How We Transformed Support for a SaaS Company

A B2B SaaS company with 2,000+ customers was drowning in support tickets. Their 5-person support team was handling 800+ tickets monthly, leading to:

• Average first response time of 8+ hours • Customer satisfaction (CSAT) at 72% • Support team turnover due to burnout • Sales team spending time on support instead of selling • Night and weekend coverage gaps

They needed to scale support without proportionally scaling headcount.

We implemented a tiered AI agent system:

Tier 1: Instant Response Agent • Acknowledged every ticket within 2 minutes • Classified tickets by type and urgency • Resolved common issues (password resets, billing questions, how-to guides) • Gathered necessary information for complex issues

Tier 2: Investigation Agent • Pulled relevant customer data from CRM and product database • Checked for known issues matching the description • Prepared context summary for human agents

Tier 3: Human Support • Received pre-researched tickets with full context • Focused only on complex issues requiring judgment • Handled escalations and relationship management

After 90 days:

• First response time: 8 hours → 2 minutes (96% reduction) • Ticket resolution by AI: 60% handled without human involvement • CSAT score: 72% → 89% (+17 points) • Support team size: 5 → 4 (1 rep moved to Customer Success) • Cost per ticket: Reduced by 45% • 24/7 coverage: Achieved without night shifts

The support team now handles only complex issues, leading to higher job satisfaction and zero turnover in the following year.

Week 1-2: Discovery • Analyzed 6 months of ticket data • Identified top 20 ticket categories • Mapped current workflows and tools

Week 3-4: Design • Created response templates for common issues • Designed escalation logic • Planned integrations with Zendesk, CRM, and product DB

Week 5-8: Build & Test • Built AI agents with human oversight • Tested with shadow deployment (AI suggested, humans approved) • Refined based on feedback

Week 9-12: Gradual Rollout • Started with 20% of tickets • Scaled to 100% over 4 weeks • Continuous monitoring and tuning

What worked well: • Starting with shadow mode built trust • Focusing on response time had immediate impact • Pre-populating context made human agents more efficient

What we'd do differently: • Invest more upfront in edge case handling • Set up better feedback loops earlier • Communicate changes to customers proactively

Keys to success: • Executive sponsorship and clear success metrics • Support team involvement in design (they knew the pain points) • Iterative approach with quick wins building momentum

This wasn't just about handling more tickets—it transformed how the company thinks about support:

• Support became a competitive advantage, not a cost center • The freed-up rep moved to proactive Customer Success, reducing churn • Data from AI interactions revealed product issues, improving the roadmap • The team now focuses on relationship-building, not ticket-clearing

One customer commented: 'I used to dread contacting support. Now I get answers in minutes. It's actually impressive.'