Opening of 'AI Data Factory,' a Corporate Data Generation Hub Supporting AI Agent Decision-Making
Key facts
- Opening of 'AI Data Factory,' a Corporate Data Generation Hub Supporting AI Agent Decision-Making
- Net Business Support has launched the 'AI Data Factory,' a dedicated facility for generating high-quality corporate data optimized for AI agents. By enhancing the 'Beegle Data' database with four key criteria—accuracy, freshness, transparency, and uniqueness—the company aims to accelerate enterprise digital transformation and AI adoption.
- Source: PR Times
- Date: June 18, 2026
Direct answer
Net Business Support has launched the 'AI Data Factory,' a dedicated facility for generating high-quality corporate data optimized for AI agents. By enhancing the 'Beegle Data' database with four key criteria—accuracy, freshness, transparency, and uniqueness—the company aims to accelerate enterprise digital transformation and AI adoption.
- Citation
- Opening of 'AI Data Factory,' a Corporate Data Generation Hub Supporting AI Agent Decision-Making (June 18, 2026), PR Times
- Source
- PR Times
- Date
- June 18, 2026
Net Business Support has launched the 'AI Data Factory,' a dedicated facility for generating high-quality corporate data optimized for AI agents. By enhancing the 'Beegle Data' database with four key criteria—accuracy, freshness, transparency, and uniqueness—the company aims to accelerate enterprise digital transformation and AI adoption.
📋 Article Processing Timeline
- 📰 Published: June 18, 2026 at 16:30
- 🔍 Collected: June 18, 2026 at 07:47
- 🤖 AI Analyzed: June 19, 2026 at 07:20 (23h 32m after Collected)
How the Role of Corporate Data is Changing with AI Agent Utilization
Technological advancements have evolved the role of corporate data from the initial stage of 'information understanding and verification' to the second stage of 'target extraction,' and now, in an era where AI agent utilization is commonplace, to the third stage: 'the foundation for AI-driven strategic decision-making.'
In the past, when humans interpreted corporate data, minor inaccuracies or outdated information could be compensated for through experience. However, in today's environment where AI agents autonomously compare, judge, and formulate hypotheses, data deficiencies directly lead to strategic errors. We believe that corporate data in the age of AI agent utilization must meet four advanced criteria beyond previous standards.
Four Requirements for Corporate Data in AI Agent Utilization
1. Accuracy: A State Where Neither AI Nor Humans Are Confused
It is not enough for data to be factually correct; it must also be complete and well-structured as a basis for decision-making. This means data is consolidated without duplication or inconsistent formatting, using corporate registration numbers as keys, and fully encompasses parent-subsidiary relationships, group affiliations, and internal organizational details such as departments and locations.
2. Freshness: Maintaining High Decision Accuracy Through Timeliness
Real-time capture of ongoing changes within each company—such as new service launches, organizational restructuring, or hiring initiatives—is essential. The data must remain up-to-date at all times to enable AI to make accurate predictions and judgments without relying on outdated information.
3. Transparency: Clearly Explaining the Basis for Decisions
The origin of the data—where it was sourced, when it was collected, how it was processed—must be fully transparent. Information such as data sources, timestamps, and data processing rules must be clearly documented. This provides confidence in AI-generated conclusions by enabling users to verify the rationale behind decisions.
4. Uniqueness: Granularity That Enables Practical Decision-Making
The data must go beyond general web-based information that is easily accessible to anyone, delivering insights at an operational level. High-granularity data—such as department-level decision-making authority, factory operations, and intercompany group relationships—enables AI to concretely determine 'where, what, and when to propose.'
To build the 'Beegle Data' database optimized for the AI era—enhanced with these four criteria—we are launching a new data generation hub: the 'AI Data Factory.'
At this facility, we are applying the latest AI technologies directly to the Beegle Data generation process, focusing on creating data that AI agents prefer—data characterized by accuracy, freshness, and transparency.
Currently staffed by 10 personnel, we plan to strengthen our team in the future. We will also actively pursue collaborations with AI agent vendors and companies developing proprietary corporate data.
Through the AI Data Factory, NBS will strongly support enterprise digital transformation and AI utilization from the data side, accelerating more accurate decision-making and value creation.
■ Net Business Support Co., Ltd. Company Overview
A venture company developing next-generation corporate data, specializing in creating targeting lists for sales and marketing teams, as well as data consolidation (name matching) and cleansing.
