NBS Launches 'DataPark,' Evolving Corporate Database into 'Corporate Data Ontology'

Net Business Support Co., Ltd. (NBS), a group company of Headwaters Inc. (Headquarters: Shibuya-ku, Tokyo; President and CEO: Masao Ikegami; hereinafter "NBS"), will officially launch in mid-August 2026 a corporate data ontology called "DataPark," which adds meaning and relationships to corporate information to support sales decision-making and AI agent utilization.

URL: https://datapark.jp/

Background: Agentic AI Requires 'Meaning and Relationships' in Corporate Data

AI adoption in enterprises is evolving from merely searching and summarizing corporate information for human users to utilizing "Agentic AI"—autonomous systems that independently perform tasks such as information gathering, target company identification, contact point determination, and proposing or executing next actions.

Historically, sales professionals have collected not only basic company data but also news, personnel changes, organizational shifts, recruitment activities, and new business developments, mentally connecting these pieces to determine "which company to approach and why now" and "which department or individual to target."

For Agentic AI to perform these human tasks, it must not only access the same information but also understand what each piece of data means and how it relates to companies, departments, locations, individuals, news, and corporate activities. This is where "ontology" comes into focus.

Ontology systematically defines entities such as companies, departments, individuals, and activities, along with their attributes, relationships, and rules. It establishes a shared understanding between humans and AI about "what exists, what it means, and how things are connected."

Major technology firms like Microsoft and Palantir are now positioning ontology at the core of their enterprise AI and data platforms. Ontology is gaining attention as a contextual foundation that provides AI agents with operational meaning and relationships, enabling informed decisions and actions. In the Agentic AI era, corporate data must not only be accurate, fresh, and granular but also externalize the mental processes of meaning-making and relationship-building that humans naturally perform.

What is DataPark? A 'Corporate Data Ontology' Connecting Companies, Departments, Individuals, and Activities

DataPark is built on "Beegle Data," the next-generation corporate database that NBS has developed and refined for over a decade. Centered on legal entity numbers for data matching, DataPark links and continuously updates information on companies, departments, locations, group companies, individuals, corporate activities, news, and marketing tags.

It includes approximately 800,000 companies with official websites and over 30 million corporate data points. Rather than presenting flat, one-row-per-company data, DataPark enables users to trace "how a company has changed and who it is connected to." The product embodies both "Data as Product"—managing accurate, up-to-date corporate data as a continuously improved product—and "Ontology as Product," which structures the meaning and relationships between data points for practical use.

*"Data as Product" refers to treating data as a continuously managed and improved product. "Ontology as Product" refers to structuring the meaning and relationships between data for usability.

Key Features of DataPark

1. Delivery of Over 800,000 Companies and 30 Million Data Points as Meaningful, Interconnected Data

Using official company website information, DataPark matches data via legal entity numbers and links departments, locations, group companies, personnel changes, corporate news, and activity tags to each company. It provides not just basic company information but also surrounding changes and relationships as corporate context.

2. Integration of Three Cloud Services and Existing Customer Base into 'DataPark'

DataPark is not a standalone new service but integrates NBS's three previously separate cloud services and their existing customer base. By consolidating functions—such as target list creation, CRM/SFA data management, and detailed company information checks—into a single platform, existing customers can more easily use multiple features across purposes.

3. Transforming CRM/SFA Customer Data into Decision Context for Agentic AI

DataPark not only cleans up inconsistencies, duplicates, and outdated information in CRM/SFA systems but also continuously links external information—such as organizational changes, personnel moves, recruitment, new ventures, news, and corporate activities—to prospects and existing customers. This integrates historical customer touchpoints in CRM/SFA with real-time external corporate changes, based on relationships between companies, departments, locations, and individuals.

Beyond merely correcting CRM/SFA data, DataPark enriches it into corporate context that enables Agentic AI to assess customer relationships and current changes, supporting decisions on re-engagement, cross-selling, and expansion into group companies.

4. AI Support for Search Criteria Creation and Target Company Identification

When sales personnel input their objectives, AI assists in creating complex search conditions and queries. It also analyzes web-based corporate activities and changes to help identify target companies and verify the rationale behind selections.

5. Planned Provision as External Context for AI Agents

The underlying Beegle Data in DataPark structures information on companies, departments, locations, individuals, news, and corporate activities as meaningful corporate context.

Going forward, NBS plans to develop formats enabling AI agents to reference Beegle Data as external context for understanding external companies. While internal systems like CRM/SFA and deal histories serve as "internal context," DataPark aims to function as the "external context" for understanding clients and prospects.

6. Usage-Based Pricing Model to Support Human and AI Agent Utilization

Traditional SaaS models typically charge per user seat. However, in the Agentic AI era, data usage scales even without an increase in human users, as AI agents continuously perform tasks like data searches, target identification, context acquisition, and CRM/SFA data enrichment.

DataPark adopts a usage-based pricing model, combining a base fee with charges based on feature usage and data volume. This supports not only human users but also the growing use of corporate data and context by AI agents, aligning DataPark's value with NBS's revenue opportunities.