R&D Agreement Signed with Green Vet Inc.
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.
AI Korea Inc.
Trustworthy AI,
built on research.
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
Four stages from research to production — one team, end-to-end.
- 01
Data
Public, observation, and client data unified in one pipeline.
JobAI Public APIs - 02
Model · Research
Explainable AI (XAI) and uncertainty quantification (UQ), validated at journal-publication level.
J. Cleaner Production 2024 · 2025 - 03
Dashboards
Spatial-temporal decision assets, operated as always-on services.
BirdFluAI Air-Report - 04
SaaS
Outcomes packaged into our own B2B products.
Homosilicus
- HUFS faculty-founded
- 2 SCIE papers
- 7 institutional partners
- End-to-end stack
RECENT
Latest
Published in J. Cleaner Production — Reinforced explainable AI for algal bloom forecasting (2025)
A reinforced explainable AI for algal-bloom forecasting under climate change, using a multi-run CAM (class activation mapping) approach. Corresponding author: Donghyun Lee.
Published in J. Cleaner Production — Reliable AI for PM uncertainty quantification (2024)
A reliable AI framework that quantifies uncertainty in deep-learning particulate-matter predictions, providing evidence for policy and operations. Corresponding author: Donghyun Lee.
SERVICES
Trustworthy AI for environmental, public, and industrial decisions.
Job-posting data, environmental GIS dashboards, trustworthy-AI design, and Homosilicus SaaS — data, models, dashboards, and SaaS, operated end-to-end by one team.
Job Posting Data
Job postings collected, cleaned, tagged, and delivered as actionable data.
- Hundreds of thousands of postings, deduplicated
- Tagged by role, region, and employer
- Delivered as API or data dumps
Visit site →
Environmental GIS Dashboard
Air, water, marine, and public-health indicators at spatial-temporal resolution.
- AI forecasting plus GIS visualization
- Validated with public partners
- REST API and embeddable widgets
Trustworthy AI Design
Our XAI and UQ consulting line — J. Cleaner Production 2024 and 2025 methods applied to client systems.
- Explainable AI · CAM-based
- Uncertainty quantification (UQ)
- Methods from J. Cleaner Production 2024 and 2025
Ask for details →
Homosilicus
Our B2B SaaS — synthetic personas validate products, pricing, and UX in 10 minutes.
- Infinite persona scaling
- Insights in 10 minutes
- Privacy-first architecture
Visit site →
PARTNERS
Partners
We collaborate with public, research, medical, and industry partners.
WHY US
Three differences — research, full-stack, Trustworthy AI.
01
We run the methods we publish.
A HUFS faculty-founded company — the trustworthy-AI methods in J. Cleaner Production 2024 and 2025 are the same methods we ship.
02
Data, models, dashboards, and SaaS — one team.
JobAI, BirdFluAI, Air-Report, and Homosilicus — four product lines operated in-house. No vendor chains to orchestrate.
03
We design AI you can put into decisions.
Explainable AI (XAI) and uncertainty quantification (UQ), built for public-, government-, and industry-sector decisions that require accountability.
RESEARCH
The methods we publish are the methods we ship.
Two SCIE papers in J. Cleaner Production, authored under AI Korea Inc. affiliation — the same methods power our consulting, dashboards, and SaaS.
2025 XAI Climate Environmental AI
Reinforced explainable AI for algal bloom forecasting under climate change: A multi-run class activation mapping (CAM) approach
Lee 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 DOI 2024 Uncertainty Air Quality Trustworthy AI
Building reliable AI for quantifying uncertainty in particulate matter predictions with deep learning
Lee 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 DOIWe operate the full AI stack — data, models, and dashboards — as one single team.
Project scope, timeline, and budget can be tailored to your needs. We reply within 1–2 business days.
Get in touch