AI / ML AI / ML
AI (Artificial Intelligence) will be utilized to achieve digital transformation.
Service Service
AI technology is used to improve operations and operational efficiency.
Not only can sensor data acquired by IoT systems be visualized, but AI analysis can also discover invisible relationships.
In addition, by multiplying operational data and time-series data acquired from other devices, it is possible to predict equipment failures and other problems.
In addition, by multiplying operational data and time-series data acquired from other devices, it is possible to predict equipment failures and other problems.
By analyzing employee attendance, traffic flow data, and human resource data using AI technology, the activity status of employees can be quantified.
By predicting employee motivation, health risk, retirement risk, and other factors, the system can be used to improve corporate productivity and reform work styles.
By predicting employee motivation, health risk, retirement risk, and other factors, the system can be used to improve corporate productivity and reform work styles.
In retail and e-commerce sites, it is necessary to forecast daily demand and purchases from various angles. AI analysis of customer POS data, behavioral data, and site browsing logs can be used to discover new KPIs.
In financial and insurance services, it is important to find valuable data from huge amounts of big data.
Increasing profitability can be achieved by forecasting financial risks and improving the efficiency of screening and underwriting operations.
Increasing profitability can be achieved by forecasting financial risks and improving the efficiency of screening and underwriting operations.
- Knowledge modelingby machine learning
- AI-based inference and KPI discovery
- Highly automated decision-making
Total Support Total Support
We provide total support for planning, design, and operation of AI systems.
Consulting
Study Phase
- Organize AI system implementation objectives and requirements
- Organize existing systems
- Consideration of improvement measures by AI
Design
Design Phase
- Determination of implementation policy
- Selection of learning model - supervised/unsupervised
- Platform Selection
- Algorithm and API selection
- Prototype Verification
Development
Introduction Phase
- Building an AI Environment
- Learning Model Implementation
- system development
- Modification of existing systems
- API Customization
Operation
Operation Phase
- Implementation of business operations
- Measurement of Effectiveness
- Review of operational flow
- Improved accuracy and functionality