Company Profile
Company Type
Private university
Team Size
50-300 staff
Industry
Education
Key Challenge
Struggling with inefficient manual chat support processes that were slowing growth and increasing operational costs. Their primary concern was enrollment management.
Tools Connected
The Challenge
This private university had reached a breaking point with their manual chat support process. With 50-300 staff managing daily education operations, the team was spending an average of 25+ hours per week on repetitive chat support 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 education business, delayed chat support 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 education 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 education chat support workflow end-to-end. Implementation began with connecting their core tools — Canvas, Slack, and Notion — 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 chat support process: receiving incoming requests or triggers, analyzing the context using education-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 Gmail for tracking and reporting, giving leadership real-time visibility into chat support performance metrics for the first time.
The Results
Measurable improvements across key education chat support metrics.
Average Response Time
99% faster
Before
8 minutes
After
< 5 seconds
Queries Resolved by AI
New capability
Before
0%
After
72%
Customer Satisfaction
42% increase
Before
3.1/5
After
4.4/5
Support Cost per Interaction
86% savings
Before
$8.50
After
$1.20
After-Hours Coverage
Always on
Before
0% (business hours only)
After
100% 24/7
“The ROI was almost immediate. Within the first month, our chat support throughput increased by over 300% while our error rate dropped to near zero. For a education business of our size, that translates directly to the bottom line. Arahi AI paid for itself in the first week.”
Operations Director
Private university
Key Takeaways
The most important lessons from this education chat support automation project.
This education team proved that chat support automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling chat support capacity by 10x without adding headcount fundamentally changed the economics of their education operations.
Consistent AI-powered processing eliminated the quality variance that came with different team members handling chat support differently.
Real-time visibility into chat support metrics gave leadership the data they needed to make better strategic decisions.
Implementation Timeline
From zero to production in 2 hours — here's how they did it.
Step 1: Connected education tools to Arahi AI
Integrated Canvas, Blackboard, and Google Classroom 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 education-specific rules for chat support: 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 education data
Ran the AI agents on a week's worth of historical chat support 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 Gmail. Monitored the first 48 hours closely, confirming 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
2 hours
AI Agents
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating chat support in education.
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