Believe it or not, web search is still thriving as an industry. As businesses invest in using AI agents to make the most of their data, there’s demand for tools that not only scrape the web to inform what those AI bots do, but also return those results in a way that’s easier to use with modern data tools.
That’s the promise behind web search startup Nimble, which recently raised a $47 million Series B round, led by Norwest. The New York company’s platform employs AI agents to search the web in real time, verify, and validate the results, and then structure the information into neat tables that can then be queried like a database.
That last part is crucial here. LLMs and AI agents are great for searching the web, connecting results from a variety of sources, and analyzing them, but they often return the results in plain text, which can be difficult to work with at an enterprise level. And that’s before you factor in hallucinations, the risk of the agent misunderstanding your instructions, or the use of unreliable sources.
By validating and structuring results into tables, Nimble lets companies use web data as if it were already part of their existing databases. The startup also integrates with enterprise data warehouses and data lakes — large centralized repositories where businesses store and analyze data — offered by the likes of Databricks and Snowflake. That means its AI agents can plug into a business’s trove of data, using it to build context, and shape how search results are structured and returned.
In effect, this lets enterprises have live, structured web data as part of their existing data environments, Nimble CEO and co-founder Uri Knorovich (pictured above, middle) told TechCrunch.
Such integrations also allow Nimble’s software to remember constraints — such as how you want the search to be performed, or which data sources to tap. This is particularly useful for applications such as competitor analysis, pricing research, know-your-customer (KYC) processes, brand monitoring, deep research, and financial analysis. (Knorovich noted that Nimble works to ensure all customer data remains within customers’ 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.)
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“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. “What we’re seeing today is that enterprises don’t need more AI; they need AI with good, reliable web search […] If you nail it down, if you can choose what your agent can search and cannot search, this is the tipping point for enterprises to say, ‘hey we can actually trust AI. We can actually put AI to work in more use cases’.”
Knorovich says the ability to search the web in real time at scale, and validate and structure search results, is what sets Nimble apart from other data brokers already in the space.
The startup currently has more than 100 customers, with the majority of its revenue coming from large enterprises, Fortune 500 companies, and even some Fortune 10 companies, including major retailers, hedge funds, banks, and consumer packaged goods companies, as well as some AI-native startups.
“Nimble is tackling a problem that has existed for years without a proper solution and is now becoming of critical urgency,” Assaf Harel, partner at Norwest, said in a statement. “Trusted live web data is increasingly becoming 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 R&D 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.
