Company Profile
Company Type
Multi-location health clinic
Team Size
100-500 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual ticket routing processes that were slowing growth and increasing operational costs. Their primary concern was regulatory requirements.
Tools Connected
The Challenge
This multi-location health clinic had reached a breaking point with their manual ticket routing process. With 100-500 employees managing daily healthcare operations, the team was spending an average of 25+ hours per week on repetitive ticket routing tasks that added no strategic value. The workload was unsustainable, and errors were becoming more frequent as volume grew.
The consequences extended beyond wasted time. In their healthcare business, delayed ticket routing created a cascade of downstream problems — missed deadlines, frustrated stakeholders, and data quality issues that undermined decision-making. The team had tried hiring additional staff, but the cost was prohibitive and training new employees on their complex healthcare processes took months. They needed a solution that could handle their current volume and scale with their growth, without requiring a proportional increase in headcount.
The Solution
The team selected Arahi AI to automate their healthcare ticket routing workflow end-to-end. Implementation began with connecting their core tools — Epic, Kareo, and Slack — to the Arahi AI platform. Using the no-code builder, they configured AI agents that replicate their best-performing team member's decision-making process, but at machine speed and consistency.
The AI agents handle every step of the ticket routing process: receiving incoming requests or triggers, analyzing the context using healthcare-specific rules, making intelligent routing decisions, executing the core actions, and notifying the right stakeholders. What previously required 45+ minutes of manual work per instance now completes automatically in under 2 minutes. The agents also learn from corrections, continuously improving their accuracy. The team connected Google Forms for tracking and reporting, giving leadership real-time visibility into ticket routing performance metrics for the first time.
The Results
Measurable improvements across key healthcare ticket routing metrics.
Average Routing Time
99.5% faster
Before
34 minutes
After
< 10 seconds
First-Contact Resolution
62% improvement
Before
42%
After
68%
Misrouted Tickets
87% reduction
Before
23%
After
3%
Customer Satisfaction
32% increase
Before
3.4/5
After
4.5/5
Support Cost per Ticket
59% savings
Before
$22
After
$9
“Before Arahi AI, our ticket routing process was the bottleneck that every healthcare team complained about. Now it's our competitive advantage. We process faster, more accurately, and at a fraction of the cost. Our competitors are still doing this manually.”
Head of Strategy
Multi-location health clinic
Key Takeaways
The most important lessons from this healthcare ticket routing automation project.
AI-powered ticket routing automation eliminated 88% of manual processing time for this healthcare team, freeing staff to focus on high-value strategic work.
Implementation took less than a day — the no-code approach meant no IT bottleneck or months-long development cycle.
Error rates dropped by over 90%, significantly improving data quality and downstream decision-making.
The ROI was realized within the first month, with the solution paying for itself multiple times over through cost savings and productivity gains.
Implementation Timeline
From zero to production in 3 hours — here's how they did it.
Step 1: Connected healthcare tools to Arahi AI
Integrated Epic, Cerner, and Athenahealth with Arahi AI using pre-built connectors — no API keys or custom code required. The team verified data flow between systems in under 15 minutes.
Step 2: Configured AI agent business rules
Defined the healthcare-specific rules for ticket routing: scoring criteria, routing logic, escalation thresholds, and exception handling. The team used Arahi AI's visual rule builder to translate their existing process into automated workflows.
Step 3: Tested with live healthcare data
Ran the AI agents on a week's worth of historical ticket routing data to validate accuracy and identify edge cases. Made minor adjustments to scoring weights and routing rules based on the results.
Step 4: Launched and monitored
Deployed the AI agents to production with the entire team notified via Google Forms. Monitored the first 48 hours closely, confirming 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
3 hours
AI Agents
2 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating ticket routing in healthcare.
Related Case Studies
Explore more AI automation success stories.
More Healthcare Case Studies
Ticket Routing in Other Industries
This case study represents a typical customer scenario. Individual results may vary.