How a client drowning in support tickets transformed customer interactions with AI

How a client drowning in support tickets transformed customer interactions with AI
A company offering small-ticket, self-serve SaaS was drowning in support requests, with ACVs that made traditional solutions uneconomical. Retention was slipping and even engineers were jumping in to help. We implemented new AI workflows that quickly owned 85% of support volume, slashed response times, and improved NRR to over 90%.
The Problem
A B2B SaaS provider had built an efficient self-serve acquisition model, but customer support became a massive pain point as they scaled. With sub-$150 ACV, they couldn't afford a large support team, and their lean crew (even engineers) was drowning in tickets. Revenue retention and satisfaction were slipping, and the unit economics demanded higher-leverage solutions.
The Engagement
Leverage AI to address support volume, improve customer satisfaction and retention, and unlock revenue expansion, all without adding headcount.
What We Did
Identifying and Scoping Use Cases
Analyzed support tickets and customer journey to identify high-volume queries and bottlenecks, then evaluated AI tools to understand how customer data could power better interactions
Identified opportunities for AI to support customer acquisition and expansion, scoping clear use cases across the full lifecycle
Building and Testing the Infrastructure
Architected and built new customer, product, and platform data assets to support AI agents, then integrated the AI into systems to enable troubleshooting and resolution
Configured AI to handle common queries and execute actions programmatically (password resets, plan changes, etc.) with preemptive human approvals.
Developed SQL-based analytical processes to identify cross-sell and upsell opportunities based on usage signals, then created automated campaigns that engaged customers with relevant offers via email and SMS.
Extended AI upstream to engage prospects during the buying process, answer pre-sales questions, and assist with onboarding.
Success Tracking and Management
Built dashboard tracking AI performance (resolution rate, satisfaction, escalations) and revenue impact.
Trained team on managing escalations and refining the knowledge base based on gaps surfaced in customer conversations.
The Impact
AI handled 85% of support volume with average response time dropping from 5+ hours to under 4 minutes, freeing the team for complex issues and high-value accounts.
Generated $120K in incremental upsell revenue in first 90 days
Net revenue retention improved from under 80% to over 90%
Bottom Line
The team turned a support bottleneck into a competitive advantage. By thoughtfully leveraging AI, we drove new revenue, improved customer experience, and created a scalable foundation for growth without ballooning headcount.