Beyond the Pilot: Neurometric’s Bid to Solve AI’s Economic Reality

NEW YORK, NY – June 25, 2026 – In the race to deploy artificial intelligence, many companies have celebrated the successful launch of pilot programs. But as these AI agents move from the lab to the production line, a harsh economic reality is setting in. The staggering cost of running these systems at scale is a primary reason an estimated 95% of generative AI pilots fail to deliver a measurable return on investment. Addressing this critical challenge, AI infrastructure startup Neurometric AI today announced the launch of its automated token engineering platform, backed by a $4 million funding round.

The company is tackling what many in the industry see as the next great hurdle for AI adoption: making it economically viable. While the capabilities of large, frontier models are impressive, their operational expense can be prohibitive. A single complex workflow for an AI agent can trigger dozens of individual model calls, and the default strategy of sending every query to the most powerful—and most expensive—model available is proving unsustainable.

"Companies have spent the past year proving that AI agents can perform increasingly complex work," said Rob May, CEO of Neurometric. "Now they have to prove the economics still make sense when those agents are operating at scale."

The High Cost of AI Ambition

The gap between a promising AI demo and a profitable, scaled deployment is littered with unforeseen costs. Enterprise spending on LLM APIs has exploded, doubling from $3.5 billion to $8.4 billion in just six months and showing no signs of slowing. This isn't just about paying API bills; it's about the inefficiency baked into the current approach.

Using a state-of-the-art frontier model for a simple task like classifying an email or extracting a date is the computational equivalent of using a supercomputer to run a pocket calculator. It works, but the waste is immense. This mismatch is a core driver of the high failure rate for agentic AI projects, with some analysts predicting over 40% will fail to reach production by 2027 due to the sheer cost and complexity of deployment.

This is the problem Neurometric aims to solve. The company argues that the industry needs to move beyond simply building powerful models and focus on the intelligence of the system that deploys them. This requires a new discipline, one they are calling "token engineering."

From Prompt to Token Engineering

For the past year, the AI community has been obsessed with "prompt engineering"—the art of crafting the perfect query to get the desired output from a model. Neurometric proposes that the more critical question isn't how you ask, but who you ask. Token engineering, as the company defines it, is the discipline of determining which model should receive a task in the first place, and whether a more specialized model should be created to handle it.

"Every model call is also a pricing decision, and those decisions compound across an agent's workflow," May explained. "Token engineering gives companies a way to control that cost without sacrificing quality."

Neurometric's platform automates this decision-making process. It acts as an intelligent switchboard for AI tasks. Its Task Endpoint Manager evaluates incoming requests against a continuously updated database of model performance and pricing. It then routes each task to the most cost-effective model capable of meeting the customer’s specified requirements for accuracy, cost, and latency.

This approach allows companies to use expensive frontier models surgically, only for the complex, nuanced tasks that demand their power. More routine and repetitive sub-tasks—like classification, data extraction, or formatting—are routed to smaller, faster, and dramatically cheaper alternatives.

Automating the 'Jagged Frontier'

When a suitable off-the-shelf model doesn't exist, Neurometric's platform has another tool: the Auto-SLM Creator. This component can automatically build, train, and serve a small language model (SLM) designed for a specific, narrow task. This is crucial for navigating what May calls the "Jagged Frontier of Inference," where the performance of any given model is highly task-specific and often unpredictable. A massive frontier model might excel at writing poetry but prove less accurate (and far more expensive) than a purpose-built SLM for parsing legal contracts.

By creating a marketplace of these specialized SLMs and providing the tools to generate new ones on the fly, Neurometric is building a system that adapts to the work. Early results from customer engagements are compelling. The company reports that models routed or created through its platform have achieved accuracy rates that beat frontier models by as much as 20 percentage points for specific tasks, while reducing costs by 80% or more and improving latency by a factor of four.

"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," stated Neurometric COO Calvin Cooper. "That discipline will determine whether agentic AI can move from promising pilots to a business model that scales."

Building the Infrastructure for a Scalable Future

The $4 million funding round, which closed earlier this year, included participation from notable firms like Betaworks, Encoded, and Everywhere.vc, as well as prominent angel investors Jason Calacanis and Dharmesh Shah. The capital injection signals strong investor confidence in the need for this new layer of AI infrastructure.

"Neurometric is tackling one of the most pressing problems in the AI ecosystem today," said Alex Benik from Encoded. He noted that the team's unique mix of AI research and systems engineering experience positions them well to solve the economic puzzle of AI at scale.

This investment comes at a time when venture capital is pouring into AI infrastructure, from data centers to specialized chips. Yet, hardware is only part of the equation. Without intelligent software to manage the immense computational resources being deployed, much of that investment will translate into waste. Platforms like Neurometric's represent the crucial software layer that optimizes resource allocation, turning raw computing power into efficient, cost-effective business outcomes.

The challenge is significant. The number of available AI models is growing exponentially, making manual evaluation and selection an impossible task for any human engineering team. As May put it, "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."

By creating a system that does this automatically, Neurometric is not just selling a cost-saving tool; it is proposing a foundational architecture for the future of applied AI—one where intelligence is not just powerful, but also practical and profitable.