Nimble Raises $47M to Give AI Agents Access to Real-Time, Verified Web Data

Nimble raises $47M to give AI agents access to real-time web data

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.) "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.

The era of human web search is over: Nimble launches Agentic Search Platform for enterprises boasting 99% accuracy

Web Search has already been disrupted by AI -- just take a look at how readily Google is presenting users with AI Overviews (summaries of search results) at the top of their results pages, how Bing early on integrated OpenAI's GPT models, and how Perplexity continues to build on its own AI-driven web search platform and browsers. Nimble announced the launch of its Agentic Search Platform, a system designed to transform the public web into trusted, decision-grade data for AI systems and business workflows. The launch is supported by $47 million in Series B financing led by Norwest, with participation from Databricks Ventures and others, bringing the company's total funding to $75 million. The initiative addresses a fundamental bottleneck in the current AI era: while large language models (LLMs) are becoming more sophisticated, they often reason over incomplete or unverifiable external information. Nimble's platform aims to eliminate this "guesswork gap" by providing a governed data layer that searches, navigates, and validates live internet data in real time. In an exclusive interview with VentureBeat, Nimble co-founder and CEO Uri Knorovich reflected on the early skepticism regarding his vision of a machine-centric internet. "Whenever we started this company, and the first time I went to investors, I told them the web is built for humans, but machines are going to be the first citizens of the web," Knorovich recalled. He noted that while initial reactions labeled him as "too visionary," the current reality of AI adoption has validated his thesis. Technology: Coordinated multi-agent architecture The core of Nimble's solution is a proprietary distributed architecture that orchestrates specialized agents to perform tasks traditionally handled by human researchers or brittle web scrapers. According to the company's infrastructure documentation, the process is broken down into five distinct layers: * Headless browser and browsing agents: These layers manage the initial interaction with a target domain, navigating complex site structures as a human would. * Parsing agents: These agents interpret the page content, identifying relevant data elements across various formats. * Data processing agents: This layer aggregates, filters, and cleans noisy internet data to produce specific, structured answers. * Validation agents: The final step involves verifying the results to ensure accuracy and completeness before delivery. Unlike standard search engines designed for consumer link-clicking, this architecture uses multimodal and reasoning capabilities from frontier models -- including those from OpenAI, Anthropic, and Meta -- to control real browsers. This allows Nimble to navigate dynamic layouts and cross-check results, producing auditable data outputs rather than simple text summaries. A new paradigm: 'The web is built for humans, but machines are the first citizens' Knorovich points out that the scale of AI interaction with the web is fundamentally different from human behavior. "We, as humans, search for maybe three or five options before we making decisions... but every day, Nimble perform more than 3.2 million interactions in the web," he explained. This sheer volume of billions of monthly searches represents a programmatic shift that requires a new type of infrastructure. The bottleneck for enterprises today, according to Knorovich, isn't the intelligence of the models, but the quality of the data they can access. "Agents are the headlines, and accurate and reliable web search is the bottleneck," he stated. Nimble vs. consumer search: Precision over speed Knorovich explicitly differentiates Nimble from general-purpose tools like Google or consumer AI search assistants. While Google has built a search experience for consumers that is optimized for speed and finding a local restaurant, enterprises require high-scale, high-accuracy results to make multi-million dollar decisions. "General purpose web search tool are great to have a general answers, such as who is the wife Leo missing," Knorovich remarked during the interview. "But enterprises need deep, granular data, and they need to have the ability to control the search filters, to control the regulation, to control what is a trusted source". Unlike consumer AI modes that may summarize a Reddit post or high-level news, Nimble provides "street-level" information that can be stored directly in an enterprise system of record. Product: Bridging the no-code and developer divide The Agentic Search Platform is delivered through two primary interfaces designed for enterprise scalability: The platform is built to deliver data with greater than 99% accuracy -- meaning fewer than 1% inaccurate or hallucinated data for the total contents of each search result returned -- and a latency of 1-2 milliseconds per request. It integrates natively with major data environments, allowing users to stream clean data directly into Databricks, Snowflake, S3, or Microsoft Fabric. During the interview, Knorovich emphasized that Nimble is designed to be model-agnostic, working seamlessly with state-of-the-art models from OpenAI, Anthropic, and Google's Gemini. This flexibility allows companies to use Nimble alongside their existing tech stack, whether they are running models in the cloud or on-premise for high-security environments like healthcare or banking. Case studies: Accuracy in action Knorovich provided several real-world examples of how this "street-level" data impacts professional workflows. For instance, a real estate broker looking to expand into a new territory doesn't need a high-level summary from a general-purpose AI. "If you want to know what's happening in the commercial real estate in Atlanta... you're not looking for search that's optimized for the millisecond," Knorovich explained. "You're looking for street-level, neighborhood-level information... data that you can actually see on a table or download to Excel". Another use case involves major financial institutions utilizing Nimble for "know your customer" (KYC) processes. By deploying an autonomous search agent, banks can cross-reference multiple public reports, criminal records, and address verifications to build a complete profile of a client before they even enter the building. The goal, Knorovich noted, is to provide the "external truth" that exists outside an organization's internal firewalls. Enterprise licensing and compliance Nimble differentiates itself from legacy scraping tools through a rigorous focus on governance and trust. The platform is "compliant-by-design," holding certifications for SOC2 Type II, GDPR, CCPA, and HIPAA. Pricing is structured to support both experimental startups and high-scale enterprise operations, aligned with the volume and depth of data retrieved. "Pricing should be aligned with the value that the user is getting... therefore, we are pricing by the amount of searches that you're running," Knorovich said. * Search and answer APIs: Standard search inputs cost $1 per 1,000, while the "Answer" function -- which provides reasoning based on search results -- costs $4 per 1,000. * Managed services: For larger organizations, managed tiers start at $2,000 per month (Startup) and scale to $15,000 per month (Professional) for unlimited agents and priority support. * Proxy access: A network of over 1 million residential proxies is available starting at $7.50 per GB Community and user reactions The transition to agentic search has already been operationalized by several Fortune 500 companies and AI-native startups: * Julie Averill, former CIO at Lululemon, stated that pricing intelligence which once took weeks to review can now be responded to in minutes by putting control in the hands of an agent. * Itamar Fridman, CEO and Co-founder of Qodo, noted that the platform's scalability was "crucial in developing more robust and reliable AI systems" by feeding LLMs with high-quality data. * Dennis Irorere, Data Engineer at TripAdvisor, highlighted that the platform simplifies the extraction of structured data from complex sources, which he described as "transformative" for his role. * Grips Intelligence reported scaling to over 45,000 e-commerce sites using Nimble's Web API to deliver real-time pricing and product data. * Alta utilizes the platform to power millions of AI-driven go-to-market workflows daily, reporting 3-4× deeper context and >99% reliability Series B to accelerate multi-agent web search and data governance The $47 million Series B funding announced alongside the platform will be used to accelerate research in multi-agent web search and further develop the governed data layer. The round saw participation from a wide ecosystem of investors, including Target Global, Square Peg, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures, and InvestInData. Andrew Ferguson, VP of Databricks Ventures, noted that Nimble complements their Data Intelligence Platform by providing a "real-time web data layer" that extends workflows beyond internal sources. This strategic investment signals a shift in the industry toward prioritizing "external truth" to ground mission-critical AI applications. For Knorovich, the future of the web belongs to programmatic interaction. "Programmatic web search is where we are building towards," he concluded. By moving away from legacy data vendors and brittle scrapers, Nimble aims to provide the real-time structure needed for AI to act with confidence in the real world.

