ProductSAIP

SAIP

Ontology-Based Decision Operating System

From Domain Knowledge
to Trusted Decisions

SAIP is an ontology-based decision OS.

It connects an organization's scattered structured and unstructured data, business rules, and expert knowledge into a single knowledge system, building a decision-making structure aligned with business objectives. Drawing on data relationships and business context, it explains root causes and contributing factors, and goes further to propose priorities, response options, and action items—supporting decisions you can trust.

Why SAIP

Solving
Critical Decision Challenges

Clarity in Complex Decisions

Even when data is digitized, if it isn't connected through business context and a knowledge system, the results of analysis rarely translate into consistent decision-making and execution.

SAIP connects data, domain knowledge, and business rules into a single structure, turning an organization's own judgment criteria into operational intelligence.

Expert Knowledge Assetization

Turning organizational experience into a lasting asset

Structures work criteria and know-how—once confined to individual experience—into an ontology, turning them into shared knowledge the entire organization can reuse

Before

Experts' core know-how scattered across individual experience and documents

After

Practitioners' know-how converted into an ontology and organized as shared knowledge across the organization

AI-ready Data Foundation

Building a data foundation for AI adoption

Connects structured and unstructured data according to business objectives and semantic relationships, building a data foundation that AI can use for search, analysis, and reasoning

Before

Data scattered without context, never leading to real decision-making

After

A data foundation AI can use in line with the business context

Domain-optimized Intelligence

Intelligence Tuned to Your Domain and Your Context

Reflects a company's own terminology, rules, causal relationships, and business processes to reduce repetitive verification and interpretation work, and to support domain-specific analysis.

Before

Repeatedly reconstructing judgment criteria based on individual experience

After

A domain-specific analytical system that reflects the company's own context and causal relationships

Trusted Decision Intelligence

Trustworthy decision-making that explains both the rationale and the impact

Drawing on data relationships and business rules, it presents both the grounds for a judgment and its contributing factors together, so results can be verified and actionable options identified.

Before

The grounds and contributing factors behind AI analysis had to be reviewed separately, limiting immediate use

After

Decision support that presents the grounds for a judgment, its contributing factors, and risks together

Core Capabilities

Built for Better Decisions

Main Functionalities of SAIP

Unstructured
Data Intelligence

Extracts key concepts and relationships from unstructured data such as documents, reports, regulations, and terms of service
Connects qualitative and quantitative data into a single knowledge system
Builds ontology-based AI-Ready Data

Semantic Knowledge
Understanding

Models the semantic relationships among business terminology, data, and domain knowledge
Supports search, querying, and analysis centered on business context rather than simple keywords
Provides context that lets LLMs draw on an organization's own context and relationships

Explainable
Decision Intelligence

Analyzes the factors behind key metrics such as revenue changes, cost increases, and KPI deviations
Explains the causes and effects of outcomes based on the relationships and causality between data
Provides grounds for judgment not just about what happened, but about why it happened

Deterministic
Workflow Engine

Implements decision logic that combines ontology, business rules, and code-based workflows
Supports an execution framework that produces consistent results under the same conditions
Complements rule-based verification to offset the uncertainty that can arise when using an LLM alone

Executive Reporting
& Decision Briefing

Summarizes complex data and analytical results into a form that executives and practitioners can easily understand
Presents the causes of performance changes, key risks, and priority action items in a structured way
Provides insights that go beyond reporting to be used in decision-making and execution

From Insight to Action

The Decision Intelligence Pipeline

A decision operations flow that runs from defining objectives to AI agent-based execution support

Goal Definition

Defines the key business questions to be addressed and the target metrics (KPIs)
Sets the scope of decisions, evaluation criteria, and priorities to establish the direction of analysis

Data Integration

Collects, cleanses, and integrates data from multiple sources, including structured and unstructured data
Identifies the key data, entities, and relationship candidates needed for decision-making

Ontology Construction

Models core concepts, entities, relationships, business terminology, and business rules
Connects expert knowledge and data to build a reusable organizational knowledge system

Knowledge Orchestration

Reasons by combining business context, expert judgment criteria, causal relationships, and business rules
Analyzes scenarios by condition and the impact of each alternative to derive the grounds for judgment

Decision Enablement

Proposes priorities, recommended response measures, risk-verification scenarios, and action items
Connects to AI agents and business workflows to put the results to use in operations and decision-making

Use Cases

Retail & Franchise

An integrated decision-making framework that connects store performance with inventory operations

Problem

Sales, inventory, promotion, and customer data are scattered across branches and systems, making it hard to read the causes of performance gaps between stores and of inventory operations all at once.

Key Task

Analyzing performance gaps across branches, identifying the causes of overstock and stockouts, analyzing promotion effectiveness, and deriving operational priorities for each store

Solution

Connects sales, inventory, promotion, and customer data on an ontology basis to analyze the key factors affecting store performance

Explains the causes and effects behind sales changes, and presents priorities for inventory operations, promotions, and action items at each branch

Finance & Risk

Identifying complex financial risks early and prioritizing responses

Problem

As transaction, customer, market, and regulatory data accumulate in separate management systems, potential risks and anomalies remain as isolated signals.

Key Task

Detecting anomalous transactions, analyzing portfolio risk, addressing regulatory and compliance requirements, and setting response priorities based on risk level

Solution

Connects transaction, customer, market, and regulatory data on an ontology basis to analyze the factors behind risk and their impact on relationships

Presents risk levels, response priorities, and risk-verification scenarios based on verifiable figures and evidence, and automatically briefs the key points

Manufacturing & Quality

A quality decision-making framework that catches anomalies before they lead to production disruptions

Problem

Because production, equipment, and quality data are separated, it is hard to narrow down the causes and scope of impact before anomalies become production disruptions.

Key Task

Tracing the causes of defects, detecting equipment anomalies early, analyzing effects across processes, and deriving priorities for preventive maintenance and quality improvement

Solution

Connects production, equipment, and quality data on an ontology basis to analyze the key factors affecting quality degradation and equipment anomalies

Explains the early signs of defects and the impact of relationships across processes, and presents execution priorities for equipment inspection, process improvement, and preventive maintenance