# Ubuntu 22.04 Base Setup
sudo apt update && sudo apt upgrade -y
sudo apt install -y docker.io nginx redis-server
sudo systemctl enable docker nginx redis

// Voice webhook configuration
exports.handler = function(context, event, callback) {
  const twiml = new Twilio.twiml.VoiceResponse();
  if(event.DialCallStatus === 'completed') {
    // Successful call handling
  } else {
    // Fallback to backup number
    twiml.dial({
      action: '/handle-fallback'
    }, process.env.BACKUP_NUMBER);
  }
  callback(null, twiml);
};

// Salesforce API Example
const query = `SELECT Id, Name, Phone, LastCase__c
FROM Account WHERE Phone = '${formattedNumber}'`;

const response = await axios.get(
  `${SF_INSTANCE}/services/data/v56.0/query`,
  {
    params: { q: query },
    headers: { Authorization: `Bearer ${token}` }
  }
);

// n8n Webhook Trigger Configuration { "name": "Call Center Workflow", "nodes": [ { "parameters": { "path": "inbound-call", "options": { "responseMode": "responseNode" }, "authentication": "basicAuth" }, "name": "Webhook", "type": "n8n-nodes-base.webhook", "position": [ 250, 300 ] }, // Additional node configurations... ] }

// Make.com scenario structure { "name": "Customer Sentiment Analysis", "blueprint": { "modules": [ { "name": "Twilio: New Call", "type": "trigger" }, { "name": "OpenAI: Analyze Sentiment", "type": "action", "parameters": { "model": "gpt-4", "prompt": "Analyze the sentiment in this customer interaction: {{1.transcript}}" } }, { "name": "Router", "type": "router", "conditions": [ { "path": "Negative", "condition": "{{2.sentiment_score}} < 0.3" }, { "path": "Neutral", "condition": "{{2.sentiment_score}} >= 0.3 && {{2.sentiment_score}} < 0.7" }, { "path": "Positive", "condition": "{{2.sentiment_score}} >= 0.7" } ] } ] } }

// Elasticsearch Time-Series Data Structure { "timestamp": "2023-10-15T14:23:45Z", "call_id": "c-abc123xyz", "duration_seconds": 342, "agent_id": "ai-support-1", "customer_id": "cust-456789", "initial_intent": "technical_support", "final_resolution": "issue_resolved", "resolution_path": ["authentication", "database_connection", "credential_reset"], "sentiment_scores": { "initial": 0.35, "final": 0.89, "lowest": 0.22, "trend": "positive" }, "llm_metrics": { "token_count": 3256, "inference_time_ms": 4532, "prompt_tokens": 1024, "completion_tokens": 2232 }, "escalation_data": { "was_escalated": false, "escalation_point": null, "escalation_reason": null } }

// Grafana dashboard configuration (JSON snippet) { "annotations": {...}, "editable": true, "fiscalYearStartMonth": 0, "graphTooltip": 0, "id": 123, "links": [], "liveNow": true, "panels": [ { "datasource": { "type": "elasticsearch", "uid": "elasticsearch" }, "fieldConfig": { "defaults": { "color": { "mode": "palette-classic" }, "custom": { "axisCenteredZero": false, "axisColorMode": "text", "axisLabel": "", "axisPlacement": "auto", "barAlignment": 0, "drawStyle": "line", "fillOpacity": 10, "gradientMode": "none", "hideFrom": { "legend": false, "tooltip": false, "viz": false }, "lineInterpolation": "smooth", "lineWidth": 2, "pointSize": 5, "scaleDistribution": { "type": "linear" }, "showPoints": "never", "spanNulls": false, "stacking": { "group": "A", "mode": "none" }, "thresholdsStyle": { "mode": "off" } }, "mappings": [], "thresholds": { "mode": "absolute", "steps": [ { "color": "green", "value": null }, { "color": "red", "value": 80 } ] }, "unit": "short" }, "overrides": [] }, "gridPos": { "h": 8, "w": 12, "x": 0, "y": 0 }, "options": { "legend": { "calcs": [], "displayMode": "list", "placement": "bottom", "showLegend": true }, "tooltip": { "mode": "single", "sort": "none" } }, "targets": [ { "alias": "Call Volume", "bucketAggs": [ { "field": "timestamp", "id": "2", "settings": { "interval": "auto", "min_doc_count": 0, "trimEdges": 0 }, "type": "date_histogram" } ], "metrics": [ { "id": "1", "type": "count" } ], "query": "", "refId": "A", "timeField": "timestamp" } ], "title": "Call Volume Trend", "type": "timeseries" } // Additional panel configurations... ], "refresh": "10s", "schemaVersion": 38, "style": "dark", "tags": ["call-center", "ai-metrics"], "templating": {...}, "time": {...}, "timepicker": {...}, "timezone": "browser", "title": "AI Call Center Operations", "uid": "ai_call_center", "version": 1, "weekStart": "" }

// Alert Manager configuration for anomaly detection apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: call-center-alert-rules namespace: monitoring spec: groups: - name: call-center.rules rules: - alert: HighEscalationRate expr: rate(call_center_escalations_total[15m]) / rate(call_center_calls_total[15m]) > 0.25 for: 10m labels: severity: warning annotations: summary: "High escalation rate detected" description: "Escalation rate is above 25% in the last 15 minutes" - alert: NegativeSentimentSpike expr: avg_over_time(call_center_sentiment_score[15m]) < 0.3 and avg_over_time(call_center_sentiment_score[1h] offset 1h) > 0.5 for: 5m labels: severity: critical annotations: summary: "Negative sentiment spike detected" description: "Average sentiment score dropped significantly in the last 15 minutes"

// Conversation evaluation schema const evaluationCriteria = { accuracy: { weight: 0.30, metrics: [ { name: "factual_correctness", weight: 0.5 }, { name: "relevance", weight: 0.3 }, { name: "completeness", weight: 0.2 } ] }, effectiveness: { weight: 0.25, metrics: [ { name: "problem_resolution", weight: 0.6 }, { name: "efficiency", weight: 0.4 } ] }, customer_experience: { weight: 0.30, metrics: [ { name: "tone_appropriateness", weight: 0.3 }, { name: "empathy", weight: 0.3 }, { name: "clarity", weight: 0.4 } ] }, compliance: { weight: 0.15, metrics: [ { name: "policy_adherence", weight: 0.5 }, { name: "disclosure_compliance", weight: 0.5 } ] } }; function calculateOverallScore(evaluations) { let overallScore = 0; for (const [category, categoryData] of Object.entries(evaluationCriteria)) { let categoryScore = 0; for (const metric of categoryData.metrics) { categoryScore += evaluations[metric.name] * metric.weight; } overallScore += categoryScore * categoryData.weight; } return overallScore; }