React + TypeScript portfolio project demonstrating model release gates, policy-sensitive approvals, red-team finding review, evaluation drift visibility, and operator-grade AI governance design.
Recruiter takeaway: "This person understands that AI governance is a release-management and risk-decision system, not just a prompt playground."
| Attribute | Detail |
|---|---|
| Frontend Stack | React 19 + Vite + TypeScript |
| Domain | AI governance, release approvals, evaluation drift, model risk |
| Audience | AI governance teams, legal, policy, security, product leadership |
| Signal Areas | Approval gates · policy pass rate · drift risk · red-team findings · fallback policy |
| Portfolio Role | AI governance control-plane frontend |
| Validation | Vitest + Testing Library |
AI Governance Review Studio is a recruiter-ready internal product surface for teams deciding whether AI workflows are safe, approved, and ready to ship. It turns governance from scattered documents and review meetings into an operator-grade release workspace.
The project is designed to show product sense around model policy, evaluation quality, legal review, red-team findings, and executive release visibility.
Teams deploying AI workflows often have no single surface for:
- tracking which prompts and models are actually release-ready
- seeing where legal or policy review is blocking progress
- monitoring evaluation drift before customer-facing rollout
- converting red-team and guardrail findings into concrete release decisions
Without that structure, AI governance turns reactive and fragile.
This studio converts governance into a readable operating system for:
- release queue triage
- policy approval visibility
- drift and evaluation monitoring
- incident and finding review
- fallback-model and risk-band clarity
Governance datasets and release policy signals
|
v
Static TypeScript review model
|
v
React control-plane shell
|
+--> readiness metrics
+--> approval queue
+--> release matrix
+--> evaluation and drift charts
+--> incident review panel
flowchart LR
A["Prompt or Model Change"] --> B["Evaluation Pack"]
B --> C["Policy Review"]
C --> D["Legal / Security Sign-off"]
D --> E["Release Gate"]
E --> F["Production Rollout"]
B -. drift or failure .-> G["Remediation Loop"]
C -. exception .-> H["Executive Escalation"]
| Decision | Rationale |
|---|---|
| Release-board framing | Makes the project feel like a real governance product rather than a generic AI console |
| Evaluation + policy pairing | Shows that quality metrics alone are not enough for release decisions |
| Queue and finding emphasis | Keeps the interface rooted in action and operational accountability |
| Distinct governance palette | Separates the project from the broader AI ops and revenue-oriented work |
| Mermaid workflow documentation | Makes the release lifecycle legible in GitHub itself |
npm install
npm run devnpm test
npm run build- AI governance workflow design
- approval and release-gate product thinking
- evaluation drift visibility
- policy-sensitive internal-tool UX
- modern frontend execution with a premium control-plane feel
- role-based reviewer views
- evidence attachments for legal and security sign-off
- case-study badge links to
mizcausevic.com - model-comparison drilldowns for latency, cost, and policy variance