AI Support | SaaS Automation

Back to Case Studies

SaaS & Technology

AI Customer Support Assistant & Ticket Routing

Reducing support load while improving response quality through AI-powered ticket classification and drafting.

SaaS automation case study featuring ai implementation and workflow automation.

50%

Workload Reduction

3× faster

Response Time

100%

Ticket Accuracy

Overview

A SaaS company's support team was overwhelmed with repetitive tickets, long response times, and inconsistent routing. The AI-powered solution reduced workload by 50% while improving response quality.

Business Context

The support team was struggling under an ever-growing ticket queue as the customer base expanded. Response times had crept up to 24+ hours for non-urgent issues, and customer satisfaction scores were declining. Analysis revealed that over 60% of tickets were variations of the same common questions, yet each required individual agent attention. Routing was inconsistent — technical issues sometimes sat in the general queue for hours before reaching the right specialist. Agent burnout was becoming a serious concern, with high turnover increasing training costs and losing institutional knowledge. Customer complaints about support quality were increasing, and the NPS scores were trending downward. The company had considered hiring additional agents, but the cost would significantly impact margins and still wouldn't solve the underlying efficiency problems. The company needed to dramatically improve efficiency without sacrificing the quality and personal touch that customers valued. ZapWizards was engaged to implement an AI-powered support transformation that would scale with growth while maintaining customer relationships.

How We Built It

We built a comprehensive AI support platform integrating with the company's existing Zendesk ticketing system through Make.com workflows. The classification engine uses OpenAI to analyze incoming tickets and categorize them by issue type, urgency level, and required expertise, enabling intelligent routing to the appropriate support tier immediately upon submission. For common question categories, the AI assistant generates high-quality draft responses drawing from the company's knowledge base and previous successful resolutions, reducing agent work to review-and-send rather than compose-from-scratch. The system identifies tickets requiring engineering escalation based on technical indicators in the description, automatically creating linked issues in the development tracker with relevant context. Automated SLA tracking monitors response and resolution times, triggering escalations when deadlines approach and providing real-time visibility into team performance. A custom Retool dashboard surfaces daily metrics including ticket volume by category, response times, resolution rates, and individual agent productivity. PostgreSQL stores all ticket data and AI interactions for analysis and continuous improvement. The AI continuously learns from agent edits to its drafts, improving response quality over time with each correction. Sentiment analysis identifies frustrated customers for priority handling to prevent escalation.

Challenges

Long response times

Repetitive questions

Support team burnout

No classification or routing logic

What We Delivered

AI assistant that drafts high-quality replies

Ticket classification model → assigns to correct support tier

Automated SLA tracking

Daily reporting dashboards

Routing to engineering for technical issues

Tech Stack

OpenAI, Make.com, Zendesk/Intercom integration, Retool, PostgreSQL

Results

50%

Workload Reduction

3× faster

Response Time

100%

Ticket Accuracy

Strategic Impact

The 50% workload reduction transformed the support team from overwhelmed to effective, with agents now handling higher volumes while experiencing less stress and greater job satisfaction. Response times improved 3x, with most tickets receiving initial response within hours rather than days, dramatically improving customer satisfaction scores. The 100% ticket accuracy routing means issues reach the right specialist immediately, eliminating the frustrating handoffs that previously delayed resolution and irritated customers. Agent satisfaction improved significantly as repetitive, low-complexity tickets are now handled semi-automatically, allowing agents to focus on interesting, challenging problems that utilize their expertise. Agent turnover decreased as job satisfaction improved, preserving institutional knowledge and reducing training costs significantly. The knowledge captured in AI responses ensures consistency across the team — customers receive accurate, helpful answers regardless of which agent handles their ticket. Engineering escalation efficiency improved as technical issues are identified and routed faster with better context, reducing the back-and-forth that previously delayed bug fixes. NPS scores have improved substantially as customers receive faster, more consistent support experiences. The scalability of the AI-augmented approach means the company can grow their customer base 2-3x without proportionally growing support headcount, fundamentally improving unit economics and profitability.