Name: Net Business Support Co., Ltd.
Established: February 26, 2014
Head Office: 1-12-2 Shibuya, Shibuya-ku, Tokyo
Representative: Masao Ikegami, President and CEO
Business Activities: Provision of data and cloud services based on 'Beegle Data,' business consulting, and system development and operation services
Website: https://www.net-bizs.jp/
[Service Inquiries]
Contact: Aida, Ouchi
Email: sales@net-bizs.jp
TEL: 03-6272-5315
Technological advancements have evolved the role of corporate data from the initial stage of 'information understanding and verification' to the second stage of 'target extraction,' and now, in an era where AI agent utilization is commonplace, to the third stage: 'the foundation for AI-driven strategic decision-making.'
In the past, when humans interpreted corporate data, minor inaccuracies or outdated information could be compensated for through experience. However, in today's environment where AI agents autonomously compare, judge, and formulate hypotheses, data deficiencies directly lead to strategic errors. We believe that corporate data in the age of AI agent utilization must meet four advanced criteria beyond previous standards.
Four Requirements for Corporate Data in AI Agent Utilization
1. Accuracy: A State Where Neither AI Nor Humans Are Confused
It is not enough for data to be factually correct; it must also be complete and well-structured as a basis for decision-making. This means data is consolidated without duplication or inconsistent formatting, using corporate registration numbers as keys, and fully encompasses parent-subsidiary relationships, group affiliations, and internal organizational details such as departments and locations.
2. Freshness: Maintaining High Decision Accuracy Through Timeliness
Real-time capture of ongoing changes within each company—such as new service launches, organizational restructuring, or hiring initiatives—is essential. The data must remain up-to-date at all times to enable AI to make accurate predictions and judgments without relying on outdated information.
3. Transparency: Clearly Explaining the Basis for Decisions
The origin of the data—where it was sourced, when it was collected, how it was processed—must be fully transparent. Information such as data sources, timestamps, and data processing rules must be clearly documented. This provides confidence in AI-generated conclusions by enabling users to verify the rationale behind decisions.
4. Uniqueness: Granularity That Enables Practical Decision-Making
The data must go beyond general web-based information that is easily accessible to anyone, delivering insights at an operational level. High-granularity data—such as department-level decision-making authority, factory operations, and intercompany group relationships—enables AI to concretely determine 'where, what, and when to propose.'
To build the 'Beegle Data' database optimized for the AI era—enhanced with these four criteria—we are launching a new data generation hub: the 'AI Data Factory.'
At this facility, we are applying the latest AI technologies directly to the Beegle Data generation process, focusing on creating data that AI agents prefer—data characterized by accuracy, freshness, and transparency.
Currently staffed by 10 personnel, we plan to strengthen our team in the future. We will also actively pursue collaborations with AI agent vendors and companies developing proprietary corporate data.
Through the AI Data Factory, NBS will strongly support enterprise digital transformation and AI utilization from the data side, accelerating more accurate decision-making and value creation.
■ Net Business Support Co., Ltd. Company Overview
A venture company developing next-generation corporate data, specializing in creating targeting lists for sales and marketing teams, as well as data consolidation (name matching) and cleansing.
Name: Net Business Support Co., Ltd.
Established: February 26, 2014
Head Office: 1-12-2 Shibuya, Shibuya-ku, Tokyo
Representative: Masao Ikegami, President and CEO
Business Activities: Provision of data and cloud services based on 'Beegle Data,' business consulting, and system development and operation services
Website: https://www.net-bizs.jp/
[Service Inquiries]
Contact: Aida, Ouchi
Email: sales@net-bizs.jp
TEL: 03-6272-5315
FAQ
What is the AI Data Factory?
A specialized facility generating high-quality corporate data optimized for AI agents, meeting criteria of accuracy, freshness, transparency, and uniqueness.
What are the features of Beegle Data?
Corporate data meeting four criteria: accuracy, freshness, transparency, and uniqueness, providing high-granularity insights for AI decision-making.
What are the four requirements for data in the AI agent era?
Accuracy, freshness, transparency, and uniqueness—fulfilling these enhances AI judgment precision.
What services does Net Business Support offer?
Data matching and cleansing, targeting list creation, Beegle Data provision, consulting, and system development services.
What are the future plans for the AI Data Factory?
Strengthening the team and actively collaborating with AI vendors and proprietary data developers.