Web data search specialist Nimble raises $47M to fuel growth
By raising funding when AI developers are dominating VC interest, the startup shows that there is a need for agents and other AI tools to include context from external sources.
Nimble, a startup providing an AI-powered platform that enables customers to instantaneously convert data from the internet to curated data tables, on Tuesday secured $47 million in venture capital funding.
The Series B round, which follows the vendor's Series A round in 2023, brings Nimble's total funding to $75 million, was led by Norwest and included investors Databricks Ventures, Target Global, Square Peg, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures and InvestInData.
Nimble plans to use the new funding to fuel continued improvement and expansion of its web data ingestion platform, according to Uriel Knorovich, the vendor's co-founder and CEO.
But beyond the product development plans the new funding will enable, the funding reflects a vote of confidence in the value of Nimble's web data collection capabilities, according to Michael Ni, an analyst at Constellation Research, who noted that AI agents and other applications increasingly need context from external sources such as the web in addition to an organization's proprietary data.
"Structured, governed external data pipelines are no longer viewed as bolt-on enrichment," he said. "Live, external data is increasingly foundational to enterprise decision automation. In today's funding climate, a $47 million Series B also signals that institutional investors believe the company has achieved product-market fit and is now scaling for enterprise adoption."
Based in New York, Nimble was founded in 2021. In addition to Nimble, platforms that enable users to extract data from the web include Apify, Bright Data and Oxylabs.
Finding funding
Funding for data management vendors used to be plentiful. In mid-2022, however, venture capital interest in such companies evaporated amid a tech-stock sell-off that coincided with precipitous drops in the Dow Jones Industrials Index, Nasdaq Composite Index and S&P 500.
Structured, governed external data pipelines are no longer viewed as bolt-on enrichment. Live, external data is increasingly foundational to enterprise decision automation.
Michael NiAnalyst, Constellation Research
Since then, while AI developers such as Anthropic and OpenAI have raised massive amounts of funding, funding rounds by data management specialists have been few and far between.
Databricks, which like Anthropic and OpenAI is investing heavily in AI, continues to execute huge funding rounds. But among other data and analytics vendors, Aerospike and Sigma are among the few that have been able to raise over $100 million in a single round over the past few years while a smattering of others have raised around $50 million in their rounds.
Now, Nimble is among them.
Nimble, in effect, is a web search platform that feeds data gathered from those searches to AI applications. To date, most AI tools have been fed by enterprises' proprietary data, which is the data they collect about themselves and their operations.
However, information such as regulatory changes, movements in key markets and competitor actions is also key to informing decisions and actions. That information is only available on the open web. To securely and accurately collect and curate that data in structured tables requires specialized tools such as Nimble's platform.
Nimble's ability to attract new funding at a time when venture capitalists are not investing heavily in data management vendors demonstrates the growing need for reliable web data in real time, according to Knorovich.
"Investors are coming to us and saying they see the potential in how search can change mission-critical AI," he said. "They see the things that we can do with the technology we've developed and how it has transformed the way businesses have been putting AI to work. … It's a big testimonial about where the market is going and how critical enterprise-level web search is going to be."
Nimble's platform is based on a series of algorithms that enable a set of agents to execute web data searches on behalf of customers, which include hundreds of enterprises such as big banks, retailers and financial consulting firms, according to Knorovich.
Meanwhile, only recent advances in AI technology have enabled Nimble to build the agents that fuel its platform, resulting in the vendor providing potentially unique capabilities.
"The combination of the advent of AI and the modern data stacks that allow us to process the data are two things that are happening just now," Knorovich said. "To have a general web search is something that a lot of people can do, but doing it at extremely high scale with enterprise reliability and completeness … is something that not a lot of people can do."
One example of an enterprise needing data from the web to inform a strategic decision is geographic expansion into a new market. Generic AI platforms such as OpenAI's ChatGPT and Gemini from Google can provide high-level information. But a more sophisticated search tool is required for fine-grained data such as average costs per square foot in on a particular street, historical sales in that area and comparable listings that closed in the past month.
Ni, like Knorovich, noted that Nimble's ability to raise funding reflects the vendor's focus on a growing need.
"If Nimble were just another data management company, it would have struggled to raise capital in this environment," he said. "The fact that Nimble secured a $47 million tells you investors see it as a vertical player providing external data infrastructure for lakehouses and agent ecosystems, and not the data back office. This is the difference between driving decisions and storing and organizing data."
Other vendors provide large-scale web data extraction, Ni continued. In addition, developers have for years been able to build customized tools to collect specific web data. However, Nimble stands apart from competitors by turning web data into structured, validated tables ready for AI, according to Ni.
"What differentiates Nimble is the way it packages browser-level automation, schema-first structuring, and validation layers specifically for production AI workflows at a time when providing the right context to AI-driven decisioning has become more important than ever," he said. "Nimble's positioning moves them closer to decision infrastructure than traditional scraping."
Fueling growth
While the funding represents validation for Nimble, what it enables is expansion and improvement, according to Knorovich.
In conjunction with the funding, Nimble on Tuesday launched a no-code agent aimed at making it easier for developers to stream live data from the web and enable non-technical users to similarly extract data from the external web.
"We already have the APIs for builders to search and extract with their code environment," Knorovich said. "We're adding a no-code layer for building AI workflows so anyone in the enterprise can do that."
Looking ahead, the funding will enable Nimble to add more agents to foster coordinated multi-agent web data searches, he continued.
"We're going to use this funding to keep accelerating our web tools and APIs for developers and adding more agentic capabilities for developers," Knorovich said. "We're also expanding our no-code AI workflow."
Ni, meanwhile, noted that while Nimble provides valuable web data extraction capabilities, the vendor would be wise to add tools around its core capabilities that that engender greater trust in AI outputs.
"Capital gives Nimble the chance to graduate from extraction engine to external context backbone," he said. "The next move isn't more scraping. It's building the trust architecture around validation, policy, and observability that make AI-driven decisions safe to execute at scale."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
