Ascerta Secures $18M to Expand AI Measurement Beyond Cost Tracking
Bellevue-based Pay-i has rebranded as Ascerta and raised $18 million in Series A funding to extend its platform from AI cost management to broader enterprise AI ROI measurement. The round was led by Dell Technologies Capital with participation from Hitachi Ventures and others, bringing total funding to $22.9 million. The startup, founded by former Microsoft executives, helps enterprises identify hidden costs and optimize AI spending across their operations.
Ascerta emerged from stealth in 2025 as Pay-i, initially focusing on identifying and reducing AI infrastructure expenses for enterprises. The company's founding team drew deep expertise from Microsoft, where the CEO spent nearly two decades managing generative AI deployment across Azure infrastructure, while the CTO previously designed systems managing hundreds of billions of daily requests. Real-world customer experiences revealed the platform's value beyond cost control—one enterprise discovered individual AI agent executions costing $70 when typical runs cost $0.40, while an insurance company achieved approximately $3 million in savings by optimizing cloud capacity reservations.
The rebrand signals the company's evolution toward comprehensive AI investment management. Rather than treating AI spending as purely a cost center, Ascerta now helps enterprises evaluate which AI tools drive measurable business outcomes and determine which initiatives warrant scaling. This shift reflects broader enterprise challenges as AI adoption accelerates and organizations struggle to demonstrate concrete return on AI investments beyond operational metrics.
Enterprise AI governance tools like Ascerta may reshape how organizations approach AI spending and accountability. As companies deploy increasingly complex AI systems across multiple departments, platforms offering visibility into actual business impact could influence budgeting decisions and encourage more disciplined AI adoption. This could reduce wasted spending on ineffective tools while simultaneously pressuring organizations to demonstrate ROI from AI initiatives, potentially slowing adoption in areas where business value remains unclear.