Company Profile
Company Type
Online learning platform
Team Size
100-500 employees
Industry
Education
Key Challenge
Struggling with inefficient manual follow-up processes that were slowing growth and increasing operational costs. Their primary concern was student engagement.
Tools Connected
The Challenge
Manual follow-up was the biggest bottleneck in this online learning platform's operations. Their team of 100-500 employees processed hundreds of follow-up requests weekly, each requiring multiple steps, cross-referencing against education-specific requirements, and coordination between departments. The average follow-up request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their follow-up output was inconsistent. Different team members followed different procedures, and there was no standardized way to handle edge cases that are common in education. A recent audit revealed that 12% of completed follow-up records contained errors that required rework — costing the organization an additional $50K annually in correction and remediation efforts. The leadership team recognized that continuing to throw people at the problem wasn't viable and began searching for an AI-powered solution.
The Solution
Arahi AI provided the automation backbone this education team needed. They deployed a multi-agent workflow that breaks the follow-up process into discrete, automated steps — each handled by a specialized AI agent. The first agent monitors triggers from Canvas and Slack. The second agent analyzes and processes incoming requests using education-specific business logic. The third agent executes actions across connected tools and notifies team members via Notion.
The beauty of the no-code approach was speed of implementation. The team had their first agent live within 90 minutes, and the full follow-up workflow was operational within a single afternoon. They used Arahi AI's template for education follow-up as a starting point, customized the business rules to match their specific process, and connected their existing tool stack without writing a single line of code. Within the first week, the agents had processed over 200 follow-up instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key education follow-up metrics.
Task Completion Time
97% faster
Before
2-3 hours average
After
< 5 minutes
Team Productivity
250% increase
Before
Baseline
After
3.5x output
Quality Score
26% improvement
Before
78% accuracy
After
98.5% accuracy
Monthly Cost
85% savings
Before
$8,200/month
After
$1,200/month
Customer Satisfaction
35% increase
Before
3.4/5
After
4.6/5
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our education follow-up workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.”
Director of Business Operations
Online learning platform
Key Takeaways
The most important lessons from this education follow-up automation project.
Automating follow-up in education delivered immediate, measurable results: faster processing, higher accuracy, and lower costs.
The key to success was connecting existing education tools to AI agents rather than replacing the entire tech stack.
24/7 automated processing eliminated backlogs and ensured consistent service quality regardless of volume fluctuations.
Starting with a pre-built template and customizing for education-specific requirements dramatically reduced time-to-value.
Implementation Timeline
From zero to production in 90 minutes — here's how they did it.
Step 1: Mapped the existing follow-up workflow
Documented every step of the current manual follow-up process, including decision points, exceptions, and handoffs between team members. Identified which steps could be fully automated versus those needing human oversight.
Step 2: Built the automation in Arahi AI
Used Arahi AI's no-code builder to create the follow-up workflow: connected Canvas and Google Classroom as data sources, configured AI decision logic for education-specific requirements, and set up automated actions and notifications.
Step 3: Parallel run with manual process
Ran the AI agents alongside the manual process for one week to compare outputs. The AI matched or exceeded human accuracy on 98% of follow-up instances, with the 2% of edge cases automatically flagged for human review.
Setup Time
90 minutes
AI Agents
3 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating follow-up in education.
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