Ascerta, the enterprise AI management company formerly known as Pay-i, has raised $18 million in Series A funding as it broadens its focus from AI cost control to measuring business value. The round was led by Dell Technologies Capital, with participation from Hitachi Ventures, BGV, Wipro Ventures, and existing investors, bringing the company’s total funding to $22.9 million. The new capital will support product expansion and go-to-market growth as large enterprises seek clearer ways to assess returns on artificial intelligence spending.
From Pay-i to Ascerta
Founded in 2024 by Microsoft veterans David Tepper, Doron Holan, and Erik Winters, Ascerta was created to address the growing complexity of AI economics inside large organizations. The company emerged from stealth in May 2025 as Pay-i with a $4.9 million seed round and an initial focus on helping companies understand and manage AI costs. As enterprise adoption expanded across models, copilots, coding tools, and autonomous agents, the company widened its scope to include usage, productivity, and measurable business outcomes.
Building an Enterprise AI Management Platform
Ascerta’s platform is designed to give corporate leaders a unified view of how AI is being used, what it costs, and whether individual initiatives are delivering enough value to justify further investment. It connects with existing enterprise AI environments, including Microsoft Copilot, Amazon Bedrock AgentCore, Salesforce Agentforce, GitHub Copilot, Claude Code, Codex, and internally developed applications. The company then links AI activity to business metrics, allowing organizations to compare adoption, performance, and return on investment across teams, tools, and use cases.
Product Portfolio
The platform currently includes three products aimed at different areas of enterprise AI deployment. Atlas measures AI value, adoption, and return on investment across individual workflows and broader portfolios, while Forge focuses on engineering teams using AI coding agents and assesses how those tools affect productivity. Convoy is designed for organizations operating their own AI infrastructure, helping them optimize capacity, consolidate workloads, and support new use cases without disrupting existing production systems.
Customer Traction and Partnerships
Ascerta says its customer base includes Atos, Wipro, and global insurance companies, while its partner ecosystem includes Microsoft, AWS, IBM, Slalom, and Trace3. According to the company, customers using its platform have improved ROI on AI initiatives by 47%, reduced agent launch times by 24%, and cut wasted AI spending by 86%, although these figures are company-reported. The platform is also being used to identify failed agent runs, overlapping projects, unapproved AI use, and inefficient capacity that can increase enterprise costs.
Funding and Expansion Plans
The Series A will be used to expand Ascerta’s platform, strengthen its commercial team, and increase integrations with major enterprise AI products. The company plans to extend beyond measuring AI performance toward tools that can actively help organizations optimize how models, agents, and workflows are deployed. This strategy reflects a broader shift among enterprises from experimenting with generative AI toward demanding clearer evidence of financial and operational impact.
Market Positioning
Ascerta is positioning its Enterprise AI Management approach as an extension of traditional FinOps, which typically concentrates on infrastructure spending rather than business outcomes. Its model combines cost accounting with adoption data, productivity analysis, and links to specific organizational KPIs, giving executives a more detailed view of where AI creates measurable value. Dell Technologies Capital Managing Director Raman Khanna said enterprises are increasingly concentrating spending on initiatives that can demonstrate tangible results, creating demand for stronger measurement and governance tools.
With its new name and fresh Series A financing, Ascerta is moving beyond its original AI cost-management focus into a broader platform for measuring enterprise AI performance and value creation. The company is betting that as organizations deploy more agents, models, and AI-enabled workflows, executives will need clearer evidence to decide which initiatives should be expanded, improved, or discontinued. Its next phase will depend on whether it can turn that demand for accountability into a scalable category spanning finance, engineering, infrastructure, and business leadership.