R&D Achievements
Government-funded R&D projects and outcomes
Technical capabilities developed through national R&D and AI training datasets
Key metric
Selected as an operator of the national AI training-data program (AI Hub) six years running, 2020–2025
Main R&D areas
We apply multimodal content understanding, media asset management, and data refinement to industry needs.
Multimodal content understanding and language AI
We research AI and machine translation that understand the visual and cultural context of webtoons and video.
- Spatially aware translation of text in webtoon images
- LLMs that account for narrative and cultural context
- Multimodal translation using background, tone, and facial expression
Related project: MSS Startup Growth Technology Development program (strategic track)
Media asset management and semantic search
We extract people, dialogue, and mood from video and audio, then structure the information for natural-language search.
- Automatic metadata generation and semantic search
- Subtitle production and editing pipelines for broadcast and OTT content
Related project: MSIT broadcasting and communications technology R&D with industry partners
AI data refinement and industry AX
We refine unstructured data and design training datasets for specialized domains including law, finance, and patents.
- Automated noise detection and data refinement
- Dataset modeling for LLM training and evaluation
- Legal LLM data, financial parallel corpora, and patent classification data
Related work: AI Hub datasets for specialized domains
Government R&D projects & national AI dataset record
National R&D since 2017 has built our language and media AI capabilities. Through AI Hub, we have developed 10 datasets as lead organization and participated in 9 more.
Major national R&D projects (2017–2024)
AI training-data program — 10 datasets as lead organization (2020–2025)
AI training-data program — 9 datasets as participant (2022–2024)
Volumes follow public AI Hub information. Rounded figures are marked “approx.” Source, annotation, and evaluation items are counted separately and should not be summed across categories.
Applying research in industry
We apply research and validation results in LETR WORKS and use field feedback to inform further research.
Core research
Advancing the latest AI models and multimodal architecture algorithms.
Validation on national projects
Verifying data and technical stability through large government programs and industry–academia collaboration.
Deployment to the commercial platform
Commercialized as the core engine of the LETR WORKS media intelligence platform.
Open research partnerships
We work with research institutions, universities, and local governments on content AI and validation for industry-specific AI transformation.