Case Study: SupportGen — Reducing Ticket Backlogs by 62% Using RAG-Based Support Agents
Overview
SupportGen, a major B2B SaaS provider, struggled with a 48-hour ticket backlog, rising user churn, and an overwhelmed support team.
They needed a scalable support automation solution — without risking hallucinations or compliance issues.
Logicwerk implemented secure, enterprise-grade RAG support agents integrated into Zendesk and Jira.
Challenges
- 48-hour backlog for Tier-1 issues
- Manual escalation of simple repeat queries
- Fragmented internal knowledge bases
- Inconsistent ticket responses
- Difficulty training new agents
Solution: AI Support Agents + Knowledge Retrieval (RAG)
1. Unified Knowledge Index
We built a single structured index using:
- Confluence
- Zendesk macros
- API docs
- Internal troubleshooting guides
2. RAG Agent with Guardrails
Capabilities included:
- Understanding tickets
- Pulling verified answers
- Responding with citations
- Identifying high-risk cases for human review
3. Workflow Integration
- Auto-drafted responses for agents
- Auto-tagging + routing
- Escalation prediction
- Resolution summarization
4. SOC2 & ISO-42001 Controls
Every answer logged.
Every agent action auditable.
Results
🚀 62% faster time-to-resolution (TTR)
Instant answers for Tier-1 issues.
📉 37% fewer escalations
Better auto-classification + routing.
🧠 68% faster onboarding of new support reps
AI-generated recommended resolutions.
💵 $1.2M in annual savings
Reduction in support toil + churn.
Final Outcome
SupportGen transformed support operations into a hybrid model — where AI handles repetitive and data retrieval work, and humans focus on empathy, complex issues, and escalations.
Work With Logicwerk
We build:
- RAG support agents
- Multi-agent support systems
- Secure enterprise knowledge graphs