Takajo AI LabTAKAJO AI Lab

AI adoption, review design, hands-on training
I help with the decisions, not just the code

For teams that want to go beyond using AI -- to design quality, operations, review, and training into their AI systems.

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What I can help with

AI Adoption Advisory

  • Technical and operational design for AI integration
  • LLM output quality assessment and reliability design
  • Pre-implementation scoping and structuring key decisions

Review System Design

  • Designing AI-assisted review workflows
  • Customization based on CriticChain
  • Building quality audit processes

Training & Curriculum Design

  • Corporate training program design (including pilot programs)
  • Integrated design of material, assignments, and review
  • Enterprise use of Takajo AI Lab

The kind of problems I work on

  • You want to adopt AI, but quality and accountability boundaries are unclear
  • You want to use LLM output in production, but don't know how to ensure reliability
  • Your training and education efforts depend on individuals, and you want to systematize them
  • You're shaping a new AI product and need help structuring the approach

How I work

  • I support decisions and architecture -- not just implementation
  • I work across technology, operations, and education to structure the real questions
  • From small PoCs to production -- I look for where things will break first
  • I focus on how AI fails and where the operational risks are, not just what it can do
  • Native Japanese speaker. Comfortable delivering documentation, deliverables, and meetings in English

Background

Building software since 1998. Embedded systems, research and patents, startups, web infrastructure, and education -- leading to where I am now.

  • Created prompt-as-code, a syntax standard for prompts (RFC-style)
  • Built CriticChain, a quality audit engine based on that standard (AGPL-3.0)
  • Designed and run Takajo AI Lab, a hands-on AI training program → Details

I defined a syntax standard for prompts, built an audit engine that enforces it, and designed a training program that runs on it.

These aren't separate projects outsourced to different teams. They were designed, built, and operated by one person, end to end.

That's why I can talk about where things actually break in practice -- not just where they should work in theory.

AI review design, quality standards, the division of labor between humans and AI. These are areas where the industry hasn't settled on answers yet. Having walked this path ahead, I can point out where the pitfalls are.

Ways to work together

  • One-off consultation
  • Monthly advisory
  • Architecture review
  • Corporate pilot / training design

Let's talk

Get in touch Connect on LinkedIn