Projects & Platforms

Local-first memory pipeline for macOS — captures screen, audio, email, calendar, and documents as structured Markdown for AI-powered querying. Reference implementation for Context Compilation Theory.

Local-first personal intelligence operating system for macOS. Captures context from email, calendar, screen, documents, and notes — makes it searchable and actionable. Privacy-preserving, self-healing, commercial-grade.

Governed lifecycle tooling for agentic engineering work. Reference implementation for Cybernetic Software Delivery — nine-stage lifecycle, artifact taxonomy, four-tier governance, and DORA-extended metrics.

An open-source, data-first multi-agent chat platform with 28 configurable domain agents, 4 fully-customizable chat modes, 4-tier memory with Context Graph, GraphRAG-lite, self-learning ETL, and comprehensive observability.

Building next-generation ETL pipelines powered by reinforcement learning with advanced Plan-Act-Reason loops that enable true self-learning capabilities—pipelines that reason about their transformations and continuously optimize their behavior.

Constructing three-dimensional ontological representations that capture the full complexity of enterprise data architectures—enabling AI systems like RAG and recommendation engines to navigate semantic relationships with unprecedented depth.

Advanced context engineering techniques that exploit transformer attention patterns to maximize information utilization in agentic systems—positioning critical context where attention naturally focuses.

Designing and implementing production-grade agentic RAG systems with 12-15+ specialized agents that optimize retrieval pipelines across terabytes of enterprise data, overcoming recency bias and attention dilution through advanced context engineering.

Research and implementation of novel context engineering techniques that overcome fundamental limitations in attention mechanisms, enabling more effective use of extended context windows in production LLM applications.

Building intelligent data integration systems that use Plan-Act-Learn loops to continuously adapt, learn, and optimize—moving toward fully autonomous data pipeline management.

Designing and implementing enterprise data fabric architectures that unify disparate data estates, enabling trustworthy AI through comprehensive governance, lineage, and quality frameworks.

Developing production systems for real-time AI output quality assessment, including hallucination detection, factual grounding verification, and reinforcement learning-based response optimization.

Research and published writing on the evolution of software interfaces toward AI-native paradigms, where traditional SaaS becomes headless infrastructure and agentic systems become the primary user interface.

Over two decades of graduate-level teaching, doctoral mentorship, and academic research in AI, data architecture, and enterprise systems. Published author of books, academic papers, and industry courses.