AI Korea Inc.
Designed and operated by a HUFS faculty-founded company — end-to-end
across data, models, dashboards, and SaaS.
Built for environmental, public, and industrial decisions that require accountability.
OPERATING PROCESS
Public, observation, and client data unified in one pipeline.
Explainable AI (XAI) and uncertainty quantification (UQ), validated at journal-publication level.
Spatial-temporal decision assets, operated as always-on services.
Outcomes packaged into our own B2B products.
RECENT
A heterogeneous transfer learning framework that pre-trains on data from dissimilar countries to address the single-country data limitation in epidemic forecasting, validated on COVID-19 data from 30 countries. Sole author: Donghyun Lee.
AI Korea Inc. has signed a mutual R&D agreement with Green Vet Inc., a pet healthcare subsidiary of Green Cross, in veterinary AI diagnostics.
A reinforced explainable AI for algal-bloom forecasting under climate change, using a multi-run CAM (class activation mapping) approach. Corresponding author: Donghyun Lee.
SERVICES
Job-posting data, environmental GIS dashboards, trustworthy-AI design, and Homosilicus SaaS — data, models, dashboards, and SaaS, operated end-to-end by one team.
Trustworthy AI
Research-grounded · HUFS spin-off
Job postings collected, cleaned, tagged, and delivered as actionable data.
Air, water, marine, and public-health indicators at spatial-temporal resolution.
Our XAI and UQ consulting line — J. Cleaner Production 2024 and 2025 methods applied to client systems.
Our B2B SaaS — synthetic personas validate products, pricing, and UX in 10 minutes.
PARTNERS
We collaborate with public, research, medical, and industry partners.
WHY US
01
A HUFS faculty-founded company — the trustworthy-AI methods in J. Cleaner Production 2024 and 2025 are the same methods we ship.
02
Job posting data, environmental GIS dashboards, trustworthy AI design, and Homosilicus — four product lines operated in-house. No vendor chains to orchestrate.
03
Explainable AI (XAI) and uncertainty quantification (UQ), built for public-, government-, and industry-sector decisions that require accountability.
RESEARCH
Three SCIE papers authored under AI Korea Inc. affiliation — the XAI and uncertainty-quantification methods among them power our consulting, dashboards, and SaaS.
Lee D. · Expert Systems with Applications · 332:133728
A heterogeneous transfer learning framework that pre-trains on data from dissimilar countries to address single-country data limits in epidemic forecasting, validated across 30 countries.
View on DOILee D., Jeon H. · Journal of Cleaner Production · 529:146805
An enhanced explainable AI for algal-bloom forecasting under climate change, using a multi-run CAM approach to extract consistent model rationales.
View on DOILee D., Lee B. · Journal of Cleaner Production · 473:143457
A reliable AI framework that quantifies uncertainty in deep-learning particulate-matter predictions, supporting policy and operational decision making.
View on DOIProject scope, timeline, and budget can be tailored to your needs. We reply within 1–2 business days.
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