Dream Arts Announces AI Practical Application Concept 'DAPA 2.0', Evolving SmartDB into an 'AI Ready Data Generation Infrastructure' through 'Semantic DX'

Dream Arts, a cloud service provider for large enterprises, has announced 'DAPA(R) 2.0', the latest iteration of its concept for practical AI implementation, 'DAPA(R) (DreamArts Practical AI(R))'.

DAPA(R) is the company's core concept for making AI practical, actionable, and functional. Since its first introduction in June 2025, it has been positioned as a framework for continuously tackling the question of 'how to integrate AI into business operations', with ongoing updates. 'DAPA(R) 2.0', the latest version, focuses on the cloud service for business digitization, 'SmartDB(R)', and outlines a path to evolve SmartDB(R) into an 'AI Ready Data Generation Infrastructure' through 'Semantic DX', which incorporates meaning, context, and authority into business data.

With DAPA(R) 2.0, in addition to the two advantages Dream Arts has built over the past 20 years—'critical business data' and 'authority management ledgers'—the company aims to achieve a new competitive edge by continuously generating 'AI Ready Data' that AI and AI agents can safely and practically utilize.

Furthermore, the company plans to sequentially implement core supporting technologies, including vectorization within 2026 and semantic technology by June 2027.

Key Points of this Release - Announcement of 'DAPA(R) 2.0', a concept to evolve SmartDB(R) from a System of Record (SoR) into a System of Decision Intelligence (SoDI). - Core elements include 'Semantic DX'—which adds meaning, context, and authority to business data—and the resulting 'AI Ready Data'. - Moving beyond theory to action: sequentially beginning the implementation of core technologies (vectorization/semantic technology). The first phase is scheduled to start within the next year.

Background: The Next Hurdle in AI Adoption is 'Meaning' and 'Context' While many companies have adopted AI, many also report that it is 'not effectively utilized in actual business operations'. One reason is not the quality of the AI model, but the quality of the business data provided to it. Missing background on 'why a decision was reached', lack of connected context across departments or systems, and generic AI's lack of understanding of organizational-specific vocabulary such as 'approval', 'routing', and 'decision-making authority'—these issues create performance gaps.

'Having data' and 'understanding the meaning' are different things. Dream Arts believes that as a stage beyond the 'digitization, visualization, accumulation, and integration' promoted by traditional DX, it is necessary to promote 'Semantic DX' to bring meaning and context to the data itself.

DAPA 2.0: Evolving SmartDB into an 'AI Ready Data Generation Infrastructure'—The 'Third Corner' For 20 years since the release of SmartDB(R), Dream Arts has built sustainable competitive advantage based on the idea of where the 'corners of the Othello game' are—areas that others cannot easily imitate.

Corner 1 'Data': Serving as the core infrastructure storing critical business data for large enterprises. Corner 2 'User Master/Authority Management Ledger': Ability to handle complex organizational and authority structures specific to Japanese large enterprises, including group companies, subsidiaries, and external personnel.

These two corners have provided the advantage that supports long-term utilization and enterprise-wide expansion. With the arrival of the AI agent era, the company adds Corner 3, 'AI Ready Data Generation Infrastructure', to gain a new competitive edge. By using SmartDB(R) daily, AI-safe, meaning-infused business data (AI Ready Data) is continuously generated. This is a field with high entry barriers for others, achievable only because of Corner 1 (data) and Corner 2 (authority management).

'AI Ready Data' refers to business data that is not just structured or digitized, but is prepared for AI or AI agents to safely and practically use by meeting conditions such as business meaning, process positioning, relationship, authority, basis for decision, and an AI-processable format.