feat(ai): scorecards de QA, avaliacao automatica, dashboard (fase 21)

CRUD de QualityScorecard/Item (criterios por tenant, sem lista fixa
hardcoded), novo AIJobType.SCORECARD_EVALUATION encadeado junto com
ANALYSIS a partir da transcricao (mesma decisao de privacidade + exige
scorecard habilitado). apps/ai-worker avalia contra todos os scorecards
habilitados do tenant, prompt montado dinamicamente a partir dos itens de
cada um, nunca guarda chain-of-thought do modelo (so' o resultado final
validado). GET /reports/ai-dashboard agrega CallAIAnalysis+QualityEvaluation
do periodo (score medio, sentimento, assuntos/objecoes, compliance
alerts, ranking de agentes).

Testado ponta a ponta contra o ai-worker real e Postgres real com RLS
(scorecard real via API, prompt montado a partir dos itens reais, job
real reservado via SKIP LOCKED, retry+dead-letter corretos) — chamada de
rede real contra OpenAI/Anthropic continua nunca exercitada. Detalhes em
docs/QUALITY_SCORECARDS.md.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01X1HxY46WGU4G1zmVDNKcWw

apps/api/src/reports/reports.controller.ts

@@ -18,6 +18,15 @@ function average(values: number[]): number | null {
   return values.reduce((sum, v) => sum + v, 0) / values.length;
 }
 
+function topCounts(lists: string[][], limit: number): { value: string; count: number }[] {
+  const counts = new Map<string, number>();
+  for (const list of lists) for (const value of list) counts.set(value, (counts.get(value) ?? 0) + 1);
+  return Array.from(counts.entries())
+    .sort((a, b) => b[1] - a[1])
+    .slice(0, limit)
+    .map(([value, count]) => ({ value, count }));
+}
+
 @UseGuards(JwtAuthGuard, PermissionGuard)
 @Controller("reports")
 export class ReportsController {
@@ -200,4 +209,69 @@ export class ReportsController {
       }),
     );
   }
+
+  /** Dashboard de IA (secao 119). `scoreMedio` = média de
+   * `CallAIAnalysis.qualityScore` (avaliação livre do modelo sobre a
+   * chamada); `avgScorecardScore` = média de `QualityEvaluation.score`
+   * (avaliação formal contra um scorecard, secao 117-118) — dois números
+   * diferentes, a especificação só pede "score médio" sem dizer qual dos
+   * dois, então mostra os dois em vez de escolher um. */
+  @RequirePermission("reports.view")
+  @Get("ai-dashboard")
+  async aiDashboard(@CurrentUser() user: AccessTokenClaims, @Query("from") from?: string, @Query("to") to?: string) {
+    const prisma = getPrismaClient();
+    const tenantId = user.tenantId!;
+    const range = dateRange(from, to);
+
+    const [analyses, evaluations] = await withTenantContext(prisma, tenantId, (tx) =>
+      Promise.all([
+        tx.callAIAnalysis.findMany({
+          where: { tenantId, createdAt: range },
+          include: { call: { select: { agentId: true } } },
+        }),
+        tx.qualityEvaluation.findMany({
+          where: { tenantId, createdAt: range },
+          include: { call: { select: { agentId: true } } },
+        }),
+      ]),
+    );
+
+    const agentIds = Array.from(
+      new Set([...analyses, ...evaluations].map((r) => r.call.agentId).filter((v): v is string => v != null)),
+    );
+    const agents = agentIds.length
+      ? await withTenantContext(prisma, tenantId, (tx) =>
+          tx.agent.findMany({ where: { id: { in: agentIds } }, select: { id: true, name: true } }),
+        )
+      : [];
+    const agentName = (id: string) => agents.find((a) => a.id === id)?.name ?? id;
+
+    const sentimentCounts: Record<string, number> = {};
+    for (const a of analyses) if (a.sentiment) sentimentCounts[a.sentiment] = (sentimentCounts[a.sentiment] ?? 0) + 1;
+
+    const agentScores = new Map<string, number[]>();
+    for (const a of analyses) {
+      if (!a.call.agentId || a.agentScore == null) continue;
+      const list = agentScores.get(a.call.agentId) ?? [];
+      list.push(a.agentScore);
+      agentScores.set(a.call.agentId, list);
+    }
+    const agentRanking = Array.from(agentScores.entries())
+      .map(([agentId, scores]) => ({ agentId, name: agentName(agentId), avgScore: average(scores)!, calls: scores.length }))
+      .sort((x, y) => y.avgScore - x.avgScore);
+
+    return {
+      callsAnalyzed: analyses.length,
+      avgQualityScore: average(analyses.map((a) => a.qualityScore).filter((v): v is number => v != null)),
+      avgScorecardScore: average(evaluations.map((e) => e.score)),
+      sentimentBreakdown: sentimentCounts,
+      topTopics: topCounts(analyses.map((a) => a.topics), 10),
+      topObjections: topCounts(analyses.map((a) => a.objections), 10),
+      complianceAlerts: analyses
+        .filter((a) => a.complianceFlags.length > 0)
+        .map((a) => ({ callId: a.callId, flags: a.complianceFlags })),
+      topAgents: agentRanking.slice(0, 5),
+      bottomAgents: agentRanking.slice(-5).reverse(),
+    };
+  }
 }