Ascerta Raises $18M to Prove Which AI Spend Pays Off

Ascerta, the enterprise AI management company formerly known as Pay-i, has raised an $18M Series A led by Dell Technologies Capital. Hitachi Ventures, BGV, Wipro Ventures, and earlier investors participated in the financing, which brings Ascerta's total funding to $22.9M.

The September 30, 2026 announcement arrives with a wider product mandate. Ascerta began by helping enterprises understand the cost of generative AI applications. It is now building a system that connects AI adoption, activity, spending, and business outcomes so leaders can decide which initiatives deserve to scale.

The capital will support product development, engineering, go-to-market expansion, and integrations across enterprise AI tools. Ascerta is entering a consequential part of the market: the measurement layer between technical AI activity and the financial decisions that keep, fix, or stop a program.

What Ascerta Announced

The $18M Series A was led by Dell Technologies Capital, with Hitachi Ventures, BGV, Wipro Ventures, and earlier investors participating. The company did not disclose a valuation or detailed deal terms.

The new round follows a $4.9M seed that Pay-i announced in May 2025. Fuse Partners and Tola Capital co-led that earlier financing, with Firestreak, Pear VC, Gaia Capital, and angel investors participating. The two rounds together account for Ascerta's reported $22.9M in total funding.

Ascerta is based in Bellevue, Washington, and is led by co-founders David Tepper, CEO, Doron Holan, CTO, and Erik Winters, COO. The company's leadership page describes a team shaped by enterprise AI, cloud infrastructure, and early-stage go-to-market work.

Why Pay-i Became Ascerta

The rebrand is more than a cosmetic change. Pay-i described a starting point in AI cost management. That job mattered because model providers, cloud platforms, applications, and agents can each create different charges, discounts, and usage patterns. Cost, however, answered only one part of the operating question.

Customers also needed to understand adoption, the work AI performed, and the business outcomes attached to that work. In the company's rebrand explanation, Tepper organized the problem around inputs, activity, and outcomes. Ascerta now calls the resulting category Enterprise AI Management.

That label makes a specific claim about organizational accountability. Engineering teams may see model calls, tokens, errors, and latency. Finance sees contracts and infrastructure commitments. Business units see workflow changes and hoped-for productivity. A decision about whether to scale an AI initiative becomes difficult when each group is working from a different record.

How the Platform Connects AI Work to Value

Ascerta has organized the platform around three products. Atlas measures adoption, value, return on investment, and waste across individual workflows and the broader AI portfolio. Forge examines how engineering teams use coding agents and whether adoption translates into productivity. Convoy helps companies manage provisioned AI capacity, consolidate workloads, and add new uses without disrupting production.

The company says the platform connects to homegrown applications and common enterprise AI systems, including Microsoft Copilot, Amazon Bedrock AgentCore, Salesforce Agentforce, GitHub Copilot, Claude Code, and Codex. Ascerta tracks activity by person, team, tool, and use case, then connects that activity to costs and intended business outcomes.

That last connection is the commercial center of the product. Token counts can explain consumption. Agent runs can describe activity. Neither tells a CFO, CIO, or business owner whether a process improved enough to justify the next dollar. Ascerta is attempting to make that decision traceable across technical and financial systems.

Customer Evidence and Its Limits

Ascerta names Atos and Wipro among its customers and says it also works with global insurance carriers. Across customers, the company reports average improvements of 47% in AI-initiative ROI, 24% in agent-launch time, and 86% in wasted AI spend.

Those figures are company-reported and should not be treated as an independent audit. They are useful evidence of the outcomes Ascerta is selling, but the durability of those results across industries, tool stacks, and deployment maturity remains an open operating question.

GeekWire reported two company-provided examples. One customer found agent runs that averaged $0.40 but sometimes spiked to $70. A global insurance carrier reportedly saved approximately $3M by consolidating AI capacity commitments. Both examples illustrate why granular measurement matters, while still relying on the company's account of customer performance.

Why Dell Technologies Capital Led the Round

Dell Technologies Capital describes Ascerta as a potential system of record for AI value creation. The phrase matters because systems of record become influential when several functions depend on the same information to make different decisions.

The other strategic investors strengthen that enterprise context. Hitachi Ventures and Wipro Ventures operate around large technology and services ecosystems, while Wipro is also identified as an Ascerta customer. The announcement does not disclose any new commercial commitments tied to those investments, so the strategic fit should not be confused with a guaranteed distribution channel.

The financing is still a bet that AI management will become a durable software category. Enterprises are adding copilots, coding agents, internal applications, models, and reserved infrastructure faster than many finance and operating systems were designed to evaluate them. Ascerta's opportunity is to become the connective record before that complexity hardens into another collection of disconnected dashboards.

What the $18M Changes

Ascerta plans to expand its platform, engineering team, go-to-market capacity, and integrations with major enterprise AI tools. More coverage should make the system useful across a larger share of an enterprise's AI estate, but broader scope will also raise the burden of attribution.

Measuring AI value requires more than observing activity. Business outcomes can depend on process design, adoption quality, data access, human review, and changes elsewhere in the organization. Ascerta will need to show that its measurements remain credible when causality is messy and internal stakeholders disagree about what success means.

That is the live consequence of the Series A. Ascerta is moving from a tool that explains spend toward a platform that may influence which AI programs receive more capital. The more important those decisions become, the more its customers will expect the measurement layer itself to withstand scrutiny.