A generation is being priced out of white-collar work. I mapped the gap, then built the tool that addresses it.

Live deployed product. Designed, architected, and shipped solo using an agentic AI build method.

In 2025, 66% of enterprises cut entry-level hiring because of AI. Graduate unemployment hit 5.8%. The entry-level white-collar path was supposed to be safe. It is contracting faster than any adjacent alternative is expanding.

Trades are expanding. The information layer that would help a young person navigate into them does not exist.

That is not a content problem. It is an architecture problem. Nobody has built the orientation layer that sits before the commitment.

How I mapped it.

I mapped the system before I touched the product. Who holds the knowledge. Where it lives. Why it never reaches the person who needs it at the moment they need it. Then I built an intervention at the exact point the system fails.

35%

Revelio Labs

drop in US entry-level job postings since AI tools went mainstream

66%

IDC / Deel, 2025

of enterprises cutting entry-level hiring because of AI

5.8%

2025

unemployment rate among recent university graduates

How I work.

I sketched. I mapped the emotional architecture. I put the pieces together so I could see the system before I tried to fix it.

Sketch of the platform's emotional architecture: a young person's fears about the future, anchored by 'my dad lost his secure job,' set against real industry workers sharing their experience.

Illustrated scenario opening the case study: Robin, worried about jobs lost to AI, shown at a family dinner and then alone, searching for direction and asking friends for help.

Wireframe sketch of the worker story screen: a video of Blake, a young tradesperson, beside a scrollable bento summary of his experience and a 'what's next?' action button.

Wireframe sketch of the explore screen: a grid of trade categories filterable by terrain type, with a swipeable carousel of trade environments below.

How I Build

The structural read

Entry-level white-collar work is contracting because AI handles the tasks that justified those roles. Trades require physical presence, contextual judgment, and accumulated tacit knowledge. The gap is not skills. It is information asymmetry at the point of entry.

The social weight

When a generation cannot find stable footing, the cost does not stay private. It compounds across healthcare systems, housing markets, and social cohesion. Designing for that moment is not charity. It is upstream intervention.

The business case

Platforms that own the orientation moment own the relationship. The apprenticeship and trades market in the US alone is a $200B+ sector with no consumer-facing product at the discovery layer. Terrain sits at that entry point.

Here is what I found. As of June 2026, there is almost no way for a young person to see the terrain ahead and get a foot in. The tools that cover the rest of the journey exist. The first step, the seeing, does not.

Robin did what anyone would. Searched. Asked around. Found a lot of advice and nothing that helped you see what a kind of work actually feels like before you commit your time, your money, your hope to it.

So here is what I built. A way to walk the terrain before you commit to it.

Try it the way Robin would. There is no right answer, and nothing to sign up for. Just look around.

This prototype is live. Interactions are measured anonymously to improve it.

ShippedLiveWCAG 2.2 AADesign system enforcedBuilt solo by directing AI agents

How it was built

I directed this build end to end using Claude Code. No team. No handoff. Design tokens enforced at write-time via pre-commit hooks. Accessibility audited to WCAG 2.2 AA. Sixteen documented build sessions.

This is not a prototype made in Figma. It is a deployed product with live interactions, measurable in GA4, built to a production standard by one person directing AI agents.

Stack

Next.js, React, TypeScript, Tailwind CSS, Framer Motion, deployed on Vercel.

Method

Built in Claude Code through an agentic design workflow. Design tokens enforced at write-time via pre-commit hooks, so the system cannot drift. Accessibility audited with axe-core and Playwright to WCAG 2.2 AA. Documented throughout in a 16-entry process book.

No team. No handoff. One person directing the build.

This is how I work. I start with the person, not the product. I stay with the problem longer than is comfortable. Then I direct the build myself, end to end, until the thing exists.

Robin is one story. The method is the same every time.

This is what I do for organisations.

I find the place where your AI deployment is creating a gap your current team cannot see, design the intervention, and direct the build myself. You get a system that works, documented so your team can maintain it, built faster than a traditional hire cycle.

If you are making decisions about AI implementation and you want someone who starts with the structural problem, let's talk.

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