Givery's 'Track' Achieves 'iCD Compliant System' Certification from iCD Association

Givery Inc. (Headquarters: Shibuya-ku, Tokyo; CEO: Takashi Ide) announced that its skill intelligence platform 'Track' has been certified as an 'iCD Compliant System' by the iCD Association. This certification recognizes the system's effective use of the task/skill dictionary defined by the association.

Through this certification, 'Track' integrates the i-competency Dictionary (iCD), which covers major Japanese skill standards such as ITSS and DSS. This allows companies to define and visualize skills with high precision using a 'common yardstick' based on market standards rather than unique, proprietary criteria.

### Background In recent years, as human capital management and DX promotion have accelerated, there has been a growing need for decision-making support in talent and organizational management—specifically identifying 'who can do what' and 'where the gap is between current talent and future needs.' 'Skill Intelligence' addresses these needs by using AI and machine learning to analyze workforce data and convert it into actionable insights. The World Economic Forum (WEF) has even recommended skill intelligence as 'essential infrastructure for a resilient economy.'

Under the concept of 'Skills as an Operating System for Management Decisions,' Track links recruitment, training, and placement based on skill data. Its core value lies in supporting advanced decision-making, such as future talent portfolio forecasting and optimal placement simulation.

### Key Capabilities Enabled by iCD Compliance 1. **Building Detailed Skill Taxonomies**: By combining major domestic standards like DSS and ITSS with the comprehensive iCD dictionary, Track has strengthened its classification system. Job levels are defined in seven stages based on SFIA (Skills Framework for the Information Age). 2. **Enhanced Objectivity through Behavioral Assessment**: The system is designed for employees to report 'actual behavioral results' rather than subjective 'can/cannot' ratings, reducing bias in self-assessments. Furthermore, objectivity can be enhanced by combining options such as visualization surveys, job-specific assessments (Knowledge/Behavior), AI-driven interviews, and external data integration.