Nimble has launched a new enterprise research product designed to help artificial intelligence agents find more relevant online information while using fewer computing resources.
Nimble Web Search Agents learn the subject area and research requirements of each customer before carrying out complex web searches. The system is intended for companies that need more precise results than those produced by general-purpose search tools.
According to Nimble, its technology improved answer quality by 21 percentage points in benchmark testing while reducing token use per query by 51%.
The launch targets one of the biggest challenges facing enterprise AI teams: giving autonomous agents reliable, current web information without wasting tokens on irrelevant searches and unnecessary pages.
Why Nimble Web Search Agents Were Developed
Traditional web search tools often return a broad collection of links that may not match the specific goal of an AI agent.
The agent must then review large amounts of information, determine which sources matter and make additional search requests when the results are incomplete. This process can increase token consumption, operating costs and the risk of producing weak answers.
Nimble argues that different business agents need different search strategies.
For example, an AI system conducting market research may need industry reports, pricing data and competitor updates. A lead-enrichment agent may instead need company profiles, executive information and recent business activity.
Giving both agents the same general search product can produce too much irrelevant information.
Nimble Web Search Agents address this problem by learning the knowledge work involved in each task and adjusting their retrieval methods accordingly.
Domain-Specific AI Research
The platform is designed to understand a customer’s research domain before it begins gathering information.
It combines Nimble’s proprietary web indexes with real-time retrieval from active websites. This allows agents to access detailed and current information while focusing on sources that are relevant to the assigned task.
The system also checks its work during the research process.
Nimble says this self-evaluation feature can reduce the amount of information that teams must verify or repeat after the agent completes a task.
The company is not positioning the product mainly as a tool for instant answers. Instead, it is targeting complex research jobs that may run for several hours and support important business decisions.
In these situations, finding the right source can be more valuable than producing the fastest possible response.
Nimble Web Search Agents Reduce Token Use
Token consumption has become an important cost issue for companies building AI applications.
Every search request, webpage analysis and generated response can increase the number of tokens processed by an AI model. When enterprise agents perform thousands or millions of tasks, those costs can grow quickly.
Nimble says its benchmark results showed that Web Search Agents used 51% fewer tokens for each query while improving the quality of the answers.
The system aims to achieve this by limiting unnecessary tool calls and avoiding pages that do not contribute meaningful information.
Uri Knorovich, Nimble’s chief executive and co-founder, said accuracy and cost are major obstacles for enterprise AI adoption.
He said agents need access to reliable, live web context without spending tokens on generic search results that may not be useful.
Customer Reports 20-Fold Token Cost Reduction
Rox, an AI-focused customer relationship management company, reported a 20-fold reduction in token costs after adopting Nimble’s service.
The company also said the information delivered to its agents became more complete and improved in quality.
Qodo, a code integrity startup, has also used Nimble’s technology.
Almog Lavi, Qodo’s head of product, said the platform allowed the company to adjust a Claude Managed Agent so it could identify relevant competitor signals instead of returning a general market overview.
These examples highlight Nimble’s focus on customised research rather than one-size-fits-all web search.
How Nimble Web Search Agents Work
The platform acts as a specialised research layer between an AI agent and the wider internet.
Instead of allowing the agent to search the web without a clear strategy, Nimble’s system learns the type of information required and adapts its retrieval process.
A typical workflow may include:
- Understanding the assigned research goal
- Identifying the most relevant source categories
- Searching proprietary indexes and live websites
- Filtering information that does not match the task
- Checking the quality and completeness of the results
- Returning structured information to the AI agent
This process is intended to help companies build agents that can conduct deeper and more focused research.
Available Through API, SDK and MCP
Nimble Web Search Agents can be accessed through an application programming interface, a software development kit and Model Context Protocol.
Model Context Protocol, commonly known as MCP, allows AI systems to connect with external tools and data sources through a standardised framework.
Developers can add Nimble’s search capabilities to an existing agent or use the platform as the foundation for a new application.
The company says the service can support several types of products, including:
- Low-latency web search
- Deep research agents
- Market intelligence tools
- Lead-enrichment systems
- Competitor monitoring
- Structured dataset creation
- Business information platforms
A free trial is available for developers and companies that want to test the service.
Enterprise AI Teams Seek Better Web Context
As AI agents become more capable, companies are increasingly using them for research, sales, customer management and competitive analysis.
However, these agents still depend heavily on the quality of the information they receive.
A powerful language model can produce a poor response when its search tools return outdated, incomplete or irrelevant material. It may also spend more money processing unnecessary information.
Nimble is betting that companies will pay for search systems that understand the task before gathering data.
This approach could become increasingly important as businesses move from experimental chatbots to agents that operate independently and support critical decisions.
Nimble Handles More Than 90 Million Daily Searches
Nimble says its platform processes more than 90 million searches each day.
Its customers include Fortune 500 corporations and younger AI-focused companies.
The volume shows the growing demand for web infrastructure that can support automated research at scale.
Unlike consumer search engines, enterprise search systems must often deliver information in a format that software agents can analyse and use without extensive manual preparation.
Nimble’s product is designed to meet that requirement by providing targeted and structured results.
Nimble Has Raised $75 Million
Founded in 2021, Nimble is based in New York and is backed by venture capital investors.
The company raised $47 million in a Series B funding round in February 2026. Norwest Venture Partners led the investment.
Other participants included Databricks Ventures, Target Global, Square Peg Capital, Hetz Ventures, Slow Ventures, R-Squared Ventures, J-Ventures and InvestInData.
Nimble has raised a total of $75 million since its founding.
The funding gives the company additional resources to expand its web infrastructure and compete in the rapidly growing enterprise AI market.
What Nimble Web Search Agents Mean for Enterprise AI
Nimble Web Search Agents reflect a broader shift in artificial intelligence development.
Companies are no longer focused only on building models that can generate convincing answers. They are also investing in the tools that allow those models to find accurate and relevant information.
Web search is a critical part of that infrastructure.
By adapting search strategies to specific business tasks, Nimble aims to help AI agents reduce costs, improve answer quality and complete research with less human correction.
The product may be especially useful for organisations running long, business-critical research tasks where missing an important source could be more costly than waiting longer for an answer.
Final Thoughts
Nimble Web Search Agents are designed to make enterprise AI research more focused, accurate and affordable.
The platform learns the requirements of each task, combines real-time web retrieval with proprietary indexes and checks its own work before returning information.
Nimble’s reported benchmark results suggest that this approach can improve answer quality while cutting token use significantly.
As more companies deploy autonomous agents for research, sales and market intelligence, specialised web retrieval systems could become an essential part of the enterprise AI technology stack.
