#Japan #AI Solutions #Tokyo #ContextFabric Hub #Iroribi

Introduction

In the era where Artificial Intelligence (AI) interacts through context, Iroribi Co., Ltd. has taken a significant step forward by launching the ContextFabric Hub for trial use. This innovative platform autonomously links data infrastructure with internal business knowledge using the power of generative AI, supporting sophisticated decision-making processes.

Bridging the Knowledge Gap

Traditionally, utilizing generative AI required specialized prompts to extract information from databases. However, ContextFabric Hub simplifies this process by generating metadata autonomously. By integrating metadata management tools and graph technologies, it achieves a seamless flow towards Retrieval-Augmented Generation (RAG), facilitating the fusion of numerical data and meaningful insights. Users can now pose abstract business challenges without needing to specify concrete table names, allowing AI to delve into internal data and derive well-founded insights.

The goal is clear: addressing the challenge that organizations face when they possess data but struggle to reach actionable answers.

Existing Challenges

As businesses increasingly recognize the value of data and AI, they encounter various barriers that inhibit realizing their potential. Common feedback highlighted during project discussions includes:

• - Prior generative AI solutions provided superficial answers, falling short in analyzing complex business challenges or generating unclear responses.
• - Difficulty in issuing precise commands regarding which tables to examine, leading reliance on engineers or analysts.
• - Even when seemingly plausible answers were offered, they lacked a firm basis in internal data, rendering them insufficient for strategic decision-making.

ContextFabric Hub aims to dismantle these barriers from the ground up.

Solution Overview

Before ContextFabric Hub

1. Expertise Barrier: Simply asking AI to investigate the causes of declining sales would require human guidance on specific tables and fields to reference.
2. Information Fragmentation: Numerical data and background information existed separately, necessitating manual searches for explanations behind numerical changes.
3. Lack of Reliability: AI often generated responses based on arbitrary data, leading to hallucinations that produced unreliable answers.

After ContextFabric Hub

1. Understanding Language: With an understanding of business terminology, AI can independently select appropriate datasets and enable advanced analysis without expert intervention.
2. Integrating Numbers and Text: The system automatically integrates structured numerical data, such as a 10% decrease in sales, with unstructured data from sources like incident reports during the same period.
3. High-Quality Responses: AI autonomously detects data quality, referencing only the most accurate and current information to provide high-quality answers.

Key Features of ContextFabric Hub

1. Semantic Analytics Engine: Automatically maps complex business terms and contexts into database structures, transforming vague instructions into precise analytical commands.
2. Graph-RAG: Connects information on what occurred and why through a graph structure, linking disparate data points and revealing deep insights.
3. MCP-Compatible Interface: Adheres to the MCP standards proposed by Anthropic, allowing major AIs like ChatGPT and Claude to access corporate contexts instantly as an external brain.
4. AI DataOps: AI assigns metadata to databases autonomously, maintaining data quality and ensuring a clean data environment that minimizes AI confusion.

Benefits of Implementation

Transforming Organizational Decision-Making

• - For Management and Sales Teams: Inquiries such as “What caused the decline in sales compared to last year?” can be addressed quickly, retrieving integrated analyses of product performance and field activity reports in seconds, greatly reducing preparation time for meetings.
• - For DX Promotion Teams: Safety in deploying AI across the organization based on solid references, such as internal regulations and technical documents.
• - For Information Systems Departments: By understanding data meanings independently, AI reduces the manual mapping and documentation work that's traditionally labor-intensive.

Future Prospects

Iroribi is committed to achieving autonomous data operations where AI continuously optimizes the data infrastructure. This initiative aims to democratize data analysis through generative AI. Initially, a trial introduction program will enhance decision-making speeds in practical applications, marking an exciting step forward in AI-driven solutions within organizations.

Company Overview

• - Company Name: Iroribi Co., Ltd.
• - Location: 2F Shinjuku Washington Hotel Building, 3-2-9 Nishi-Shinjuku, Shinjuku-ku, Tokyo 160-0023, Japan.
• - Established: February 1, 2021
• - CEO: Terumasa Shimoyama
• - Business Activities: Planning, consulting, and engineering in the use of digital technologies.
• - Website: https://iroribi.com/

Contact Information

For further inquiries regarding this matter, please contact:

• - Iroribi Co., Ltd.
• - Email: [email protected]