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 has worked early on to integrate OpenAI’s GPT model, and how Perplexity continues to build on its own AI-powered web search platform and browser.
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 backed 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.
This initiative addresses a fundamental obstacle in the current AI era: while large language models (LLMs) are becoming more sophisticated, they often reason on incomplete or unproven external information. Nimble’s platform aims to eliminate this "estimate difference" By providing a controlled 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 Norovich reflected on early skepticism about its vision of a machine-centric Internet.
"Whenever we started this company, and the first time I went to investors, I told them that the Web was built for humans, but machines are going to be the first citizens of the Web," Norovich recalled. He said that initial reactions labeled him as "very 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 document, the process is divided into five distinct layers:
- Headless Browser and Browsing Agent: These layers manage the initial interaction with a target domain, much like a human navigates complex site structures.
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Parsing Agent: These agents interpret page content, identifying relevant data elements in a variety of formats.
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Data Processing Agent: This layer collects, filters, and cleans noisy Internet data to generate specific, structured answers.
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Verification Agent: 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 OpenAI, Anthropic, and Meta – to control real browsers. This allows Nimble to navigate dynamic layouts and cross-check results, producing audible data output rather than simple text summaries.
A new paradigm: ‘The web is built for humans, but machines are the first citizens’
Norovich points out that the scale of AI interaction with the web is fundamentally different from human behavior. "We, as humans, explore perhaps three or five options before making a decision… but every day, Nimble drives over 3.2 million interactions on the web," he explained. This sheer volume of billions of monthly searches represents a programmatic shift that requires a new type of infrastructure.
According to Norovich, the barrier for enterprises today is not 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 hurdle," He said.
Agile vs. Consumer Search: Accuracy over Speed
Norovich clearly differentiates Nimble from general-purpose tools like Google or consumer AI search assistants.
While Google has created a search experience for consumers that is optimized for speed and finding local restaurants, enterprises need high-level, high-accuracy results to make multi-million dollar decisions.
"General purpose web search tools are great for finding general answers, such as who is leo’s wife missing," Norovich commented during the interview. "But enterprises need deep, detailed data, and they need the ability to control search filters, control regulation, control a trusted source". Unlike consumer AI modes, which can summarize Reddit posts or high-level news stories, Nimble offers "street level" Information that can be stored directly in an enterprise system’s records.
Product: Bridging the no-code and developer divide
The Agentic Search platform is delivered through two primary interfaces designed for enterprise scalability:
- Web Search Agent: A no-code AI workflow builder that enables business teams to describe the data they need and receive structured data streams without writing a single line of code.
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wEB Tools SDK: A suite of APIs for builders to search, extract, and crawl the web directly from their code. It includes specialized tools such as the /crawl API to map entire domains and the /map API to create domain trees.
The platform is built to deliver data with over 99% accuracy – meaning less than 1% incorrect or misleading data for the total content of each search result returned – and latency of 1-2 milliseconds per request.
It integrates seamlessly with major data environments, allowing users to stream clean data directly into Databricks, Snowflake, S3 or Microsoft Fabric.
During the interview, Norovich emphasized that Nimble is designed to be model-agnostic, working seamlessly with cutting-edge models from OpenAI, Anthropic, and Google’s Gemini. This flexibility allows companies to use Nimble with their existing technology stack, whether they are running the model in the cloud or on-premises for high-security environments like healthcare or banking.
Case Study: Accuracy in Action
Norovich provided several real-world examples of "street level" Data impacts professional workflow. For example, a real estate broker that wants to expand into a new area doesn’t need high-level summaries from a general-purpose AI.
"If you want to know what’s happening in commercial real estate in Atlanta… you’re not looking for a search that’s optimized to the millisecond," Norovich explained. "You’re looking for street-level, neighborhood-level information… data you can actually look at in a table or download into Excel".
Another use case involves major financial institutions using Nimble. "know your Customer" (KYC) procedures. By deploying an autonomous search agent, banks can cross-reference multiple public reports, criminal records, and address verification to build a complete profile of a customer before he or she enters a building. The goal, Norovich said, is to provide "external truth" That exists outside an organization’s internal firewall.
Enterprise Licensing and Compliance
Nimble differentiates itself from legacy scraping tools through a strict focus on governance and trust. the stage is "custom-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 recovered.
"Pricing should be in line with the value that the user is getting… So, we are pricing based on the volume of searches you are doing," Norovich said.
- API Search and Reply: Standard search inputs cost $1 per 1,000, while "Answer" The function—which provides logic based on search results—costs $4 per 1,000.
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Managed Services: For larger organizations, managed tiers start at $2,000 per month (Startup) and go up to $15,000 per month (Professional) for unlimited agents and priority support.
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Proxy Access: Network of over 1 million residential proxies available starting at $7.50 per GB
Community and user reactions
The transition to agentic search has already been initiated by several Fortune 500 companies and AI-native startups:
- Julie Averill, former CIO at Lululemonsaid pricing information that once took weeks to review can now be responded to in minutes, putting control in the hands of an agent.
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Itamar Friedman, CEO and co-founder of Qudo, Noted was the scalability of the platform "Important in developing more robust and reliable AI systems" By feeding high-quality data to LLM.
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Dennis Irorere, Data Engineer at TripAdvisorhighlighted that the platform simplifies the extraction of structured data from complex sources, which he described as "transformative" For his role.
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intelligence information Scaling has been reported on over 45,000 e-commerce sites using Nimble’s Web API to provide real-time pricing and product data.
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Alta Millions use the platform daily to power AI-powered go-to-market workflows, 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 into 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-Square Ventures, J-Ventures and InvestInData.
Andrew Ferguson, VP of Databricks Ventures, said that Nimble complements their data intelligence platform by providing a. "real time web data layer" That extends the workflow beyond internal sources. This strategic investment signals a shift in prioritization in the industry "external truth" Grounding mission-critical AI applications.
For Norovich, the future of the Web belongs to programmatic interactions. "Programmatic web search is where we’re building," He concluded. Moving away from legacy data vendors and brittle scrapers, Nimble aims to provide the real-time structure needed for AI to function with confidence in the real world.
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