AiV Selected for Korea's Scale-Up TIPS Program to Accelerate Industrial AI Innovation

AiV, a provider of AI-powered automation solutions for manufacturing and logistics, announced that it has been selected for the Scale-Up TIPS program, an initiative sponsored by Korea's Ministry of SMEs and Startups (MSS). The selection follows the company's recent KRW 21 billion (USD 15 million) Series C funding round, marking another major milestone in AiV's growth.

Scale-Up TIPS is a government-backed program designed to support the growth of high-potential technology companies through collaboration between private investment firms and R&D organizations. Companies that receive more than KRW 1 billion in private investment from certified operators become eligible for additional government funding to accelerate research and commercialization.

Through its selection, AiV will receive KRW 1.2 billion (approximately USD 900,000) in government R&D funding over the next three years.

As part of the Scale-Up TIPS project, AiV plans to further advance its core AI and optical technologies for industrial quality inspection and logistics automation. The company will focus on next-generation research in unsupervised learning algorithms, Polyview optical technology, and its industrial MLOps platform, AiVOps, with the goal of accelerating AI adoption across manufacturing environments.

AiV also plans to deploy newly developed deep learning models through AiVOps, enabling equipment manufacturers and end customers worldwide to develop, deploy, and operate AI models on the platform. This strategy will strengthen AiV's platform-centric global expansion.

Sung Minsoo, CEO of AiV, commented:

"AiV has consistently driven product innovation in industrial deep learning-based inspection and logistics automation. Through the Scale-Up TIPS program and our recent investment, we will accelerate the commercialization of our MLOps platform while expanding AI adoption in manufacturing and logistics. Our goal is to automate industrial processes that have traditionally been difficult to automate due to technical limitations, ultimately strengthening our competitiveness in the global market through deep learning computer vision technologies."