Nimble vs. Coworker

Nimble delivers expert-level web search for AI agents — built to drive higher accuracy while cutting token costs. Most AI agents rely on generic, one-size-fits-all search tools that treat every query the same way, regardless of what the agent is actually trying to accomplish. The result is noisy, irrelevant results: agents burn tokens parsing raw web pages, re-searching the same ground on every call, and still surface low-quality answers. As agentic AI scales into production, this generic approach becomes the weakest link in the stack.

Coworker is an enterprise AI platform that routes each task to the right model with the right context. Teams get frontier-quality outputs at about 82% lower cost per task, and run roughly 5x more inference per dollar than they would on direct Claude or OpenAI consumption. Overview. Coworker is built for companies whose AI usage is growing faster than their AI budget. As frontier model adoption has scaled, customers face a tradeoff between slowing down adoption and cutting headcount to control spend. Coworker takes a different approach.