CloudMetrics (name changed) | saas
SaaS Company Deflects 73% of Support Tickets with Custom AI Chatbot
Challenge
CloudMetrics was drowning in support volume. Their team of 12 agents handled over 1,200 tickets per week, with an average first response time of 18 hours. Annual support spend had hit $420K and climbing. Customer churn data showed that 28% of cancellations cited "slow support" as a primary factor. The deeper problem was ticket composition. An audit of six months of data revealed that 65% of all tickets were L1 issues—password resets, billing questions, API key rotation, and onboarding steps already documented in their help center. These repetitive tickets consumed most of the team's bandwidth, leaving complex technical issues sitting in queue for 2-3 days. The support team was burning out. Two senior agents had left in the previous quarter, and the remaining team was stuck in a cycle of triaging routine requests instead of doing the technical troubleshooting they were hired for. Management was considering hiring 4 more agents at $280K total cost, but recognized that throwing headcount at the problem wouldn't fix the underlying inefficiency.
Solution
Results
-73%
Weekly Support Tickets
1,200/week → 324/week
-99.9%
First Response Time
18 hours → 8 seconds
-$180K saved
Annual Support Cost
$420K → $240K
+35%
CSAT Score
3.4/5 → 4.6/5
Support team freed from L1 triage
Agent Focus on Complex Issues
35% of time → 85% of time
Implementation Timeline
Week 1
Discovery & Knowledge Base Audit
Audited 2,400 help articles for accuracy, identified gaps in API documentation, analyzed 6 months of ticket data to map the top 50 question clusters, and defined conversation flow boundaries.
Week 2
RAG Pipeline & Conversation Design
Built the Pinecone vector store with optimized chunking, developed GPT-4o prompt chains for each conversation flow, created the confidence-threshold escalation logic, and wired up Stripe read-only access for billing queries.
Week 2-3
Integration & Testing
Connected Intercom for handoff with context passthrough, built n8n workflows for ticket creation and routing, deployed web chat and in-app widgets, and ran 200 test conversations against real historical tickets.
Week 3
Launch & Monitoring
Rolled out to 10% of traffic, monitored hallucination rates and escalation patterns, tuned confidence thresholds based on real conversations, then opened to 100% with a 48-hour observation window.
Tools & Platforms
“The chatbot handles the routine stuff perfectly—password resets, billing lookups, API key questions—all resolved in seconds. Our support team now focuses on problems that are actually worth solving. The handoff context is the part nobody talks about: when a ticket does reach a human, the agent already knows what the customer tried and what didn't work.”
Sarah Chen
VP of Customer Success
