At a major information equipment manufacturer, we proposed the product concept for document management software and were responsible for foundational technology R&D. Commercialization was subsequently led by the business division, and the product grew into a large-scale commercial software solution that remained in use for many years.

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This experience demonstrated that developing strong technology is only part of the challenge. A successful product also requires a concept that users can understand, integration into existing workflows, and a form that can ultimately be brought to market.

For an overseas technology venture, we conducted on-site due diligence covering interviews with all engineers, manufacturing and production processes, component and product inventory, development structure, and management practices.

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Rather than simply verifying technical performance, we assessed the capabilities of the technical team, manufacturing scalability, inventory management, and management discipline to determine whether the business could succeed as an investment or strategic partnership opportunity.

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Based on the technical and business-model insights gained through this assessment, we also proposed a product concept for an enterprise IT service. This included hardware design using an overseas manufacturer, as well as low-noise and low-power design considerations.

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The engagement extended from technical due diligence to product planning, and the resulting service went on to become a long-running business.

In connection with a partnership with an overseas cloud service provider, we were involved in technology and product assessment, planning the service for the Japanese market, establishing customer support operations, and launching online sales.

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We encountered a gap between the product area originally envisioned and the areas in which the partner company actually had a competitive advantage. This led to challenges involving product quality, persistent defects, and development backlogs.

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Through the experience of adapting and operating an overseas service for the Japanese market, we also learned the importance of maintaining a single, shared cloud platform.

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In addition, we experienced firsthand that SaaS is not simply a product that sells once deployed. It is an “explanation-driven product” that requires sales professionals who can clearly communicate its value, together with ongoing customer support.

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We built domestic customer support and online sales operations from the ground up and also worked on expansion across the Asia-Pacific region.

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This experience forms part of the foundation for our approach to cloud-service technical due diligence, where we assess not only functionality and architecture, but also sales models, operating costs, and support structures.

At an overseas R&D facility operated by a major corporate group, we conducted interviews with local engineers and evaluated the organization.

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We identified a gap between the research-oriented focus of the organization and the product development capabilities required by the business division, and reported the structural factors preventing research results from being translated into commercial products.

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At another overseas product development center, we evaluated software developers, hardware developers, and a small-scale manufacturing facility as an integrated operation.

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While we confirmed strong implementation capabilities and broad technical knowledge, we also identified areas requiring separate assessment, including understanding of fundamental computer science principles and long-term design thinking.

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Through these experiences, we have learned to evaluate not only titles and deliverables, but also how engineers think and how much of the design and implementation they can carry out independently, based on interviews and direct observation of their work.

At a domestic manufacturing operation, we interviewed approximately 20 engineers and production staff, including foreign workers.

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We found that communication was heavily concentrated around specific interpreters and coordinators, while technical and operational issues were not being communicated directly between management and frontline workers.

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Even when the necessary technology and equipment are in place, productivity and quality cannot be sustained if there are problems with language, organizational structure, authority, or information flow.

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This experience has reinforced our view that technical due diligence in manufacturing should examine not only equipment and production processes, but also workforce structure and communication pathways.

At a major corporate research institute, we hosted engineering and science talent from Europe and Asia and provided guidance through R&D projects.

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Rather than treating participants simply as trainees, we involved them in actual technical assessment and R&D activities, evaluating their expertise, analytical capabilities, implementation skills, and future potential.

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Some participants subsequently earned PhDs overseas, went on to become university executives, or co-founded globally used IT services.

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This experience contributes to our approach to evaluating key personnel in startups and R&D organizations, identifying high-potential technical talent, and assessing dependency on individuals and talent-retention risks.

We led mentoring activities supporting the commercialization of university-originated research through a public startup support program.

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Our assessment went beyond the novelty of the underlying technology to examine potential customers, competing technologies, intellectual property, development structures, funding plans, and the path to commercialization.

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Technical excellence and business viability are not the same thing.

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We have worked with researchers and business leaders to identify what is missing to move research results into the market and to design a practical path toward commercialization.

Today, we evaluate overseas AI drug-discovery companies and similar businesses by taking an integrated view of their technical information, machine-learning models, datasets, software infrastructure, computing environments, and R&D teams.

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We assess not only AI model performance, but also the quality and long-term availability of training data, reproducibility, dependencies between models, reliance on key individuals, and the feasibility of applying the technology to drug-discovery research.

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Ultimately, our role is not simply to determine whether one technology is technically superior to another.

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We organize the implications for management decisions—including whether a partnership, investment, or acquisition is the appropriate approach, and what risks may emerge after the engagement—and present our findings in a form that can support decision-making.

We have been involved in the planning of educational services, curriculum design, institutional applications, and accreditation for universities, graduate schools, and new academic departments.

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For long-term initiatives involving multiple educational institutions, we have evaluated education systems from technological, business, and organizational perspectives.

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In online education, service quality depends on more than the functionality of a learning management system. Student access patterns, identification of inactive participants, support structures, faculty and staff understanding of the educational content, and the ability to recruit and market the program nationwide can all affect the quality and viability of the service.

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Higher education is also a service that must communicate its value in order to be chosen.

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Through these experiences, we have evaluated not only the technology itself, but also the operational, human-resource, sales, and marketing challenges that cannot be solved simply by introducing a system.