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 exi
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 exi
