Here’s the full text of the blog post **“Case Study: How Company X Increased Efficiency with AI Agents.”** --- ## Case Study: How Company X Increased Efficiency with AI Agents **Published:** 15 Jan 2025 **Author:** Vaibhav Solanki **Read Time:** 4 min --- ### The Challenge Company X, a Fortune 500 enterprise, was overwhelmed with support tickets. - **High volume:** Thousands of tickets per month. - **Long response times:** Average first‑reply time of 48 hrs. - **Low customer satisfaction:** NPS dropped from 70 to 45. The support team was stretched thin, handling repetitive queries manually and struggling to triage urgent issues. --- ### The Solution Company X partnered with StitchGrid to deploy a **triaging AI agent** that could: - **Identify** the nature of each incoming ticket (billing, technical, account, etc.). - **Auto‑respond** to 40 % of common queries instantly. - **Escalate** complex cases to human agents with contextual notes. **Key components:** | Component | Role | |-----------|------| | **AI Agent (ChatGPT‑4)** | Natural‑language understanding and response generation. | | **StitchGrid’s Agent Framework** | Orchestration, workflow management, and integration with existing ticketing systems. | | **Knowledge Base Sync** | Continuous updates from the company’s internal documentation. | | **Analytics Dashboard** | Real‑time metrics on agent performance and ticket volume. | --- ### Implementation Steps 1. **Discovery & Planning** - Map out ticket categories and existing response templates. - Identify high‑volume query patterns. 2. **Agent Development** - Build a custom prompt to guide the AI in triage and auto‑response. - Train on historical ticket data for contextual accuracy. 3. **Integration** - Connect the AI agent to the company’s ticketing platform via StitchGrid’s MCP transport. - Set up webhook callbacks for escalations. 4. **Testing & Fine‑Tuning** - Run a pilot on 10 % of tickets. - Adjust thresholds and fallback rules. 5. **Rollout & Monitoring** - Full deployment with real‑time monitoring dashboards. - Weekly reviews and model retraining as needed. --- ### Results | Metric | Before | After | |--------|--------|-------| | **First‑reply time** | 48 hrs | 4 hrs | | **Ticket resolution time** | 72 hrs | 30 hrs | | **Support agent workload** | 1 ticket / agent / day | 0.6 ticket / agent / day | | **Customer NPS** | 45 | 68 | | **Cost savings** | $350k / year | $210k / year | *The AI agent handled 40 % of tickets autonomously, freeing human agents to focus on complex issues.* --- ### Lessons Learned 1. **Start small:** Pilot on a subset to validate accuracy before scaling. 2. **Continuous learning:** Regularly update the knowledge base and retrain the model. 3. **Human‑in‑the‑loop:** Keep escalation paths clear; agents should feel supported, not replaced. 4. **Transparency:** Share success metrics with stakeholders to maintain trust. --- ### Takeaway By leveraging StitchGrid’s AI agent framework, Company X transformed its support operations—reducing response times, boosting customer satisfaction, and cutting costs—all while maintaining high-quality service. The same approach can be adapted to any industry where repetitive, high‑volume queries dominate. --- **Want to see how AI agents can work for your business?** [Contact us](https://stitchgrid.in/contact) or schedule a demo today.

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