Neurometric Secures $4 Million to Automate AI Token Engineering

The platform addresses a growing friction point for enterprises: the reliance on expensive frontier models for every task within an AI workflow. Neurometric’s Task Endpoint Manager evaluates incoming requests against real-time performance and pricing data, routing each task to the most cost-effective model that meets specific latency and accuracy requirements. When existing options fall short, the company’s Auto-SLM Creator builds a specialized small language model tailored to that specific workload.

CEO Rob May argues that as agentic AI scales, human oversight of model selection is no longer feasible. By automating these choices, the firm enables companies to reserve expensive frontier intelligence for complex problems while offloading routine tasks to cheaper, purpose-built alternatives. Early deployments have shown that these optimized paths can outperform general-purpose models by up to 20 percentage points in accuracy while simultaneously reducing latency and overhead.

The $4 million investment, which closed earlier this year, includes backing from firms such as Betaworks, ex-Ante, and Everywhere.vc, alongside notable angel investors including HubSpot CTO Dharmesh Shah. Neurometric plans to use the capital to scale its research and engineering teams, further expanding the platform’s optimization capabilities as the ecosystem of available models continues to fragment.