AI in Practice

We don't just advise on AI — we embed it in our own operations and keep it running

We don't merely talk about AI's potential. We have integrated it into our own business design, development processes, and site construction — and we operate it daily. The practical knowledge gained through continuous trial and iteration is applied directly to the support we provide our clients.

Key Highlights

Established an operational framework that combines multiple AI tools to comprehensively map and visualize our own workflows, assigning each tool where it works best
Proactively practice AI-driven, spec-first development — internalizing the design, implementation, and verification cycle
Built and operate live systems — including this corporate site — by our principal, accumulating reproducible, real-world knowledge
Only apply methods validated through internal use to client engagements, ensuring proposals are grounded in proven practice rather than theory

What We Practice

Multi-AI Task Mapping & Visualization

Rather than relying on a single AI tool, we combine multiple AI systems suited to each purpose to visualize our entire workflow. From process auditing to classifying routine and non-routine tasks, we continuously identify and update the boundaries within which AI operates effectively.

Right-Tool-for-the-Right-Task Design

We design which AI to deploy at which phase based on the nature of each task and the required output quality. By establishing internal standards for tool selection and operating rules, we achieve a sustainable AI practice that doesn't depend on any single individual.

Spec-Driven Development with AI

We practice a development flow in which AI leads implementation starting from a specification document. By integrating AI into requirements definition, design, coding, and review, we enable fast, high-quality in-house development even with a small team.

In-House Implementation by Our Principal

Our principal personally builds and operates live systems, including this corporate site. By insisting on in-house development rather than outsourcing, we continuously verify the effectiveness, limitations, and room for improvement of AI as direct practitioners.