Scott JosephsonAI Product Builder
I own the path from AI strategy to working software: problem framing, architecture, agentic workflows, retrieval systems, governance, implementation, and deployment.
Featured Projects
Patent-pending autonomous audio mixing platform connecting AI research, custom architecture, agent orchestration, and production deployment.
AI-enabled healthcare applications for clinician decision support concepts, patient education, drug discovery workflows, and trial intelligence.
A public-company disclosure intelligence project that makes SEC filing content easier to explore, interpret, and turn into useful research signals.
A PyPI-published Python toolkit for typed, validated Spec-Driven Development specs and AI coding-agent ingestion artifacts.
A practitioner’s guide to transforming ambiguous software intent into implementation-ready specifications for product teams and AI agents.
Published and preprint research on generative AI, agentic AI, prompt engineering, RAG, source separation, and musical score writing.
About
I am an AI consultant, senior engineering leader, product builder, author, and PhD researcher. My work combines the precision of software engineering with the ambiguity-handling required for enterprise AI adoption.
I have built and led AI work across healthcare, life sciences, federal systems, telecom, consulting, and independent product ventures. The common thread is practical translation: converting complex AI capability into usable architecture, governed workflows, and deployed applications.
Operating through Intelligytics to advise, architect, and build AI systems across strategy, agentic AI, product engineering, and applied research.
Published Tickets Don’t Compile and released specddkit, a Python library for structured Spec-Driven Development and AI coding-agent workflows.
Focused on AI-driven music systems, LLMs, retrieval, prompt engineering, multi-agent workflows, and symbolic/semantic music intelligence.
Led and delivered AI, NLP, ontology, semantic data fabric, RAG, and cloud engineering work across IQVIA, Amgen, federal programs, and consulting engagements.
Core Competencies
Roadmaps, operating models, AI Centers of Excellence, governance frameworks, use-case prioritization, and delivery planning.
LLM orchestration, multi-agent workflows, prompt engineering, evaluation loops, tool use, RAG, GraphRAG, and model-grounded applications.
Full-stack AI applications, Python services, cloud deployment, data pipelines, semantic layers, observability, and secure software delivery.
Clinical NLP, ontologies, knowledge graphs, decision-support concepts, trial intelligence, patient-facing AI, and evidence-grounded outputs.
A disciplined way to align product intent, executable requirements, validation, and AI coding-agent context before implementation begins.
Turning research in LLMs, music AI, agentic systems, and retrieval into usable product architecture and measurable technical programs.
Research Papers
“The best AI programs do not start with model enthusiasm. They start with a clear operating model for evidence, architecture, governance, evaluation, and implementation.”
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