Data intelligence firm S2W is enhancing key features of its ontology-based decision-making operating system, 'SAIP,' and expanding its non-security business.
S2W announced on the 30th that it has upgraded SAIP's functionality to structure and integrate various forms of data scattered both inside and outside the company, while also reflecting industry-specific characteristics as well as internal corporate business rules and procedures.
This enhancement was driven by S2W's accumulated expertise in AI safety within the security and defense sectors. The core lies in systematically combining industry-specific context, situational information, and domain expert knowledge based on ontology and knowledge graphs.
The plan is to resolve data silo issues, a major pain point in the AI transformation process, as well as the difficulty of utilizing data in different formats together. The focus is on enabling AI to be applied in actual business operations to support actionable decision-making.
Barriers to adoption have also been lowered. SAIP is designed to be utilized without requiring major changes to existing systems or separate reconfiguration of data. It can directly link various data and documents used by companies for their work, including Enterprise Resource Planning (ERP) and data warehouses, as well as Excel and PowerPoint, without separate format conversion.
When source data is modified, the corresponding content is automatically reflected in the ontology. This reduces the burden of data reorganization required to maintain currency.
The explainability of analysis results has also been strengthened. S2W implemented explainable AI technology that allows users to trace back the path of result derivation to verify grounds and influencing factors. This enables users to directly review the validity of AI analysis results before incorporating them into business judgments.
S2W explained that this verification structure serves as a foundation for applying AI analysis to practical work even in tasks requiring high reliability and accuracy. The company also dispatches on-site deployment engineers to understand customers' actual work environments and requirements. It supports the construction and operation of SAIP-based systems tailored to each company's specific challenges.
SAIP is being used to automate repetitive regular and irregular tasks in real-world settings and to support decision-making by synthesizing various data. The core lies in analyzing by connecting internal corporate data, on-site business knowledge, past cases, and external data, going beyond the level of simply querying or summarizing specific data.
For example, when a specific change or event occurs within a company, it analyzes signals from past cases and the external market and industrial environment together to identify which factors had an impact, providing grounds for judgment as well. The focus is on SAIP analyzing and organizing tasks that people previously performed by individually checking multiple systems and reports, thereby helping on-site personnel make quick, evidence-based decisions.
S2W has demonstrated the utility of SAIP in industrial settings by supplying it to domestic companies such as Hyundai Steel and Lotte Members. With this product enhancement as a starting point, the company plans to expand its market beyond manufacturing, distribution, and finance to mission-critical industries such as defense, energy, and telecommunications.
S2W is scheduled to introduce the key features of the enhanced SAIP and industry-specific use cases at the 'Microsoft Industry Summit' being held at COEX in Gangnam-gu, Seoul, on the same day.
Park Geun-tae, Chief Technology Officer of S2W, stated, "For a company's AI transformation to lead to substantive innovation beyond simple automation, AI must understand the unique context and work methods of each company and industry, and contribute to rational decision-making and subsequent execution." He added, "As this enhancement has strengthened SAIP's utility and reliability, we will expand its application scope to various industries that require complex, high-dimensional decision-making."
in TECH
