The 'Prove It First' Gambit: Keystone.AI Bets on De-Risking Enterprise AI

BELLEVUE, WA – July 07, 2026 – For the past decade, manufacturers have been fighting a losing battle. Despite billions invested in digital transformation and planning systems, a stubborn problem persists: inventory has grown approximately 40% faster than sales in the U.S. This quiet crisis has trapped trillions of dollars in working capital on balance sheets, capital that could be used for innovation, expansion, or weathering economic shocks.

Into this landscape of high stakes and frustrated investment comes a new proposition from Keystone.AI, an enterprise software company spun out of the global advisory firm Keystone. The company today announced “Proof Before Production,” a 12-week program that offers to prove its AI platform can unlock millions in cash flow using a manufacturer’s own data—before a single dollar is spent on a full-scale production license. It’s a bold, “try before you buy” gambit in a sector accustomed to long, costly, and often uncertain technology deployments.

A Cure for 'Pilot Purgatory'

The central challenge for enterprise AI adoption has never been a lack of promise, but a surplus of risk. Executives are all too familiar with the cycle of “pilot purgatory,” where promising technologies demonstrate potential in controlled environments but fail to scale, bogging down in lengthy implementations, data integration nightmares, and an inability to prove a clear return on investment. This has created a deep-seated skepticism toward vendors promising revolutionary outcomes.

Keystone.AI's new model is engineered to dismantle this skepticism. By offering a time-boxed, 12-week engagement, the company is shouldering the burden of proof. The process is straightforward: a manufacturer provides its own historical ERP data, Keystone.AI’s Deep Enterprise™ AI Platform (DEEP™) converts it into AI-ready signals, runs a backtest against actual historical demand, and produces a verified business case detailing the precise amount of cash it can release.

"Most enterprise AI buyers pay for tools and wait for outcomes, often over long implementation periods," Brad Miller, President of Keystone.AI, stated in the announcement. "Proof Before Production flips it. We prove the value of our platform on our customers' data, before we talk about production." This is more than just marketing rhetoric. Miller, who previously served as CIO at Moderna, was an inaugural customer of the technology, giving him a unique perspective from both sides of the table. His move to lead the newly independent Keystone.AI signals a deep conviction in the platform’s efficacy.

Critically, the DEEP™ platform is designed to deploy inside a customer’s existing cloud environment, sitting alongside legacy systems. This “no rip-and-replace” approach is a direct appeal to CIOs haunted by the specter of disruptive and expensive overhauls to core ERP systems, a common barrier to adopting new technology.

Unlocking the Balance Sheet

While the program’s structure is a strategic innovation in itself, its true appeal lies in the financial outcomes it promises. For years, manufacturers have been caught in a difficult trade-off. To lower unit costs, they rely on bulk production runs and long lead times. But this locks in decisions months before actual customer demand is known, forcing them to build massive inventory buffers to protect service levels against market volatility.

"In spite of investment in planning systems, decision intelligence, and agentic AI, manufacturers still don't trust their forecasts," explained Keystone.AI CEO Greg Richards. "They have no choice but to spend hours in collaborative planning sessions and to rely on buffers that result in millions of dollars in excess inventory."

Keystone.AI claims its platform can break this cycle. In previous deployments with large global manufacturers, the company reports uncovering between $47 million and $240 million in working capital. It achieved this by reducing forecast errors by 20% to 60% and cutting inventory days by 25% to 40%—all without sacrificing customer service levels. These aren't just operational metrics; they represent a direct injection of liquidity back into the business, a prospect that resonates powerfully in the C-suite.

By framing the conversation around freeing up capital rather than just improving efficiency, Keystone.AI is elevating the discussion from the factory floor to the boardroom. The pitch is aimed as much at the Chief Financial Officer as it is at the Chief Supply Chain Officer.

From Reactive Signals to Predictive Intelligence

What enables these results is a fundamental shift in how the AI approaches the problem. Traditional forecasting systems, even those with some machine learning capabilities, are often limited to extrapolating from aggregated historical sales data. They struggle with increasing product variety, lumpy demand patterns, and external shocks, forcing human planners to constantly override their recommendations.

Keystone’s DEEP™ platform operates on a more granular level. According to the company, it ingests raw transactional data—orders, shipments, invoices, and deliveries—and encodes them as a series of events. By creating unified timelines across customers, SKUs, and locations, it builds a model of underlying customer behavior that is often invisible in high-level reports. The platform isn't just guessing what will happen next; it's building a predictive model of why it will happen.

This approach aligns with the broader evolution of enterprise AI. According to analysts at Gartner, the market is rapidly moving toward “agentic AI”—more autonomous systems that can plan, act, and adapt with minimal human intervention. While DEEP™ still recommends decisions for human approval, its ability to analyze complex constraints and SKU economics to propose an optimal plan is a significant step in this direction.

The company’s pedigree, born from a technology and economics advisory firm that has served 40% of the Fortune 500, provides a foundation of strategic and economic rigor. This isn't just a technology in search of a problem; it's a solution forged from years of analyzing complex digital ecosystems. For manufacturers long caught between the risk of a costly AI investment and the certainty of capital trapped in inventory, this 'prove it first' approach may finally represent the key to unlocking their data vaults.