KONST, a Taiwan-based next-generation AI compute operator, has closed a US$30 million Series B funding round led by ADATA Technology, with strategic participation from M Mobility, Pegatron Venture Capital, and several other corporate investors. The company will use the proceeds to scale its AI data center buildout across Asia and accelerate its Token Factory strategy. The investment reflects rising demand for enterprise AI infrastructure that moves from initial pilots into daily operations.
Demand-side investors back enterprise AI adoption
The Series B round brings together backers from memory and storage, electrification solutions, electronics manufacturing, energy, semiconductors, and thermal management. Many of these corporate groups are themselves on the demand side of AI adoption and understand the infrastructure requirements from direct experience. KONST stated that beyond the capital, it places greater value on the synergies these investors offer across industry use cases and customer applications.
Ben Chang, co-founder and chairman of KONST, said the round is about more than the amount raised. He emphasized that KONST and its investors share a vision that AI must become part of how enterprises actually operate, not stall at proof of concept. Once AI enters daily operations, he added, computing power shifts from a one-time project expense to essential infrastructure that enterprises rely on every day.
Three layers turn compute into a service
KONST's Token Factory approach treats compute as production equipment and tokens as its output. At the foundation, Konstra AI covers site selection, the buildout of mechanical, electrical, and environmental control systems, GPU cluster tuning, and ongoing operations and maintenance. The new funding will help expand this build capacity across Asia.
The middle conversion layer, Glows.ai, breaks cluster compute into smaller units that enterprises can access with per-second billing and pay only for what they use. At the top, Horizon AI's (k) ATP Token manages governance and delivery, determining who can use what, how much, and how charges are applied. KONST believes this three-layer division of labor shows how Taiwan's hardware and supply chain strengths can extend from manufacturing compute to delivering it.
Governance layer addresses a key adoption bottleneck
KONST identifies governance as the most easily overlooked of the three layers and the point where enterprise AI adoption often stalls. The problem is not that models fall short, but that organizations cannot clearly say which department used which models, how much was spent, or what happened with each request. The (k) ATP Token is designed around the principle of 'One key. Every model.'
The platform uses a four-tier structure of organization, workspace, project, and key to allocate quotas down the hierarchy, set model access at the project level, and flag budgets before they are exceeded. Every request is recorded in audit logs with its model and usage details. For enterprises operating at scale, the platform brings every model under one contract and one invoice with custom pricing based on actual usage.
Focus on large enterprise adoption
KONST said Horizon AI focuses on sizable business groups and large organizations where AI adoption involves multiple departments, integration with existing systems, and long-term governance requirements. These customers need a partner that can work alongside them through the entire adoption cycle rather than a point solution. Horizon AI has already completed deployments for several enterprises and institutions and is conducting needs assessments with large customers across multiple industries.
With fresh capital and a group of strategic corporate investors, KONST is positioning itself to meet the growing enterprise shift from AI experimentation to routine operational use. Its three-layer model aims to connect compute supply, flexible cloud delivery, and usage governance under a single architecture. The company expects this approach to help large organizations adopt AI with clearer cost control, stronger oversight, and a faster path from pilot to production.