Email Deliverability Monitoring with AI

The Limitations of Traditional Deliverability Monitoring

Most deliverability monitoring is reactive. You check your bounce rate after a campaign sends. You look at Gmail Postmaster Tools a day or two later. You notice your open rates dropped last week. By the time you detect a deliverability problem through traditional metrics, the damage has already been done. Your sender reputation has already declined, and recovery takes days or weeks of careful remediation.

The fundamental issue is that traditional metrics are lagging indicators. Bounce rates tell you about emails that already failed. Open rates (already unreliable due to privacy proxies) tell you about engagement that already happened or did not happen. Spam complaint rates arrive with a delay. None of these metrics give you early warning before a problem impacts your subscribers.

How AI Changes Deliverability Monitoring

Machine learning models can detect deliverability anomalies earlier than traditional threshold-based alerting because they analyze patterns across multiple signals simultaneously. A human looking at dashboards might notice that bounce rates ticked up slightly, but a trained model can detect that the combination of a slight bounce rate increase, a shift in delivery timing patterns to Gmail, and a change in the ratio of soft-to-hard bounces collectively indicate an emerging reputation problem with 90% confidence.

The key ML approaches used in deliverability monitoring include: anomaly detection (identifying when metrics deviate from expected patterns), classification (categorizing the type and severity of deliverability issues), and time-series forecasting (predicting future deliverability based on current trends). Let us look at how each applies in practice.

// Simple anomaly detection for deliverability metrics
interface DeliverabilitySnapshot {
  timestamp: string;
  domain: string;
  deliveryRate: number;
  bounceRate: number;
  complaintRate: number;
  avgDeliveryTime: number;  // seconds
  inboxPlacement: number;   // estimated
}

class DeliverabilityMonitor {
  private history: DeliverabilitySnapshot[] = [];
  private readonly windowSize = 30; // days

  addSnapshot(snapshot: DeliverabilitySnapshot): void {
    this.history.push(snapshot);
  }

  detectAnomalies(current: DeliverabilitySnapshot): Anomaly[] {
    const anomalies: Anomaly[] = [];
    const recent = this.history.slice(-this.windowSize);

    // Check each metric against rolling statistics
    const metrics: (keyof DeliverabilitySnapshot)[] = [
      "deliveryRate", "bounceRate", "complaintRate", "avgDeliveryTime"
    ];

    for (const metric of metrics) {
      const values = recent.map(s => s[metric] as number);
      const mean = values.reduce((a, b) => a + b, 0) / values.length;
      const std = Math.sqrt(
        values.reduce((sum, v) => sum + (v - mean) ** 2, 0) / values.length
      );
      const zScore = (current[metric] as number - mean) / (std || 1);

      if (Math.abs(zScore) > 2) {
        anomalies.push({
          metric,
          currentValue: current[metric] as number,
          expectedRange: { low: mean - 2 * std, high: mean + 2 * std },
          zScore,
          severity: Math.abs(zScore) > 3 ? "critical" : "warning"
        });
      }
    }

    return anomalies;
  }
}

Diagnosing Root Causes Automatically

Detecting that something is wrong is only half the challenge. Diagnosing why is where AI delivers the most value. A deliverability problem could stem from dozens of root causes: a blacklisting, DNS misconfiguration, content triggering spam filters, sending volume spike, list quality degradation, or authentication failure. Traditional monitoring tells you the symptom. AI-powered diagnosis points to the cause.

The diagnostic approach works by correlating multiple signals. If bounce rates increase specifically for Microsoft domains while Gmail delivery remains normal, the system can narrow the diagnosis to Microsoft-specific issues (likely content filtering or IP reputation with Microsoft\'s SmartScreen). If delivery times increase across all domains simultaneously, it suggests an infrastructure issue rather than a reputation issue. If complaint rates spike after a specific campaign, the content itself is likely the problem.

// AI-powered root cause diagnosis
interface DiagnosticResult {
  issue: string;
  confidence: number;
  evidence: string[];
  remediation: string[];
}

async function diagnoseDeliverabilityIssue(
  anomalies: Anomaly[],
  recentEvents: EmailEvent[],
  dnsRecords: DNSRecords
): Promise<DiagnosticResult[]> {
  const diagnoses: DiagnosticResult[] = [];

  // Check authentication first
  if (!dnsRecords.spf.valid || !dnsRecords.dkim.valid) {
    diagnoses.push({
      issue: "Email authentication failure",
      confidence: 0.95,
      evidence: [
        !dnsRecords.spf.valid ? "SPF record invalid or missing" : "",
        !dnsRecords.dkim.valid ? "DKIM signature verification failing" : ""
      ].filter(Boolean),
      remediation: [
        "Verify SPF record includes your sending IPs",
        "Rotate DKIM keys and update DNS records",
        "Check DMARC policy alignment"
      ]
    });
  }

