I am a systems builder focused on human‑AI collaboration and AI architecture. My work centers on designing systems where language models operate within governed environments that preserve truth, continuity, and structured reasoning.
Over the past year I have developed several interconnected systems, including AiMe (a persistent human‑AI partnership system) and Ethos (a framework for evaluating value‑driven behavior under pressure using structured evidence pipelines). My work explores the idea that intelligence in AI systems emerges not from models alone, but from the architecture surrounding them.
I enjoy building systems that turn complex technical capabilities into simple, reliable experiences for real users. My approach emphasizes truth as an architectural property, model‑agnostic infrastructure, and clear human‑centered interaction.
Architected and built a persistent AI system designed for long‑term human collaboration. The system separates model capabilities from system governance, allowing language models to operate within a structured architecture that preserves continuity, memory, and truthfulness. AiMe integrates persistent conversational memory, system‑level governance layers, and model‑agnostic infrastructure to support reliable human‑AI interaction over extended periods of work.
View AiMe →Designed and implemented a structured pipeline for analyzing whether historical figures maintained their values under pressure. Ethos uses deterministic extraction layers, phrase analysis, polarity modeling, and a three‑model adjudication panel to evaluate passages from historical texts. The system emphasizes traceable evidence, reproducibility, and architectural safeguards against hallucination or interpretive drift.
View Ethos →Developed a framework focused on making truthfulness an architectural property of AI systems. Verum uses verifiable sources, structured retrieval, and system‑level provenance tracking to ensure responses can be traced back to evidence rather than model inference alone.
View Verum →Designed and implemented an intelligent infrastructure monitoring system that moves beyond traditional alert‑based monitoring. Instead of relying on static thresholds, Marshal analyzes contextual signals — trend velocity, temporal baselines, correlated anomalies — to detect developing incidents before thresholds are breached. The system operates through a deterministic Confidence Gate that evaluates signal significance, action confidence, and operational risk to determine one of four response tiers: Autonomous, Notify & Act, Recommend, or Observe. All actions are written to an append‑only audit log prior to execution, ensuring complete system accountability.
View Marshal →Founder and lead architect of an independent AI systems initiative focused on human‑AI collaboration and system architecture.
- Designed and implemented architecture for multi‑component AI systems
- Developed frameworks emphasizing truth, traceability, and system governance
- Integrated multiple model providers into model‑agnostic infrastructure
- Designed interaction layers translating complex AI systems into usable human experiences
- Published research and technical writing exploring AI architecture and human‑AI partnership
Owner and operator of a residential and commercial property maintenance business. Responsibilities include client management, operational planning, logistics, equipment maintenance, quoting and estimating, and service execution. The business operates as a self‑managed enterprise requiring strong prioritization, reliability, and direct customer engagement.
Worked within a family‑operated residential care facility supporting daily operations and management. Responsibilities included facility maintenance, logistics coordination, and assisting with operational oversight in a care environment requiring reliability and attention to detail.
Before founding Canady Lawn Service, worked across a range of independent technical and skilled trades roles including construction and carpentry, horse training and farrier work, equipment maintenance and repair, and independent contract work across multiple trades. These roles developed practical problem‑solving ability, self‑direction, and the habit of learning new technical domains quickly.
Interest in computing began in the early 1980s with the Commodore VIC‑20, leading to a lifelong interest in computers, systems, and technology. This foundation eventually evolved into the independent development of modern hardware extensions for classic Commodore computers under the commodore4ever name — including Wi‑Fi modems and dual‑display communication devices — and ultimately into the independent development of modern AI systems beginning in November 2024.