Nimble raises $47M to scale agentic web search platform for enterprise AI - SiliconANGLE

Nimble raises $47M to scale agentic web search platform for enterprise AI Nimble announced today that it has raised $47 million in new funding to accelerate development of its agentic web search platform, expand its multi-agent research capabilities and scale up its governed real-time web data infrastructure for enterprise artificial intelligence deployments. Founded in 2021 as Thhe Data Company Technologies Inc., Nimble offers a real-time web search and data platform designed to address the challenge of obtaining structured, verifiable data from the live public internet for use in enterprise AI systems. Many AI deployments rely on static datasets, internal records or unstructured web summaries that are difficult to audit or reproduce. Nimble's approach, on the other hand, centers on coordinating multiple AI-driven agents that browse live websites, extract information and convert dynamic web content into structured, schema-first datasets suitable for operational use in AI deployments. "The greatest source of intelligence for businesses and AI is the web, but the data is dynamic and hard to verify, which is why we built Nimble," said co-founder and Chief Executive Uri Knorovich. "Businesses already run multi-agent systems where one agent searches, another verifies results from the web, and a third takes action and Nimble's agentic search powers that loop with verified data from the web." Nimble's platform works by using AI models to control full web browsers rather than relying solely on application programming interfaces or static scraping scripts. The agents navigate websites, interact with dynamic page elements, handle changing layouts and retrieve data directly from live sources. Having scraped sites, the platform then applies a governed data layer to process the collected information through steps such as cleaning, deduplication, joining and aggregation. The output is converted into structured tables that can be queried, stored or integrated into enterprise analytics and AI systems. The platform's capabilities include a no-code workflow builder that allows teams to configure browser-based search agents and automate recurring web data tasks and a software development kit provides programmatic access to search, extraction and crawling functions for developers. The system is designed to support long-running, multistep workflows in which one agent gathers information, another cross-checks results, and a governed layer validates outputs before they are delivered into downstream applications. The platform is used in workflows that require timely and verifiable external data, such as financial due diligence, retail pricing analysis, market research, media monitoring and social listening. Nimble's platform also integrates with services from Databricks Inc. and Microsoft Corp. to allow customers to incorporate structured web data into existing data pipelines, business intelligence tools and agent-based applications operating in production environments. The company says Fortune 500 companies use the company's platform to stream trusted web data directly into their workflows. Notable Nimble customers include Databricks, Uber Technologies Inc., The Coca-Cola Co., Tripadvisor Inc., L'Oréal SA, Deloitte Touche Tohmatsu Ltd., Microsoft and LG AI Research. The Series B round was led by Norwest Venture Partners LP, with participation from Databricks Ventures and existing investors including Target Global Management GmbH, Square Peg Capital Pty. Ltd., Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures and InvestInData. "Nimble is tackling a problem that has existed for years without a proper solution and is now becoming of critical urgency," said Assaf Harel, a partner at Norwest. "Trusted live web data is increasingly becoming a prerequisite for AI agents performing critical business decisions. As enterprises deploy AI in high-stakes environments, the need for trusted, clean, governed, live web data becomes essential." The new funding takes the total raised by Nimble to $75 million.