Believe it or not, web search is still a thriving industry. As businesses invest in AI agents to leverage their data, there is growing demand for tools that do more than just scrape the web to inform those AI bots. Companies also need those results delivered in a way that is easier to use with modern data tools.
This is the promise behind web search startup Nimble, which recently raised a 47 million dollar Series B round led by Norwest. The New York company’s platform uses AI agents to search the web in real time, verify and validate the results, and then structure the information into neat tables that can be queried like a database.
That final step is crucial. Large language models and AI agents are excellent at searching the web, connecting results from various sources, and analyzing them. However, they often return results in plain text, which can be difficult to work with at an enterprise level. This is before even considering issues like hallucinations, the risk of the agent misunderstanding instructions, or the use of unreliable sources.
By validating and structuring results into tables, Nimble allows companies to use web data as if it were already part of their existing databases. The startup also integrates with enterprise data warehouses and data lakes, such as those offered by Databricks and Snowflake. This means its AI agents can connect to a business’s internal data trove, using it to build context and shape how search results are structured and returned.
In effect, this gives enterprises live, structured web data as part of their existing data environments. Such integrations also allow Nimble’s software to remember constraints, such as how a search should be performed or which data sources to use. This is particularly useful for applications like competitor analysis, pricing research, know-your-customer processes, brand monitoring, deep research, and financial analysis. Nimble works to ensure all customer data remains within customers’ own data infrastructure to comply with data retention and security policies.
To that end, the startup has partnered with Databricks, Snowflake, AWS, and Microsoft to help streamline enterprise deployments that require access to internal data sources. Databricks also participated in this Series B round.
The CEO stated that while models can do many things, most production AI failures are not due to poor models but to data failures. He emphasized that enterprises today do not necessarily need more AI; they need AI with good, reliable web search. The ability to choose what an agent can and cannot search is a tipping point for building trust and deploying AI in more use cases.
He says the ability to search the web in real time at scale, and to validate and structure search results, is what sets Nimble apart from other data brokers in the space. The startup currently has more than one hundred customers, with the majority of its revenue coming from large enterprises, Fortune 500 companies, and even some Fortune 10 companies. These include major retailers, hedge funds, banks, consumer packaged goods companies, and some AI-native startups.
A partner at Norwest said that Nimble is tackling a problem that has existed for years without a proper solution and is now becoming critically urgent. Trusted live web data is increasingly a prerequisite for AI agents performing critical business decisions.
The Series B also saw participation from returning investors Target Global, Square Peg, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures, and InvestInData. Proceeds from the round will be used to expand research and development in multi-agent web search and a governed data layer that processes and validates search results. Nimble has now raised a total of 75 million dollars.