Dnotitia Inc., a company that works on advanced AI memory and semiconductor-based solutions, has raised KRW 90 billion (US$61.2 million in its Series A funding round.
With this new funding, the company plans to grow its AI storage business, focusing on its key technologies called Seahorse and VDPU.
The funding round was led by Elohim Partners, along with several other investors like Kiwoom Investment, Starting Line, Maple Investment Partners, and more. Existing investors also invested again, showing strong trust in Dnotitia’s technology and its progress in bringing products to the market.
Dnotitia is growing its AI storage business using two main technologies: Seahorse and VDPU. Seahorse is a type of database that helps store and search data efficiently, while VDPU is a special chip made for handling vector data.
The company says VDPU is the first chip of its kind in the world. It is designed to make data searching and processing faster, especially in generative AI systems where large amounts of data can slow things down.
“AI has so far evolved around compute, but memory is becoming increasingly important. At Dnotitia, that means how data is stored and how it can be quickly retrieved and used when needed,” said MK Chung, CEO of Dnotitia. “With this investment, we will accelerate our AI storage business built on Seahorse and VDPU as we continue to build Dnotitia into a key player in next-generation AI infrastructure.”
“Dnotitia is one of the few companies developing a vector database, a SaaS platform, and a dedicated vector data processing chip to address bottlenecks in AI infrastructure,” said Jaeho Lee, CEO of Elohim Partners. “We made this investment after reviewing the company’s technology maturity, progress toward commercialization, and the continued participation of existing investors.”
Founded in 2023, Dnotitia is a technology company that develops advanced AI and data storage solutions. In simple terms, it focuses on helping computers store, manage and quickly access large amounts of data which is crucial for modern AI systems.
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