Believe it or not, web search is still a thriving industry. As companies invest in using AI agents to make the most of their data, there is also a growing demand for tools that not only scrape the web and inform AI bots’ behavior, but also return the results in a way that is easy to use with modern data tools.
That’s the promise behind Nimble, a web search startup that recently raised $47 million in a Series B round led by Norwest. The New York company’s platform employs AI agents to search the web in real time, validate the results, and structure the information into neat tables that can be queried like a database.
The last part is important here. LLM and AI agents are great for searching the web, connecting results from different sources, and analyzing them, but they often return results in plain text, which can make it difficult to work at an enterprise level. That’s before considering hallucinations, the risk of agents misunderstanding instructions, or the use of unreliable sources.
Nimble allows businesses to use web data as if it were part of an existing database by validating and structuring results into tables. The startup also integrates with enterprise data warehouses and data lakes (large-scale, centralized repositories where businesses store and analyze data) from companies like Databricks and Snowflake. This means the company’s AI agents can connect to a company’s troves of data and use it to build context and shape how search results are structured and returned.
This effectively allows enterprises to leverage live, structured web data as part of their existing data environment, Nimble CEO and co-founder Uri Knorovich (pictured above, center) told TechCrunch.
Such integration allows Nimble’s software to remember constraints, such as how you perform a search or the data sources you tap on. This is particularly useful for applications such as competitor analysis, price research, know your customer (KYC) processes, brand monitoring, deep dives, and financial analysis. (Knorovich said the company is working to ensure that all customer data remains within the customer’s data infrastructure to comply with data retention and security policies.)
To this end, the startup 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 AI failures in production are due to failures in the data, not because the models are inadequate,” Knorovich says. “What we’re seeing today is that enterprises don’t need more AI. They need AI with good, reliable web search (…) When you get down to it, if you can choose what agents can and can’t search for, this is the tipping point for enterprises to say, ‘I can actually trust AI. I can actually leverage AI for more use cases.'”
Knorovich says the ability to search the web at scale and in real time, 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, and the majority of its revenue comes from large enterprises, Fortune 500 companies, and even some Fortune 10 companies, including major retailers, hedge funds, banks, and consumer goods companies, as well as some AI-native startups.
“Nimble is addressing a problem that has existed for years without a suitable solution, but now with great urgency,” Asaf Harel, a partner at Norwest, said in a statement. “Trusted live web data is becoming a prerequisite for AI agents to make critical business decisions.”
Returning investors also participated in the Series B: Target Global, Square Peg, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures, and InvestInData. Proceeds from this round will be used to expand research and development of multi-agent web search and the managed data layer that processes and validates search results.
Nimble has now raised a total of $75 million.
