All Technologies Used
Motivation
The client’s support team handled 8,000+ tickets per month, up 40% YoY, without insight into ticket drivers or AI opportunities. Response times were slipping, CSAT was 61% and declining, and adding headcount was becoming unsustainable. Existing tools like Zendesk and Confluence were underutilized. Azati conducted a two-week As-Is assessment to uncover the real issues, shaping the solution that followed.
Main Challenges
What looked like a staffing problem was actually process inefficiency: 60% of incoming tickets were repetitive L1 queries answerable from existing documentation. Agents spent most of their time on non-judgment work, retrieving and formattin, rather than tasks requiring expertise. The solution wasn’t more people; it was removing the right work from their queue.
The client's Confluence instance held 2,400 articles, but agents rarely referenced it. Content was inconsistently structured, partially outdated, and not organized for retrieval. Direct LLM integration without restructuring produced hallucinations at an unacceptable rate. Before any AI could work reliably, the knowledge foundation had to be rebuilt.
There were no defined criteria for what constituted an L1 versus L2 ticket. Senior agents were pulled into simple queries. Critical enterprise issues sometimes sat in the general queue. Zendesk reports showed volume and CSAT, but nothing about which categories drove the most cost, which had the highest re-open rate, or where knowledge gaps were causing errors.
The client had no dedicated AI engineering team. Any system deployed without ongoing management would degrade within weeks as the product evolved, new features, pricing changes, updated policies. A one-time implementation was not an option. Managed operations had to be the model from day one.
Our Approach
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Knowledge Base Restructuring and Embedding
- Full Confluence content audit and gap identification
- Article restructuring and metadata tagging
- Vector embedding optimized for RAG retrieval
- Automated sync pipeline for ongoing content updates
AI Triage, Routing, and Response Generation
- Intent classification and priority scoring
- Confidence-tiered routing across three escalation levels
- RAG-based response generation with source citation
- Agent feedback loop for continuous quality improvement
Zendesk Integration
- Native Zendesk API integration
- Automated ticket tagging and status updates
- AI action logging per ticket
- Reporting via existing Zendesk dashboards
Managed AI Operations
- Weekly hallucination sampling and correction pipeline
- Monthly performance and cost reporting
- Ongoing Confluence knowledge updates
- Quarterly automation expansion
Business Value
Reduced Agent Workload: 62% of L1 tickets resolved without agent involvement, freeing the team to handle a 35% volume increase without adding headcount.
Faster Responses: First response time on automated tickets dropped to under 3 minutes. Agent handling time on assisted tickets reduced 44% through AI-generated context and suggested replies.
Higher Customer Satisfaction: CSAT improved 18 points within the first 90 days, driven by response consistency and dramatically faster resolution on common queries.
Cost Control: AI compute costs running 40% below initial projections through active model routing and caching management maintained by Azati.
Scalable Automation Growth: Automation coverage grew from 12 ticket categories at launch to 31 categories by month nine, without additional implementation budget. Every month of managed operations expanded the ROI.
