over 100 customers, the majority of its revenue comes from large enterprises, and its roster spans Fortune 500 and Fortune 10 companies including major retailers, hedge funds, banks, and consumer packaged goods companies, alongside AI-native startups.

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