AX, From Insight to Action.

Better judgement, faster execution — changing how companies and industries run.

PARTNERS & CLIENTS
What We Do

AX · AI Transformation

We decide where AI belongs in your work, then carry its judgement through to action.
Six steps, from diagnosing the data to building and running it.

설비와 업무 시스템 데이터를 진단해 AI 적용 기회를 선별하는 모습
01

Finding the opportunity · Checking the data

We pick the work where AI pays off most, then check whether the data to train it actually exists.

  • Choosing candidates Work heavy with repeated decisions and waiting, ranked by expected gain and difficulty
  • Data check Judging trainability from the quality and labelling of equipment and business system data
  • Data collection Equipment connected over industrial protocols (OPC-UA, Modbus, MQTT), operating data organised
흩어진 데이터를 표준화해 의미 기반 검색과 데이터 관계로 연결하는 모습
02

Standardising data · Turning it into knowledge

Equipment, document and event data are organised under one shared meaning, forming a knowledge base that models and agents both draw on.

  • One data format Equipment names and codes unified, relationships defined through industry terms and an ontology
  • Documents and images linked Documents, drawings and images searched by meaning, with their data relationships visible
  • A shared vocabulary Time-series, events and text tied together so models and agents read the same thing
시계열과 문서 데이터에 적합한 AI 모델을 학습하고 비교하는 모습
03

Modelling · Training

We compare models against the task, the security rules and the operating conditions, choosing a setup that weighs accuracy against the cost of false alarms.

  • Model per task Time-series models for anomaly detection, predictive maintenance and forecasting; generative AI with RAG for documents
  • Combining models An in-house small language model (sLLM) and a commercial LLM API, combined to fit the security requirements
  • How we choose Experiment tracking and cross-validation, plus what a false alarm actually costs that team
운영 데이터로 AI 판단을 검증하고 승인 결과를 다시 학습하는 모습
04

Field validation · Human check

Field validationproves the gain, and human review and approvalthen sets how far it runs on its own.

  • Measuring the effect Before and after compared on the same basis, using real operating data (PoC)
  • Human in the loop The reasoning behind each judgement is checked; unclear cases feed the next round of training
  • Agreeing the thresholds Alarm levels and autonomy are tuned with the owning team, weighing false alarms against missed ones
AI 에이전트가 업무 시스템과 설비를 연결해 승인된 작업을 실행하는 모습
05

AI agents · Connecting the systems

AI is wired into MES, ERP and the equipment itself, so that analysis flows through to reporting, approval and action.

  • Into the work Queries, analysis and reporting through a natural-language agent; equipment faults follow detect → propose → approve → act → confirm
  • Choosing how to deploy Cloud, on-premises or air-gapped — whichever the security requirements call for
  • System integration Connected to MES and ERP by API so it works inside the existing flow
모델 성능을 감시하고 재학습·배포·되돌리기를 수행하는 AI 모델 운영 순환
06

Operating · Keeping it learning

We keep watching how the model behaves, and when accuracy slips we retrain and redeploy.

  • Watching data and accuracy Shifts in input data and model accuracy are tracked, signalling when to retrain
  • Versions and rollback Model versions and deployment history are kept, so a proven earlier version can be restored if something goes wrong
  • Continuous improvement User feedback and new data go back into training, and accuracy is verified again
Applications

Where it applies

We connect data and AI to real industry work so that the way you operate actually changes.

Manufacturing

AI analysis of production and quality datato optimise processes and automate quality

Enterprise

AI agents automate repetitive workand support knowledge use and decisions

Mobility

Using vehicle and traffic datato make movement and operations safer and more efficient

Energy

Analysing energy data and optimising demand and consumptionto run energy operations more efficiently

Public

Using public data and AIto automate civil, administrative and customer service work

BUILD CASES

Selected projects

See how data became judgement, and judgement became better operations.

Factory Insight AI 화면 예시
ROBOT MANUFACTURING

Factory Insight AI

Early fault detection on screw-fastening and pick-and-place robots — cause and remedy explored in conversation
  • LSTM Autoencoder
  • LLM Assistant
  • LightGBM
AI Smart Optimizer 화면 예시
PROCESS OPTIMIZATION

AI Smart Optimizer

Anomaly detection and thermal mass estimation for heat-treatment equipment — with recommended process settings
  • XGBoost
  • Transformer
  • Edge Gateway
제지 공정 AI 자율운영 화면 예시
AUTONOMOUS OPERATION

Autonomous operation of a paper mill with AI-OT

An autonomous operations platform joining equipment, energy, quality and safety across the paper process
  • AI-OT
  • Predictive Maintenance
  • MLOps
See all build cases