I turn complex product and operational knowledge into clear, governed, reusable documentation for people and AI systems.
20+ years across enterprise software, SaaS, healthcare, life sciences, financial services, online help, knowledge bases, release documentation, eLearning, and documentation operations — now extended through Docs-as-Code and AI-assisted documentation workflows.
Each case study focuses on the problem, my ownership, the documentation system or deliverables, and the value created.
Eight browser-based applications for document generation, review, migration, XML validation, FAQ generation, deterministic prompting, and RAG-readiness auditing.
Open case study →
A governed Markdown-to-publishing workflow combining GitHub, MkDocs, Vale, AI-assisted review, human approval, and HTML/PDF/DOCX outputs.
Open case study →
Centralized ownership, naming, version control, approvals, review cycles, dashboards, and HIPAA-aligned content controls across healthcare IT.
Open case study →
End-to-end knowledge-base ownership across Live Chat, Chatbot, Ticketing & Messaging, and Knowledge Base, including Amy Chatbot online help.
Open case study →
Modernized a legacy suite, restructured online help, supported major/minor releases, and authored release notes and known issues for 12+ patches.
Open case study →
Delivered 40+ learning assets across English and Japanese titles and coordinated technical and linguistic review across international vendors.
Open case study →
Enterprise documentation across 10+ major release cycles for the Mantas Behavior Detection Platform, Fraud Detection, and Oracle FCCM solutions, with writing-team leadership and US–India coordination.
Open case study →Information architecture, governance, content models, review cycles, and cross-functional documentation operations.
Git, GitHub, Markdown, MkDocs, static-site publishing, YAML, pull requests, and CI/CD workflows.
Online help, knowledge bases, release notes, SOPs, runbooks, troubleshooting, installation guides, policies, and training.
LLM-assisted review, RAG readiness, metadata, chunking, traceability, privacy checks, and human-in-the-loop approval.
The public implementation demonstrates source control, deterministic style checks, AI-assisted contextual review, human approval, static-site generation, and multi-format publishing.
See the workflowExplore the public GitHub work, download the current CV, or connect through LinkedIn and Scriptorium Lab Services.