I'm James Hayashi — a Lead AI Engineer with a decade architecting multi-agent systems, advanced RAG, and cloud-native AI infrastructure. I build systems that respect the "Ma" of information — where precision logic meets the organic flow of human intuition. 林 ジェームスと申します。 マルチエージェント、 先端RAG、 クラウドネイティブAI基盤の設計を十年。 情報の「間」を尊重し、 精密な論理と人間の直感が交わる場所を、丹精に築きます。
I progressed from foundational software engineering at Cybozu Global to leading AI strategy, R&D, and cross-functional teams across PreScouter, A2 Healthcare, and Septeni Holdings. Today, I architect autonomous multi-agent platforms that reduce manual enterprise work by 75%. サイボウズ・グローバルでの基盤開発から始まり、 PreScouter・ A2ヘルスケア・ セプテーニ・ホールディングスにてAI戦略、R&D、 機能横断チームを率いてきました。現在は、企業の手作業を75％削減する 自律マルチエージェント基盤を設計しています。
In the space where ink meets paper, there is no room for hesitation. I apply this same Zen-like clarity to high-performance AI — every line of code is a brush stroke, deliberate and efficient. My methodology blends the rigor of Kintsugi (strength through refinement) with the cold precision of silicon engineering. 墨が紙と出会う瞬間に、迷いの余地はありません。私は同じ禅の明晰さを 高性能AIに注ぎます——一行のコードは、一筆の筆跡。意図を持ち、効率を備える。 私の方法論は、金継ぎ（錬磨による強さ）の厳しさと、 シリコン工学の冷徹な精度を、静かに結びます。
"Precision is not the absence of art — it is the most refined form of it." 「精度とは、芸術の不在ではない——その最も練り上げられた形である。」
Generative AI systems at production scale — from LLM applications to multi-agent orchestration, advanced RAG, and knowledge graphs tuned for domain accuracy.
Tri-cloud native — shipping distributed AI on AWS, Azure, and GCP. Real-time ETL, vector databases, and serverless architectures deployed in regulated environments (HIPAA).
"The most sophisticated intelligence is not just powerful, but balanced."
Enterprise Autonomous Multi-Agent Orchestration Platform
Architected a distributed multi-agent system on LangGraph + CrewAI. Engineered GraphRAG with cross-encoder re-ranking over 50 TB of unstructured data.
AI-Powered Marketing Insights Engine
Led multi-agent system on Azure with LangGraph; automated 80% of analytical workflows and set the technical roadmap for the AI engineering team.
Autonomous Clinical Workflow & Patient Engagement Platform
End-to-end HIPAA-compliant multi-agent LLM system with a safety co-pilot reducing hallucinations on critical medical queries to under 0.1%.
Biomedical Knowledge Graph & Insights Engine
Serverless AWS Kinesis pipeline ingesting 100 K+ scientific documents, powering a biomedical knowledge graph of 5 M+ entities in Amazon Neptune.
Advertising Data · Distributed Systems Foundation
The foundation. Core advertising data pipelines in Python, client-facing dashboards in JavaScript, and an anomaly-detection prototype that identified system incidents 30% faster than manual methods.
Seventeen syllables — 5 · 7 · 5. A haiku holds the entire shape of attention. For an engineer, it is the cleanest function ever written: input the moment, output the truth. 十七音の俳句には、注意そのものの形が宿る。エンジニアにとって、 それは最も清らかな関数である——瞬間を入力し、真実を出力する。
A distributed swarm of specialised agents — Planner, Critic, Executor — that automate complex enterprise workflows end-to-end with stateful long-term memory.
A reasoning agent that auto-generates weekly client performance reviews, cutting report creation by 90% while preserving analytical rigor.
5M+ entities and 20M+ relationships in Amazon Neptune, fed by a serverless Kinesis pipeline processing 100K+ scientific documents.
A supervisor agent watching the primary medical LLM — reducing hallucinations on critical queries from industry-standard >3% down to under 0.1%.
A prototype at Cybozu that identified system incidents 30% faster than manual methods. The seed of a decade in AI.
"We reject the black-box approach. Every line of code is a brush stroke — deliberate, impactful, beautiful in its efficiency."
Whether for collaborative research, architecture review, or a new AI engagement — I welcome the transmission of your intent. 共同研究、設計レビュー、新たなAI案件——どのような形であれ、 貴方の意図を、静かに受け取ります。