Nimble claims its new, domain-specialized Web Search Agents cut token costs in half while boosting retrieval accuracy

Nimble, a New York City-based tech startup VentureBeat previously covered for its efforts to re-invent web search for enterprises by using multiple AI agents to improve accuracy and depth, is taking another step toward its vision of a world in which agents do most of the web searching instead of us typing and reviewing the results manually. Nimble today launched Web Search Agents, a new retrieval system designed to help AI agents perform more 21% more accurate web research while using significantly fewer tokens — 51% less compared with leading AI search alternatives on comparable, according to the firm. While Nimble did not disclose its specific benchmarking methodology or competitors evaluated, the results underscore a growing trend in enterprise AI: optimizing retrieval has become as important as improving the underlying language models themselves.Nimble's leadership says the product combines self-learning retrieval strategies, proprietary web indexes, and live web access to deliver domain-specific search capabilities that outperform general-purpose web search services for enterprise workloads."Our research team built self-learning retrieval algorithms that learn a customer's domain," said Nimble CEO and co-founder Uri Knorovich in an interview with VentureBeat. "They find the exact information more efficiently, reduce the amount of multi-hop reasoning required, and lower token usage while improving accuracy."Rather than positioning itself as another general search engine, Nimble is targeting developers building autonomous agents that require continuously updated information from the public web for research, lead generation, competitive intelligence, compliance, and other business-critical workflows.It's also designed to slot in seamlessly to an enterprise's existing systems and workflows."You can run the agent directly through the Nimble API with zero infrastructure," Knorovich said. "For large enterprises, we're partnering with Microsoft, Oracle, Snowflake, and others so customers can deploy these agent systems inside their own infrastructure."How does it work and stack up to other, existing AI-powered search and agentic systems? Read on to find out.Moving beyond generic AI web search into specialized search agents that fit your enterprise's needsMost AI applications today rely on general-purpose search application programming interfaces (APIs) for search engines and public knowledge bases that return broad collections of files, leaving the language model responsible for determining which sources are relevant. That process often requires multiple retrieval steps, additional reasoning, and significant token expenditure before an agent produces an answer. This is obviously inefficient and raises the cost spent to run AI search looking through irrelevant sources. Nimble argues that before long, every enterprise will need its own methods for searching, retrieving, and validating external information since each enterprise relies on its own distinct preferred sources, signals, and standards of trust. As such, instead of applying one search strategy to every workload, Nimble's Web Search Agents are designed to learn the characteristics of a specific domain and adapt how information is retrieved, providing agents with structured, relevant context rather than forcing them to sift through large amounts of generic search results."Instead of one generic retrieval model, we build specialized retrieval models for each customer's domain, making them faster, cheaper, and more accurate," Knorovich explained. "A single enterprise can run hundreds of different agents. Each one has its own domain expertise, guardrails, goals, and search algorithm. The optimization starts with the second search, without requiring any setup from the customer."Its goal is not only to reduce redundant retrieval, but also to shorten multi-step research paths and avoid repeatedly sending raw pages through a language model for