I build AI workflows for high-stakes operations.
TwoSevens.ai is my public workbench for an unusual profile: ICU clinical experience, AI workflow implementation, lab infrastructure practice, and the willingness to own the work after the first demo runs.
The founder story
Operator before founder. Builder before pitch deck.
My background starts in ICU travel nursing, where systems have to survive ambiguity, pressure, handoffs, and real consequences. That experience shapes how I build AI workflows: start with the work, make the state visible, define the review gates, and keep humans responsible for high-stakes decisions.
The technical side of TwoSevens.ai grew from the same habit: build working examples, connect the data and infrastructure, and label boundaries honestly. Clinical AI, MRO workflow automation, trading research, security workflow boundaries, music tooling, and infrastructure operations show range without turning into separate startup pitches.
13 years as an ICU travel nurse in high-pressure clinical environments.
Hands-on AI workflow implementation across clinical, operations, data, and product surfaces.
Next.js, React, TypeScript, Tailwind, PostgreSQL, Docker, and infrastructure operations experience.
Public proof pages designed around conservative claims, protected demos, and clear safety boundaries.
How I work
The work has to be understandable before it can be automated.
The goal is not to add AI decoration to a workflow. The goal is to make the workflow legible enough that automation can be constrained, evaluated, and trusted.
Workflow first
The system starts with how work actually moves: inputs, handoffs, exceptions, approvals, and failure points.
AI behind gates
Model output should be structured, reviewable, and routed through human judgment when the domain is high-stakes.
Operate the system
Infrastructure, monitoring, queues, recovery paths, and runbooks are part of the product, not afterthoughts.
Why ICU experience matters here
Clinical operations teach you to respect ambiguous inputs, incomplete context, fragile handoffs, and the difference between a system that looks clean and a system that survives contact with real work. That is exactly the discipline AI workflow implementation needs.
What I am looking for
- AI implementation roles where the hard part is mapping messy work into reliable systems.
- Healthcare AI and clinical workflow roles that need real operator judgment in the room.
- Infrastructure-heavy automation roles where services, queues, data, and runbooks all matter.
- Founder/operator technical roles that need someone who can build, test, ship, and keep moving.
Résumé PDF
Employer summary
Code samples
Code samples available on request
Direct email
[email protected]
Professional profile