Why should you care?

Ops and product teams need actionable AI outputs inside the same Angular admin they already use.

See it live — copy this example

Paste into your MeanVerse project (Angular + Express + MongoDB), then run with ng serve / node / mongosh as noted.

// meanverse-ai/
//   api/src/routes/insights.routes.ts
//   web/src/app/features/ai/insight.service.ts

router.get('/insights/fraud-summary', auth, requirePermission('insights:read'), async (req, res, next) => {
  try {
    const summary = await InsightService.fraudSummary(req.user.tenantId);
    res.json(summary);
  } catch (e) { next(e); }
});

@Injectable({ providedIn: 'root' })
export class InsightService {
  private http = inject(HttpClient);
  fraudSummary() {
    return this.http.get<FraudSummary>('/api/ai/insights/fraud-summary');
  }
}

What happened?

• API aggregates Mongo transactions with risk scores — model may run in Python sidecar.
• Angular InsightService fetches summary for charts; permission gates sensitive AI data.

Practice next

1. Store model scores on transaction documents or insights collection.
2. Expose read-only summary endpoints — no model in browser.
3. Build Angular dashboard with signals for KPI cards.
4. Add date range filter param to fraud-summary.
5. Cache summary in Redis for 60s to protect model service.

Remember

AI dashboard = API aggregates + Angular viz. Keep models and keys server-side. RBAC on insight endpoints.

Fraud ops center

Analysts review top 50 flagged transfers each morning.

Outcome: Dashboard cuts review time 30%; false positive rate tracked weekly.