Short Bio
Ken Imoto is a WebRTC & Voice AI engineer and founder of Propel-Lab. He is the author of the LLMO Framework and has published 11 books on AI development, context engineering, and AI search optimization.
Medium Bio
Ken Imoto is a WebRTC & Voice AI engineer building AI-native organizations at Propel-Lab. He is the creator of the LLMO (Large Language Model Optimization) Framework, which helps businesses optimize their content visibility in AI-powered search engines. Ken has published 11 books including "Practical Claude Code," "Harness Engineering," and "LLMO Practical Guide" across Kindle and Zenn. His research spans AI text analysis, vision-language model bias, and voice AI latency optimization, with 3 papers published on Zenodo. His work on Qiita has attracted over 67,000 pageviews.
Full Bio
Ken Imoto is a WebRTC & Voice AI engineer and founder of Propel-Lab, where he builds AI-native organizations powered by LLMs, automation, and distributed agents.
He is the creator of the LLMO (Large Language Model Optimization) Framework, a systematic approach to optimizing content visibility in AI-powered search engines like ChatGPT and Perplexity. The framework is documented in his book "Why ChatGPT Ignores Your Website: The LLMO Practical Guide" and the companion site llmoframework.com.
Ken has published 11 books spanning AI development, context engineering, MCP security, and AI search optimization. Notable titles include "Practical Claude Code — Context Engineering for Modern Development," "Harness Engineering — From Using AI to Controlling AI," and "MCP Security in Practice."
His research includes three peer-reviewed papers on Zenodo: "Excess Vocabulary in Japanese AI-Generated Text" (analyzing 651 statistically significant excess words across 7 LLMs), "AI Text Slop" (measuring stylistic convergence in Japanese technical writing), and "AI Blue" (quantifying color recognition bias in vision-language models). These studies are the first quantitative analyses linking specific LLM behaviors to the broader "AI Slop" phenomenon.
Ken is currently researching voice AI latency optimization (the 300ms barrier), context engineering for multi-agent AI systems, and generative agent simulation.
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