Shpetim HaxhiuSHPito.devFull-stack AI systems
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Productized AI app

SlotShield AI

A productized AI web app with modern UI, Vercel deployment, mapped GitHub repo, and screenshot-backed portfolio evidence.

Open live appVercel URLGitHub
Outcome
A compact shipped AI product candidate that demonstrates fast concept-to-deployment execution.
AI productNext.jsVercelTypeScript
SlotShield AI deployed app screenshot.
Challenge
Small AI product candidates need enough polish and evidence to be understandable before the full product strategy is finalized.
Approach
01

Ship the product surface with a custom domain and mapped repository.

02

Use screenshot-backed evidence from the Vercel portfolio inventory to keep the project inspectable.

03

Treat it as a candidate for further positioning once the exact product niche is validated.

Project article

Why this project matters.

A short review of the product surface, workflow signal, and portfolio value.

SlotShield AI is included as a productized AI candidate rather than a fully explained flagship. It has the important delivery evidence: a live URL, Vercel production deployment, GitHub mapping, and screenshot capture.

The project demonstrates fast app execution and the ability to package AI ideas into public surfaces. The next portfolio step would be tighter product positioning and a clearer use-case narrative.

It remains valuable in the project set because it shows breadth and shipping cadence while the stronger flagship stories carry the deeper workflow detail.

Proof points

What the system makes visible.

These are the artifacts that make the project usable beyond the first demo.

Custom-domain deployment
Mapped repository
Portfolio screenshot evidence
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Contact
Bring the product pressure, system constraints, and expected business outcome.
Send the desired outcome, users, current bottleneck, stack, and timeline. I will respond with a practical senior engineering path for the build.
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