Decision AI
Beyond Analysis, Toward Decisions
Decision-Making Beyond Data Analysis
From Intelligence
to Actionable Decisions
Using your organization's data and business context, Decision AI analyzes, prioritizes, and recommends — enabling faster, more consistent decisions where the work actually happens.
Analysis Exists Everywhere,
But Decisions Remain Difficult
Scattered Information, Complicated Decisions
Decisions Require More Than Data
- Organizations secure a variety of analysis results and insights, but the process of turning them into actionable decisions remains complex.
- Repetitive review and consultation delay decision-making, and analysis results fail to connect to actual response and execution.
Too Many Signals, Not Enough Priorities
- Data and events continue to increase, but it is difficult to quickly identify meaningful signals that account for both the relationships between information and its importance.
- A lack of the structure and criteria needed to comprehensively analyze the relationships and impact between information and to judge priorities delays response and increases the burden of decision-making.
Decisions Vary by Expertise
- Even with the same data and situation, the interpretation of information and criteria for judgment can vary depending on the individual's experience and level of domain understanding.
- Judgment criteria that rely on individual experience and tacit knowledge make consistent organizational-level decision-making difficult and are hard to accumulate and leverage as organizational knowledge.
How S2W Transforms
Analysis into Action
From Analysis to Action: The S2W Approach
Decision Briefing
S2W's decision-making agent reconstructs diverse data and analysis results into actionable briefings suited to an organization's business context. Using an ontology-based knowledge structure and domain-specific AI, it understands the relationships between information and provides key issues and the grounds for judgment.
Real-world decision-making is the process of deciding what to judge and act on first amid diverse data and interests. In actual work, however, reviewing scattered information and grasping key issues and their impact requires significant time and effort, and judgment criteria can vary from person to person. By automatically deriving key issues and priorities and providing the briefings needed for execution, S2W reduces review time and supports faster, more consistent decision-making.
Issue Prioritization, Impact Assessment, Executive Briefing, Operational Reasoning, Actionable Intelligence
Optimizing Decisions
for Real-world Operations
Decision-Making Optimized for Real Operations
Connected Intelligence
- Collecting, normalizing, and connecting information from diverse systems and data sources based on sophisticated data pipelines and real-time data processing technology
- Connecting data, events, and entities into a single decision-making context and identifying relationships and patterns that are difficult to grasp with individual systems to provide the integrated perspective needed for decision-making
Priority-driven Decisions
- Comprehensively assessing impact, urgency, and business importance based on relationship-based analysis and domain-specific reasoning technology
- Deriving priority response targets and the order of execution even in complex situations, and proposing actionable response measures to improve the speed and accuracy of decision-making
Consistent Decision Framework
- Designing experts' judgment criteria and business rules into a structured decision-making model to perform consistent analysis and judgment for the same situation
- Using an AI agent that applies the same decision-making logic enterprise-wide to minimize decision variance based on the individual or their level of experience and provide a consistent judgment framework at the organizational level
Institutionalized Expertise
- Structuring the relationships among experts' judgment criteria, business rules, and domain concepts through an ontology to reflect them in the decision-making process
- Turning experience and know-how once dependent on individuals into reusable knowledge assets at the organizational level, and building a decision-making framework that can be continuously expanded and utilized
Role-based Intelligence Delivery
- Reflecting an organization's work systems and authority structures to provide each user with differentiated access to the data, analysis results, and decision-making information they need
- Reducing information overload and providing insights optimized for each role to support fast and effective decision-making
The Continuous Decision Intelligence Loop
Decisions That Improve With Every Cycle
01
Build a Connected Data Foundation
- Designing data pipelines to collect and integrate information from diverse systems and data sources, and performing data normalization and entity resolution
- Converting data with different formats and structures into a consistent data model, and building an integrated data layer usable for decision-making
02
Domain Knowledge Modeling
- Structuring the relationships among experts' judgment criteria, business processes, and domain concepts on an ontology basis, and modeling them in the form of a knowledge graph
- Defining the semantic relationships between data and knowledge through semantic modeling to build a foundation through which AI can understand business context and domain knowledge rather than mere data
03
Build Reasoning Intelligence
- Combining structured knowledge with operational data to build domain-specific AI and designing a contextual reasoning framework that understands situations and context
- Applying decision-making scoring models for relationship analysis and impact assessment to derive priorities and generate the insights needed for decision-making
04
Operationalize Decision Workflows
- Connecting data analysis, reasoning, and recommendation processes based on agent orchestration technology, and integrating them with actual work processes through workflow automation
- Applying a human-in-the-loop structure to operate AI's judgment alongside expert review, building a trustworthy decision-making framework
05
Operational Deployment
- Applying AI to work systems and operational processes to build real-world usage environments for analysis, response, search, and work support
06
Continuous Improvement
- Continuously improving AI's performance and accuracy by reflecting the data and user feedback accumulated during operation
Proven Through Real-world
Intelligence
Context-based decision-making AI
DRI, AI Agent for National Security Intelligence
In criminal investigation and security analysis, connecting scattered data such as the dark web, Telegram, and breach incident information and interpreting case context requires high expertise and significant resources. S2W built DRI (Deep Research Investigator), an AI agent specialized for the crime and security domains. Through a conversational interface, DRI explores data and analyzes the relationships between information and key circumstances to support the decision-making of investigators and analysts. Currently deployed in XARVIS, it supports the complex investigative process—from information exploration and relationship analysis to situational interpretation and the derivation of follow-up leads.
AI Agent for Manufacturing Decisions
On the manufacturing floor, quality, equipment, and productivity data are scattered across multiple systems, making consistent analysis and decision-making difficult. S2W structured manufacturing experts' knowledge and operational data through an ontology and built a decision support platform combined with domain-specific AI. Through AI-automated reports, it analyzes operational data and organizes key issues and insights to provide decision-making information based on consistent criteria. This turns judgment criteria once dependent on individual experience into organizational knowledge assets, and helps interpret information by the same standards even in complex manufacturing environments.
DRI, AI Agent for National Security Intelligence
In criminal investigation and security analysis, connecting scattered data such as the dark web, Telegram, and breach incident information and interpreting case context requires high expertise and significant resources. S2W built DRI (Deep Research Investigator), an AI agent specialized for the crime and security domains. Through a conversational interface, DRI explores data and analyzes the relationships between information and key circumstances to support the decision-making of investigators and analysts. Currently deployed in XARVIS, it supports the complex investigative process—from information exploration and relationship analysis to situational interpretation and the derivation of follow-up leads.
AI Agent for Manufacturing Decisions
On the manufacturing floor, quality, equipment, and productivity data are scattered across multiple systems, making consistent analysis and decision-making difficult. S2W structured manufacturing experts' knowledge and operational data through an ontology and built a decision support platform combined with domain-specific AI. Through AI-automated reports, it analyzes operational data and organizes key issues and insights to provide decision-making information based on consistent criteria. This turns judgment criteria once dependent on individual experience into organizational knowledge assets, and helps interpret information by the same standards even in complex manufacturing environments.
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