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 architectureThe 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 speedKnorovich 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 divideThe Agentic Search Platform is delivered through two primary interfaces designed for enterprise scalability:Web search agents: A no-code AI workflow builder that enables business teams to describe the data they need and receive structured data streams without writing a line of code.Web tools SDK: A suite of APIs for builders to search, extract, and crawl the web directly from their code. This includes specialized tools like the /crawl API for mapping entire domains and the /map API for creating domain trees.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 actionKnorovich 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 complianceNimble 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 GBCommunity and user reactionsThe 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% reliabilitySeries B to accelerate multi-agent web search and data governanceThe $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.
