Neurometric helps companies optimize token spend for cost, speed, and reliability.
NEW YORK, June 25, 2026 /PRNewswire/ — Neurometric AI, an AI infrastructure company helping businesses control the cost and performance of agentic workloads, today launched its automated token engineering platform and announced $4 million in funding closed earlier this year.
As companies move AI agents from experimentation into production, a single workflow can generate dozens of model calls. Many businesses continue to send each task to a frontier model, even when a smaller and less expensive model could deliver the same or better result. Neurometric evaluates those calls individually, modifies prompts as needed, and routes each task to the most cost-effective model capable of meeting the required performance threshold and creates a purpose-built small language model when no available option fits. For simple high volume workloads, the Neurometric platform automatically generates specialized small language models to optimize the speed and cost of the tasks.
“Companies have spent the past year proving that AI agents can perform increasingly complex work. Now they have to prove the economics still make sense when those agents are operating at scale,” said Rob May, CEO of Neurometric. “Every model call is also a pricing decision, and those decisions compound across an agent’s workflow. Token engineering gives companies a way to control that cost without sacrificing quality.”
Neurometric Brings Token Engineering Into One Platform
Companies currently rely on a mix of manual testing and individual point solutions to decide which models should handle different AI tasks. Those choices can quickly become outdated as new models enter the market and pricing, speed and performance change.
Neurometric brings model routing, small language model creation and access to a marketplace of pre-trained task specific SLMs into one platform. Its Task Endpoint Manager evaluates incoming requests against continuously updated model performance and pricing data, then routes each task according to the customer’s accuracy, cost and latency requirements.
When no existing model meets those requirements, Neurometric’s Auto-SLM Creator builds and serves a small language model designed for the specific task. The platform’s SLM marketplace also allows customers to access models already developed for common and recurring workloads.
This approach allows companies to use frontier models where their capabilities are necessary while moving more narrowly defined tasks to smaller and less expensive alternatives.
Across early customer engagements, models routed or created through Neurometric have achieved accuracy rates that have beaten frontier models by as much as 20 points, while reducing cost and latency compared with using frontier models for the same work.
“Companies need to know where frontier-level performance is worth paying for and where a smaller model can deliver the same result at a fraction of the cost. That discipline will determine whether agentic AI can move from promising pilots to a business model that scales,” said Neurometric COO Calvin Cooper.
“The number of available models is growing too quickly for companies to evaluate every option by hand,” May said. “And the number of tools and techniques to improve them are growing even faster. Things change so fast a human token engineer can’t keep up. That decision needs to be automated and continuously reevaluated as the market changes.”
$4 Million Raised to Expand the Platform
Neurometric closed $4 million in funding earlier this year while the team continued developing and testing the platform with customers.
The round included participation from Betaworks, ex-Ante, Everywhere.vc, Encoded, Vermillion, Abstraction, and Mu Ventures. Several prominent angels also participated including Jason Calacanis, co-host of the All-In Podcast, and Dharmesh Shah, CTO of Hubspot.
Neurometric will use the funding to expand the engineering and AI research teams to provide customers even more optimization tools as part of it’s core platform.
Alex Benik from Encoded, and investor in the round, stated “Neurometric is tackling one of the most pressing problems in the AI ecosystem today. The team has a unique mix of AI talent and systems engineering experience that positions them well to take on this task and help companies optimize their token spend at multiple layers of their infrastructure.”
A New Discipline for the Agentic AI Era
Neurometric is positioning token engineering as the discipline of determining how each task within an AI workload should be completed based on the required quality, cost and speed.
Unlike prompt engineering, which focuses on improving the instructions given to a model, token engineering determines which model should receive a task in the first place and whether a more specialized model should be created to handle it.
The need for that capability is expected to grow as companies deploy more AI agents, individual workflows generate more model calls and the number of available models continues to expand.
“Frontier intelligence will keep getting less expensive, but companies will also consume far more of it,” May said. “The winners will not be the businesses that simply buy the most tokens. They will be the ones that know where advanced intelligence creates value and where a smaller model can do the job just as well.”
Availability
Neurometric’s automated token engineering platform is available now at neurometric.ai. The company will be meeting with customers, investors and media during the AI Engineer World’s Fair from June 29th to July 2nd in San Francisco, California.
About Neurometric
Neurometric is the automated token engineering platform, routing every AI task to the most cost-effective model that meets the required quality bar, and generating purpose-built small models when none exists. Learn more at neurometric.ai.
Forward-Looking Statements
This release may contain forward-looking statements. Actual results may differ. Neurometric assumes no obligation to update such statements.
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SOURCE Neurometric AI