  // Analyze domain-specific bounce patterns
  const bouncesByDomain = groupBy(recentEvents.filter(
    e => e.type === "bounced"
  ), e => extractDomain(e.email));

  for (const [domain, bounces] of Object.entries(bouncesByDomain)) {
    const bounceRate = bounces.length / recentEvents.filter(
      e => extractDomain(e.email) === domain
    ).length;

    if (bounceRate > 0.05) {
      diagnoses.push({
        issue: `Elevated bounce rate for ${domain}`,
        confidence: 0.85,
        evidence: [
          `${(bounceRate * 100).toFixed(1)}% bounce rate (threshold: 5%)`,
          `${bounces.length} bounces in the last 24 hours`
        ],
        remediation: [
          `Check if your IP is listed on ${domain}\'s blocklist`,
          "Review recent content for spam trigger words",
          "Reduce sending volume to this domain temporarily"
        ]
      });
    }
  }

  return diagnoses;
}

Brew\'s Self-Improving Deliverability AI

Brew has built what they call a "self-improving deliverability engine" that continuously learns from sending patterns across their entire platform. When the system detects a deliverability anomaly for one sender, it cross-references the pattern against data from all senders to determine if it is an isolated issue or a broader trend (like a Gmail policy change). This collective intelligence approach means Brew can often detect and diagnose issues faster than single-sender monitoring tools.

The practical benefit is that Brew\'s system generates actionable alerts like "Your delivery rate to Outlook.com dropped 8% in the last 6 hours. Based on similar patterns, this is likely caused by your increased sending volume this week. Recommendation: reduce Outlook volume by 30% for the next 48 hours and warm back up gradually." This level of specific guidance is what separates AI-powered monitoring from simple threshold alerts.

Predictive Deliverability Scoring

The most forward-looking application of AI in deliverability is predictive scoring: estimating the likelihood of inbox placement before you send. This uses a combination of sender reputation signals, content analysis, authentication status, and historical patterns to assign a deliverability score to each planned campaign.

// Predictive deliverability scoring
interface DeliverabilityScore {
  overall: number;          // 0-100
  byProvider: {
    gmail: number;
    outlook: number;
    yahoo: number;
    apple: number;
  };
  risks: Risk[];
  recommendations: string[];
}

// Using Brew\'s pre-send analysis
const score = await brew.deliverability.analyze({
  from: "updates@yourapp.com",
  subject: "Your weekly digest",
  html: emailContent,
  estimatedVolume: 50000,
  targetAudience: {
    listAge: "6-months",
    avgEngagementRate: 0.18
  }
});

console.log(score);
// {
//   overall: 87,
//   byProvider: { gmail: 92, outlook: 83, yahoo: 88, apple: 91 },
//   risks: [
//     { factor: "outlook-content", severity: "medium",
//       detail: "Subject line contains promotional language" }
//   ],
//   recommendations: [
//     "Consider a more neutral subject line for Outlook recipients",
//     "Segment Outlook recipients and A/B test subject variants"
//   ]
// }

Postmark offers a similar pre-send content analysis, though it focuses on spam filter detection rather than comprehensive deliverability prediction. Their SpamAssassin-based checker identifies specific content patterns that trigger filters. While less sophisticated than ML-based approaches, it catches the most common content-related deliverability issues.

Implementing Continuous Monitoring

To build an effective AI-powered monitoring system, you need three components: data collection, model inference, and alerting. Data collection involves ingesting webhook events from your ESP, DNS monitoring results, and blacklist check results on a regular cadence. Model inference runs your anomaly detection and diagnostic models against the latest data. Alerting routes findings to the right people through the right channels.

// Continuous monitoring pipeline
import cron from "node-cron";

// Run every 15 minutes
cron.schedule("*/15 * * * *", async () => {
  // 1. Collect current metrics
  const snapshot = await collectDeliverabilityMetrics();

  // 2. Run anomaly detection
  const monitor = new DeliverabilityMonitor();
  await monitor.loadHistory(); // Load last 30 days
  const anomalies = monitor.detectAnomalies(snapshot);

  // 3. If anomalies detected, run diagnostics
  if (anomalies.length > 0) {
    const recentEvents = await getRecentEvents("24h");
    const dnsRecords = await checkDNS(snapshot.domain);
    const diagnoses = await diagnoseDeliverabilityIssue(
      anomalies, recentEvents, dnsRecords
    );

    // 4. Alert based on severity
    for (const diagnosis of diagnoses) {
      if (diagnosis.confidence > 0.7) {
        await alerting.send({
          channel: diagnosis.confidence > 0.9 ? "pagerduty" : "slack",
          title: diagnosis.issue,
          body: [
            "Evidence:",
            ...diagnosis.evidence.map(e => `  - ${e}`),
            "Recommended actions:",
            ...diagnosis.remediation.map(r => `  - ${r}`)
          ].join("\n")
        });
      }
    }
  }

  // 5. Store snapshot for future analysis
  await storeSnapshot(snapshot);
});

Practical Recommendations

If you are starting from scratch, begin with simple threshold-based monitoring and add ML sophistication over time. Monitor delivery rate, bounce rate, and complaint rate with fixed thresholds. Add domain-level breakdowns to catch provider-specific issues. Then layer in statistical anomaly detection using z-scores against rolling averages. Only invest in predictive scoring and automated diagnostics once you have enough historical data (at least 3 months of daily metrics) to train reliable models.

For teams that do not want to build monitoring from scratch, Brew includes AI-powered deliverability monitoring out of the box. Postmark provides excellent transparency with their delivery stats and bounce tracking. Google Postmaster Tools and Microsoft SNDS are free and essential regardless of which ESP you use. The combination of ESP-native monitoring and mailbox provider tools gives you comprehensive coverage without building a custom system.