Local and Hybrid Inference
Assess local-model deployment, hosted-model tradeoffs, routing, resource constraints, observability and failure handling.
APPLIED AI ENGINEERING / TECHNICAL EXPLORATION
Useful AI requires more than a model.
A practical approach to AI architecture, local compute, agent orchestration, governance and evidence-based evaluation—rooted in extensive software engineering experience.
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01 / SYSTEM ARCHITECTURE
Design for control.
Engineering Workflows
Explore coordinated agents for coding, test generation, review, documentation and research with explicit human acceptance boundaries.
Measurable Behavior
Define reference datasets, reproducible tests, accuracy and failure metrics, human review, and regression checks rather than relying on plausible outputs.
Bounded AI Adoption
Clarify allowable data flows, model access, external-provider exposure, logging, authorization and confidential information controls.
02 / HIGH-ASSURANCE CONTEXT
AI is a component.
Accountability is a system.
Regulated environments call for clear intended use, documented design decisions, independent verification, change control and risk-based validation. AI can assist these activities without substituting for them.
When discussing medical-device applications, deployment controls must be evaluated against the actual regulatory classification, intended use, quality system and software safety context.
This page describes engineering approaches and research interests. It does not assert that any AI-enabled medical product has received regulatory clearance or approval.
03 / EVOLVING PRACTICE
From experiments to repeatable engineering.
Current independent explorations include GPU-backed local inference, model routing, code-quality agents, automation pipelines and AI-assisted creative systems. Future case studies will distinguish prototypes, validated results, and production deployments.
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