I design AI-enabled applications and information systems that make complex information accessible, useful, and actionable. My work connects artificial intelligence with software engineering, cloud architecture, and domain expertise to support research, improve workflows, and strengthen informed decision-making.
My approach begins with the information itself: its origin, structure, context, permissions, and reliability. Effective AI depends on more than selecting a model. It requires an architecture that connects relevant knowledge to the right task, preserves meaningful distinctions, and enables people to evaluate the resulting output. This is particularly important in legal, government, and enterprise environments, where accuracy and traceability directly affect the value of a system.
AI Architecture and Application Integration
I approach AI as part of the complete application lifecycle, from defining the business problem and preparing information to integrating services and supporting ongoing operations. My focus includes large language model (LLM) integration, retrieval-augmented generation (RAG), semantic search, vector databases, and AI-assisted workflow automation.
These capabilities connect naturally with my broader work in API integration, content management systems, and cloud infrastructure. I design around the relationships between users, information, applications, and services so that AI capabilities serve an operational purpose within the organization.
Areas of focus include:
- AI application architecture: Connecting models, data sources, application logic, and user interfaces within maintainable systems.
- Knowledge retrieval and semantic search: Structuring information to support discovery by meaning, context, and relevance.
- Retrieval-augmented generation: Designing workflows that supply relevant source material to language models and support source attribution.
- AI agents and workflow automation: Connecting defined tasks with tools, organizational knowledge, permissions, and human review.
- Enterprise and CMS integration: Incorporating AI capabilities into Drupal platforms, APIs, and existing business processes.
- Cloud deployment and operations: Addressing access control, observability, performance, and operating cost throughout the architecture.
Information Quality and Human Judgment
My interest in AI is closely tied to a broader professional purpose: improving access to accurate information and challenging the assumptions that prevent established institutions from changing.
That purpose informs my work across legal information, public reference platforms, and contextual knowledge systems, including the Brady List and Quantum Dictionary. These projects present a common challenge: making substantial bodies of specialized information easier to organize, understand, and use without losing the context that gives them meaning.
I bring that same discipline to AI development. Source quality, retrieval relevance, access boundaries, and human review belong in the design from the beginning. A useful system should help people examine evidence, understand uncertainty, and exercise informed judgment.
Technical Leadership and Project Engagement
I contribute as an architect, hands-on engineer, and technical advisor, translating organizational goals into application designs and implementation priorities. I work across technical and nontechnical teams to identify useful applications for AI, assess integration requirements, and establish a practical path from concept to operation.
I welcome employment, consulting, and project partnerships involving AI application development, enterprise knowledge systems, legal technology, and cloud platform architecture. My strongest contribution is connecting the strategic purpose of a project with the information, software, and infrastructure required to deliver it.
