Case Study: How Company X Increased Efficiency with AI Agents

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

Discovery & Planning
- Map out ticket categories and existing response templates.
- Identify high‑volume query patterns.
Agent Development
- Build a custom prompt to guide the AI in triage and auto‑response.
- Train on historical ticket data for contextual accuracy.
Integration
- Connect the AI agent to the company’s ticketing platform via StitchGrid’s MCP transport.
- Set up webhook callbacks for escalations.
Testing & Fine‑Tuning
- Run a pilot on 10 % of tickets.
- Adjust thresholds and fallback rules.
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

Start small: Pilot on a subset to validate accuracy before scaling.
Continuous learning: Regularly update the knowledge base and retrain the model.
Human‑in‑the‑loop: Keep escalation paths clear; agents should feel supported, not replaced.
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 or schedule a demo today.