The core purpose of data analysis lies in prediction and interpretation.

To address pain points across various business processes, we identify the root causes and propose tailored solutions. We also develop analytical models and optimize processes based on these models to enhance productivity, improve quality, and reduce costs across multiple domains.

To achieve this, we continuously research and apply machine learning and deep learning algorithms—not only for structured numerical data, but also for unstructured data such as images, videos, speech, and text—delivering meaningful insights to our clients.

At ATTIC, we strive to help our clients gain a deeper understanding of their business through data-driven insights, enabling them to discover new opportunities and gain a competitive edge.

We carry out projects that predict NOx and SOx concentrations in exhaust gases from factories to trigger alarms in advance or identify optimal operating conditions to reduce emissions. We also conduct analysis projects to predict the quality of discharged water after chemical or biological treatment and to propose optimal operating conditions for improved efficiency and energy savings.

In the energy sector, we have developed AI-based optimization models to improve operational efficiency at biogas plants and wastewater treatment facilities. Additionally, we’ve built and implemented models for forecasting solar power generation at distributed plants, as well as early fault detection models for more efficient maintenance.

Through these efforts, we aim to move beyond experience-based operation toward more accurate, stable, and efficient systems.

Risk analysis–including anomaly detection, reliability prediction, and predictive maintenance through equipment failure forecasting–is one of ATTIC’s key areas of research.

We build reference sets from normal-condition data and use them as a basis for modeling each key variable.

By analyzing the error distribution between real-time data and model estimations, we can predict abnormal conditions. A deep understanding of the characteristics of various data collected from equipment is crucial for effective model implementation.

To obtain the benefits of process improvement through analytical models, these models must be implemented as systems and applied in real-world settings.

ATTIC supports clients across all stages of data-focused transformation—from developing automated machine learning solutions to ensuring effective operational management.

In building solutions, we focus on accurately understanding client needs to deliver an enhanced user experience. We have developed a modular machine learning pipeline that enables rapid development of desired features.