Ascerta Raises $18M Series A to Turn Enterprise AI Spend Into Measurable ROI

Ascerta has raised an $18 million Series A as it broadens its focus from AI cost management to a larger challenge: helping enterprises determine which AI investments are actually creating business value. The round was led by Dell Technologies Capital, with participation from Hitachi Ventures, BGV, Wipro Ventures and existing investors.

The financing brings the Bellevue, Washington-based company’s total funding to $22.9 million. It also arrives alongside a new name for the business, which was previously known as Pay-i. Ascerta says the rebrand reflects an expansion into what it calls Enterprise AI Management: a system for measuring AI cost, adoption and business outcomes across an organization.

That pitch lands at a moment when enterprise AI programs are moving beyond controlled experiments. Companies can count tokens, licenses, agent runs and lines of AI-generated code, but those operational metrics do not necessarily show whether an initiative is saving time, increasing revenue or improving a business process. As Unite.AI has previously examined, AI cost control becomes substantially harder as agentic workloads move into production. Ascerta is betting that finance and technology leaders now need a shared system for connecting those costs to results.

Moving from AI usage data to business value

Ascerta was founded in 2024 by Microsoft veterans David Tepper, Doron Holan and Erik Winters. Tepper, the company’s CEO and co-founder, spent 19 years at Microsoft and led generative AI strategy for internal use across Azure. Holan, CTO and co-founder, spent 27 years at Microsoft and worked on hyperscale throttling infrastructure designed to handle hundreds of billions of requests per day. Winters serves as COO and co-founder.

The company emerged from stealth as Pay-i in May 2025 with a $4.9 million seed round centered on AI cost management. Since then, Ascerta says conversations with enterprises revealed a wider problem. Organizations did not only want to know how much AI cost; they wanted to understand who was using it, what agents and models were doing, and whether the activity justified further investment.

Ascerta connects to AI systems already running across an enterprise, including internally developed applications and services such as Microsoft Copilot, Amazon Bedrock AgentCore, Salesforce Agentforce, GitHub Copilot, Claude Code and Codex. It then tracks AI activity through the work performed and into the business outcomes that activity is meant to influence.

The objective is to give CIOs, CFOs and AI leaders a common view of technical usage and financial performance. Rather than treating token consumption or model-call volume as an end in itself, the platform is designed to associate an AI use case with relevant key performance indicators and show which projects appear ready to scale, which require intervention and which should be stopped.

Three products for the enterprise AI lifecycle

Ascerta packages its capabilities into three products. Atlas measures AI value, adoption and ROI across individual workflows and larger portfolios. Forge focuses on how engineering teams use coding agents, giving leaders a way to connect adoption with development productivity. Convoy is aimed at organizations running their own AI capacity and is designed to help them allocate that infrastructure without disrupting production.

The company says its measurement goes beyond model pricing by accounting for elements such as sub-token costs, hidden fees and enterprise discounts. It can also break adoption and cost data down by person, team and tool. That level of detail is intended to expose a problem that aggregate spending reports can hide: two departments may use the same model while producing very different business returns.

Ascerta currently works with customers including Atos, Wipro and global insurance carriers, and it lists Microsoft, AWS, IBM, Slalom and Trace3 among its technology and services relationships. The company reports that its platform has helped customers improve returns on AI initiatives by 47%, shorten agent launch times by 24% and reduce wasted AI spending by 86%. Those figures are company-reported results rather than independent benchmarks, but they illustrate the economic outcomes Ascerta wants enterprises to monitor continuously.

Why investors see an Enterprise AI Management category

The Series A will support product development and growth as Ascerta attempts to establish Enterprise AI Management as a distinct software category. The timing is significant: AI agents are becoming embedded in coding, customer service and operational workflows, while the cost and accountability for those systems increasingly span technology, finance and business teams.

For Ascerta, the opportunity is not simply to produce another cloud-cost dashboard. Its larger argument is that enterprises need an operating layer for deciding where AI deserves more capital. If the company can make technical activity legible as business performance, it could help organizations move from enthusiastic experimentation to disciplined deployment—and give executives a clearer answer when the board asks what their AI investment is actually worth.