Nimble Secures $47M to Power AI Agents with Real-Time Web Data

The demand for reliable, real-time web data to fuel artificial intelligence systems is driving significant investment in new search technologies. Businesses increasingly rely on AI agents for critical decisions, yet they face persistent challenges with unstructured results and unverified information. Nimble, a New York-based startup, has secured a $47 million Series B investment led by Norwest Venture Partners to address this exact problem. The company’s platform uses specialized AI agents to search the web, verify the findings, and structure the information into queryable tables, effectively turning live web data into a usable database resource.

A major hurdle for enterprise AI adoption is how information is delivered. While large language models excel at gathering and analyzing data from diverse sources, they typically output plain text. This format is cumbersome for large-scale business applications and comes with risks like AI hallucinations, misinterpreted instructions, and reliance on dubious sources. Nimble’s approach transforms this workflow. By validating and structuring results into organized tables, companies can integrate web data seamlessly with their existing data warehouses and lakes, such as those from Databricks and Snowflake. This integration allows Nimble’s agents to leverage a company’s internal data for context, shaping how search results are structured and delivered.

This capability creates a live stream of structured web data within a company’s established data environment. According to Nimble CEO and co-founder Uri Knorovich, the software can also remember specific constraints and preferences for searches, including which sources to use. This functionality proves invaluable for complex business tasks like competitor analysis, pricing research, financial analysis, brand monitoring, and know-your-customer (KYC) verification. The company emphasizes that all customer data remains within the client’s own infrastructure to ensure compliance with data security and retention policies.

To streamline enterprise deployments that require blending internal and external data, Nimble has established partnerships with major cloud and data platform providers, including Databricks, Snowflake, AWS, and Microsoft. Databricks also participated as an investor in this latest funding round.

Knorovich argues that the primary failure point for production AI is often data quality, not the models themselves. “Enterprises don’t need more AI; they need AI with good, reliable web search,” he stated. The ability to control what an agent can and cannot search, and to trust the validated, structured output, represents a tipping point for business trust in AI, enabling its use in more critical applications. He believes Nimble’s real-time, scalable search with built-in validation and structuring is what differentiates it from other data providers in the market.

The startup has already attracted over 100 customers, with most revenue derived from large enterprises. Its client base includes Fortune 500 and even some Fortune 10 companies, spanning major retailers, hedge funds, banks, consumer packaged goods firms, and AI-native startups.

Assaf Harel, a partner at lead investor Norwest, highlighted the growing urgency for a solution like Nimble’s. “Trusted live web data is increasingly becoming a prerequisite for AI agents performing critical business decisions,” he said in a statement.

The Series B round included participation from returning investors Target Global, Square Peg, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures, and InvestInData. The new capital will fund expanded research and development, focusing on multi-agent search systems and a governed data layer for processing and validating results. To date, Nimble has raised a total of $75 million.

(Source: TechCrunch)