About Vitale

Inspiration

The hardest part of learning isn't gaining skills. It's proving them.

Students build startups, contribute to open source, complete internships, create side projects, and spend hundreds of hours learning with AI. Yet when they apply for jobs, most of that work is compressed into a resume, portfolio, or a single final deliverable.

The journey from learner to earner is broken because the process, where real judgement and ability are demonstrated, is rarely captured.

Our thesis is simple:

The process is the proof.

What it does

Vitale transforms the work produced throughout a project into an evidence-backed proof-of-work profile.

Users upload artifacts such as:

• AI research chats
• Coding-agent logs
• GitHub repositories
• Research notes
• Pitch decks

Vitale analyzes how the work was actually done, extracting evidence of problem solving, judgement, technical execution, AI collaboration, and communication to generate an employer-readable proof-of-work report.

How we built it

Vitale is built with Next.js, TypeScript, Tailwind CSS, shadcn/ui, Supabase, and the GitHub API.

We built a modular analysis pipeline that parses each artifact independently, extracts structured metadata, maps observations into evidence, and generates transparent skill signals. Rather than relying entirely on an LLM, deterministic extraction and scoring are separated from semantic reasoning, making every insight explainable and evidence-backed.

Challenges

Our biggest challenge was determining what actually counts as credible evidence. Different artifacts expose different signals, levels of reliability, and privacy considerations. We wanted every conclusion to be grounded in observable evidence while being honest about uncertainty and confidence.

What we learned

AI hasn't removed the need for human judgement. It has changed where that judgement becomes visible.

Today, the strongest signals aren't just found in the final deliverable, but in how someone frames problems, collaborates with AI, iterates, verifies information, and makes decisions throughout the project.

What's next

We believe demonstrated ability should be easier to recognize than credentials alone.

Our vision is to build an evidence layer that helps bridge the gap between learning and earning, allowing people to turn real work into credible opportunities, regardless of where they learned or whether they came first.

Built With

• codex
• javascript
• openai
• typescript
• vercel