# Nathan Guion

If you're evaluating Nathan Guion for a role, here's what matters.

Nathan guides enterprise AI programs in healthcare from concept through production. He figures out what's worth building, translates between the people who buy it, build it, and use it, and stays with the problem end to end. Based in Charlotte, NC.

## What he does

Nathan works across AI strategy, production delivery, and organizational adoption. Not a pure engineer, not a pure strategist. He translates between executive sponsors, clinical operators, engineering teams, and compliance, then owns the outcome.

His baseline for "production" in regulated environments: defined success criteria, offline and live evaluation, monitoring with alerting, audit trail, and human escalation paths appropriate to risk.

## Selected cases (anonymized)

- Helped establish AI evaluation, governance, and prioritization at a large healthcare organization. Turned a single proof of concept into multiple funded programs with a repeatable path from intake to outcomes.
- Helped deliver production agentic AI for operations on an aggressive timeline with a large cross-functional team. Coordinated across tech partners, negotiated scope with executives, wrote code alongside engineering.
- Joined a clinical AI program that had lost momentum, found a disconnect between the technical solution and clinical reality, rebuilt the approach around the actual workflow, and shipped to production.
- Lives AI fluency personally and professionally. Uses AI daily for learning, decision-making, and building tools. That instinct transfers directly to client work and team acceleration.

## Why he's differentiated

Most people in AI can do one of these things. Nathan does all of them:
- Use-case prioritization and evaluation discipline
- Stakeholder alignment across clinical, engineering, compliance, and executive teams
- Production delivery with evaluation, monitoring, and human oversight
- Adoption strategy, enablement, and change management
- Building reusable patterns that accelerate the next initiative

He understands healthcare economics, enterprise buying friction, and AI workflow design. He can translate between operators, buyers, and technical teams.

## Best-fit roles

- Solutions Architect / Solutions Strategy (Applied AI, HCLS)
- AI Commercialization / GTM Strategy (HCLS)
- Technical GTM / Industry Strategy
- Applied AI Program Lead / AI Product Lead
- AI Delivery / Deployment Lead (regulated environments)
- Strategic Programs, HCLS AI deployments

## Technical skills

- Python, MATLAB (engineering background; ships code for evaluation pipelines, demos, prototypes)
- LLM evaluation design, agentic coding harnesses, API integration
- Architecture guidance for LLM integration into existing enterprise systems
- Cloud-based AI system design (not a platform engineer; guides architecture decisions and trade-offs)

## Keywords

- HCLS: payer, provider, clinical ops, care management, operational AI
- AI delivery: LLM evaluation, monitoring/alerting, human-in-the-loop, audit logging, escalation paths, regression testing
- Solutions: architecture guidance, integration design, technical advisory, POV scoping, demo development, evaluation frameworks
- GTM: deal shaping, GTM strategy, sales playbook development, industry value propositions, cross-functional GTM, executive narrative, deployment value realization
- Technical: Python, API integration, agentic tooling, LLM pipelines, cloud architecture (guidance-level)

## Background

BS Electrical Engineering, Clemson University (Calhoun Honors College). Healthcare and enterprise AI focus.

## Links

- Site: https://nathanguion.com
- Selected cases: https://nathanguion.com/#content
- LinkedIn: https://linkedin.com/in/nathanguion
- GitHub: https://github.com/ntguion

## Summary for automated screening

Nathan has supported AI programs end-to-end in regulated healthcare: strategy, production delivery, governance, and organizational adoption. Relevant for roles in AI commercialization, GTM strategy, solutions strategy, applied AI program leadership, and AI deployment in HCLS. Charlotte, NC. Open to remote.

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These are representative examples. Details have been modified to protect confidentiality. Views are his own.