AI Operations Builder · Ho Chi Minh City, Vietnam
I find the leverage point in a messy workflow — and direct AI to build the fix.
I'm Joshua James Yenson. Not an engineer by trade — an operator who spent 5+ years running people and workflows in a fast-paced, multi-campus environment. When something was costing time or leads, I didn't wait for IT — I directed Claude and Claude Code to fix it myself. Now I build that same kind of fix for small businesses: missed calls, slow follow-ups, and manual admin, turned into tools that actually get used.
- AI tools shipped & in daily use
- 5+
- team members managed day to day
- 20+
- qualified leads sourced by an AI pipeline I built
- 184
AI tools shipped & in daily use
team members managed day to day
qualified leads sourced by an AI pipeline I built
This is what changes
If a workflow is quietly costing you leads or time, I can probably fix it.
Missed calls, unanswered website enquiries, manual admin — the same pattern I fix for my own team at Vinalearn, built as a product for yours.
Before
- A call comes in while you're on a job. It goes to nothing.
- A website visitor asks a question after hours. No one answers.
- By the time you follow up, they've already called someone else.
After
- The caller gets an instant text back — the lead stays warm.
- The website visitor gets an answer immediately, day or night.
- You follow up when you're free, with the details already captured.
That's the whole shift.
Selected work
AI tools built and shipped
Real operational problems, fixed with AI I directed and shipped — not case studies about tools that never left a slide deck.
How I build
Same loop, every project
Five very different tools, one repeatable process — this is what actually happens between 'this is annoying' and 'the team uses it every day.'
Spot the leverage point
I sit inside the workflow first — as the manager running it, not an outside consultant. The bottleneck has to be real and specific, not a hypothetical one.
Scope the fix precisely
What surface changes, what data feeds it, what "good" looks like. A vague brief produces a vague tool — this is the step people skip.
Direct AI to build it
I'm not an engineer — Claude and Claude Code do the implementation. My job is precise direction and reviewing every output against how it'll actually be used.
Ship it, then iterate
It goes in front of the team that has to use it. I watch what breaks, diagnose root cause over quick fixes, and iterate until adoption is real, not rolled out.
Spot the leverage point
The short version
Most people wait for a handoff.
I open the tools and build the fix myself.
Capabilities
Two skill sets, one loop
The reason these tools get used instead of shelved is that I've sat on both sides of the problem: I know the workflow because I run it, and I know how to direct AI to fix it.
AI & Automation
- Daily, hands-on use of Claude & Claude Code
- Workflow automation (n8n, project-based)
- Prompt design & AI orchestration
- Scoping & directing AI-built tools
- Testing & iterating on AI output against real usage
Operations & Leadership
- Teacher recruitment & onboarding, full cycle
- Structured observation & evaluation frameworks
- KPI systems & performance management
- Change management & genuine adoption, not just rollout
- Formal reporting & documentation
Next step
Have a workflow that's costing you leads or time?
I'm always interested in operational problems that a well-scoped AI tool could actually solve.