Korea Deep Learning Secures USD 7.4 million Series A Investment, Poised to Lead Global AI Market


Korea Deep Learning, a visual intelligence AI integrated solutions company for public and corporate sectors, announced that it has successfully raised USD 7.4 million (10 billion KRW) in Series A funding.

This investment round was led by TransLink Investment, with SV Investment as a key co-investor, and Industrial Bank of Korea also participating. This marks Korea Deep Learning’s first venture capital investment in five years since its establishment. Until now, the company had grown primarily through its own revenue, with the exception of USD 74,074 (100 million KRW) received for TIPS program support. During this period, it achieved high growth, averaging 400% annually, accumulating USD 7.4 million (10 billion KRW) in revenue and securing over 80 clients.

Over the past five years, Korea Deep Learning has developed document processing automation solutions based on its proprietary Vision-LLM (Vision-Language Model), trained on large-scale text and image documents. Its technological prowess has been verified through the establishment of document automation, structuring, and search systems in practical environments across various domains, including public institutions, healthcare, and finance.

Launched in March of this year, “Deep Agent,” a document-based AI agent, is a next-generation document AI solution that surpasses the limitations of existing OCR by simultaneously interpreting the visual structure and linguistic meaning of documents to perform practical business actions. For example, when a contract is uploaded, it automatically extracts key clauses, analyzes risks, and sends notifications to the responsible person. It can be implemented within two weeks without separate data collection or training, and it reduces document processing time by over 80% with an accuracy level of 97-99%. With this technology, it secured contracts worth approximately USD 814,815 (1.1 billion KRW) within two months of its launch, proving its market viability.

Recently, the company has been strengthening its technological competitiveness by being selected as an NVIDIA Inception program member and recruiting AI researchers from KAIST. The investment will be primarily allocated to advancing VLM technology specialized in documents and securing key talent. The plan is to expand the AI engineer organization to develop foundation-based document understanding models, comprehensively enhance OCR pipelines, inference optimization, and MLOps pipelines. Additionally, localization efforts and the establishment of overseas partnerships are planned for global market expansion.

Korea Deep Learning is currently conducting large-scale recruitment, primarily for its AI technology development team. The company emphasizes a culture where individual developers are the true owners of projects, providing an environment where even new developers can grow into core contributors. They can directly design and implement technologies linked to actual products, such as AI OCR and RAG-based search systems.

TransLink Investment stated, “Korea Deep Learning possesses unique Vision-LLM technology and rapid execution capabilities in the document AI sector. Having already proven its market potential through public and corporate clients, we decided to lead this investment, believing in its significant potential for rapid expansion in the global market.”

Jihyun Kim, CEO of Korea Deep Learning, said, “We have proven our business viability by securing USD 7.4 million (10 billion KRW) in cumulative revenue and 80 clients solely through our technology, without external investment beyond the TIPS program support. Through this investment, we will significantly expand our AI talent and advance Deep Agent into a global-level document AI platform. Ultimately, we will continue to lower the barriers to AI adoption so that all companies can be freed from complex document tasks.”

Meanwhile, this Series A round is currently ongoing, and the investment amount and participating investors may be added depending on the final closing time.

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