Neurometric Raises $4M to Help Enterprises Control the Cost of Agentic AI
Neurometric is building infrastructure to route AI tasks to the right models based on cost, accuracy, and performance.
As companies move from experimenting with AI to deploying agentic workflows at scale, a new infrastructure challenge is becoming harder to ignore: model costs.
Every task does not need the same model. Some workflows require frontier-level reasoning, while others can be handled by smaller, more specialized models. But for enterprises, deciding which model to use, when to use it, and how to manage performance across a fast-changing market is becoming its own operational problem.
Neurometric is building around that problem.
The company raised $4 million in pre-seed funding from Betaworks, ex/ante, Everywhere Ventures, Encoded Ventures, Vermillion, Abstraction, Mu Ventures, Jason Calacanis, and Dharmesh Shah.
Neurometric provides an automated token engineering platform for companies running agentic AI workloads at scale. Its platform helps organizations route tasks to the most cost-effective and accurate language models, with tools including an automated task endpoint manager, an SLM marketplace, and an auto-SLM creator.
That is the larger opportunity behind the round. As AI usage grows inside the enterprise, the question is no longer just which model is best. It is how to build systems that can continuously optimize across cost, latency, accuracy, and reliability.
Neurometric will use the new capital to expand its engineering and AI research teams and continue developing optimization tools for a language model market that is changing quickly.
As agentic AI moves from pilots into production, infrastructure that makes model usage more efficient, measurable, and cost-effective may become a critical layer of the AI stack.
Read the full article on The SaaS News.
