What if your support team could catch Drupal site issues before your customers even noticed them?
This session tells the story of how we built an internal AI-powered workflow tool — using Claude Code, Drutiny, and a suite of integrations, that transformed how our Technical Account Managers (TAMs) serve large enterprise Drupal customers. Instead of reacting to incidents, TAMs now receive proactive daily audit summaries, AI-generated root-cause analysis, and actionable recommendations delivered directly to Slack and email, all without opening a single dashboard.
We'll walk through:
- The problem: TAMs managing dozens of enterprise Drupal customers, overwhelmed by manual audit runs and reactive support cycles
- The solution: A Node.js/Express automation backend integrating Drutiny, Gmail, Slack, Salesforce, and Claude AI to surface critical issues automatically on a scheduled cadence
- The impact: Real customer retention wins. Cases where automated Drutiny analysis flagged EOL module risks, security advisories, and performance regressions before they became support tickets
- The lessons: What it takes to operationalize Drutiny at scale, how AI-assisted analysis reduces noise, and where human judgment still matters.
This is not a product pitch - it's an honest case study in building with the tools the Drupal ecosystem gives us, using open-source audit infrastructure to make enterprise customer success measurable and repeatable.
Audience: Drupal site owners, agency TAMs, support leads, and anyone curious about AI-assisted site health monitoring.
Session Outline (45 min)
0–5 min — The problem: reactive TAM workflows at scale
5–15 min — How Drutiny works: a quick primer for the audience
15–25 min — The tool: architecture, what Claude AI adds, live demo
25–35 min — Customer success stories: 3 real cases (anonymized)
35–42 min — Lessons learned, what didn't work, what we'd do differently
42–45 min — Q&A
Learning Outcomes
Attendees will leave able to:
1. Explain how Drutiny can be integrated into automated workflows beyond one-off CLI runs
2. Describe how AI (Claude) adds interpretation layer on top of raw audit data
3. Identify 3 classes of Drupal site issues most commonly caught proactively vs. reactively
4. Apply the scheduling + alerting pattern to their own team's customer success workflow
Why This Fits "Success Stories & Innovation"
- Project challenge: Scaling TAM attention across 10+ enterprise Drupal customers per TAM
- Team collaboration: Cross-functional (support, engineering, TAMs)
- Methodology: Shift-left site health, audit-driven proactive engagement
- Role of Drupal: Drutiny is a Drupal-native audit framework; this story only exists because of the Drupal ecosystem
- Community takeaway: A replicable pattern any Drupal agency or hosting team can adopt