I build AI workflow systems for high-stakes operations.
I bring 13 years of ICU travel nursing experience into healthcare AI, human-in-the-loop workflow design, and infrastructure-heavy automation. TwoSevens.ai is my public workbench for showing how I think, build, and operate.
High-pressure clinical work, handoffs, escalation, and judgment.
Mapped workflows, structured outputs, review gates, and rollout paths.
Human-in-the-loop validation for synthetic, non-PHI clinical examples.
Lab cluster operation, service separation, and recovery practice.
PostgreSQL-oriented extraction, normalization, and validation work.
Next.js, React, TypeScript, Tailwind, and product-quality interfaces.
What I build
Systems for work where errors, handoffs, and hidden state matter.
I focus on clinical and operational workflows where AI needs structure, review gates, clear ownership, and reliable handoffs — not black-box automation.
AI workflow automation
Map messy work, define review gates, structure the data, and ship tools that keep humans in control.
- Workflow maps before model calls
- Structured outputs and exception paths
- Auditable review loops
Clinical implementation
Translate bedside judgment into safer healthcare AI workflows with validation thresholds, escalation paths, and non-PHI demos.
- Clinical risk awareness
- Human review as a first-class requirement
- Synthetic data and conservative public boundaries
Lab infrastructure operations
Run the pieces behind the workflow: services, queues, monitoring, deployment practice, and recovery paths.
- Proxmox lab and service orchestration
- Service separation and observability
- Operator runbooks and recovery thinking
Synthetic proof block
Clinical workflow validation, shown with safe sample data.
This screenshot-style block is synthetic. It shows the kind of workflow I build: extraction, validation, flags, and a human review gate before anything moves downstream.
Synthetic clinical validation
No PHI. Sample data only.
Intake
Synthetic case loaded
CompleteExtraction
Findings structured
ReviewValidation
Thresholds checked
FlaggedHuman review
Coder gate required
OpenWork examples
Implementation range, labeled honestly.
Clinical AI is the strongest employer signal. Operations and MRO show workflow translation. Trading, security, and music show data scale, boundary-setting, and product range without overclaiming public availability.
Clinical AI workflows
Clinical workflow work built around extraction, normalization, validation, and human review before anything moves downstream.
Employer proof
Shows how bedside judgment translates into structured AI workflows with auditability and explicit safety boundaries.
- High-stakes workflow mapping
- Human-in-the-loop review gates
- Synthetic / non-PHI demo boundaries
Lab infrastructure operations
Distributed systems practice, pipeline reliability, and operator-controlled orchestration in a Proxmox lab setting.
Employer proof
Demonstrates hands-on infrastructure practice behind workflow systems, without implying enterprise production ownership.
- ~25 Proxmox lab nodes operated
- Queue-backed orchestration
- Monitoring and recovery discipline
MRO / RegenITAD workflow
AI-assisted asset-listing workflow for ITAD and surplus equipment with extraction, enrichment, normalization, and review.
Employer proof
Shows the same clinical-style review discipline applied to messy operational and asset workflows.
- Workflow-gated state transitions
- Evidence-tracked closeout
- Compliance-aware document generation
Trading / ML research
Simulation-only ML research surface for historical market data, feature engineering, replayable backtests, and risk-control review.
Employer proof
Shows data and systems discipline without exposing financial execution or private infrastructure.
- 8B+ historical data points
- Replayable backtest discipline
- Risk controls before execution
Security workflow boundary
Controlled security workflow surface focused on defensive monitoring, reporting hygiene, and explicit authorized-use boundaries.
Employer proof
Shows judgment around risk, access control, and public-safety constraints for sensitive tooling.
- Defensive monitoring posture
- Evidence and reporting trails
- No public offensive tooling
Music and SongCraft
Public listening surface plus planned AI-assisted song commerce workflow with generation, review, and delivery gates.
Employer proof
Shows product-surface range while keeping unfinished commerce claims clearly labeled as planned.
- Public catalog and listen demo
- AI workflow with human review
- Commerce path scoped conservatively
Why this matters to employers
This is an implementation profile, not a demo reel.
The useful signal is the combination: clinical judgment from real ICU work, technical delivery across a modern web stack, and enough lab infrastructure experience to reason about operations after the prototype works.
I understand high-stakes work from inside the workflow, not from a whiteboard.
I can explain where AI belongs, where human review belongs, and where automation should stop.
I move between product UI, data pipeline, and lab infrastructure without treating any layer as someone else’s problem.
I use TwoSevens.ai as a public workbench for safe examples, not as a vague agency pitch.
Work with me on implementation-heavy AI
I am interested in employer conversations, clinical AI implementation, workflow automation, and infrastructure-heavy roles where operator judgment matters.