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 high accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key education follow-up metrics.
Task Completion Time
Dramatically faster
Before
Hours
After
Minutes
Team Productivity
Significant increase
Before
Baseline
After
Multiplied output
Quality Score
Notable improvement
Before
Inconsistent
After
Consistently high
Monthly Cost
Major savings
Before
High
After
Much lower
Customer Satisfaction
Notable increase
Before
Below target
After
Above target
“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 the vast majority of follow-up instances, with 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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