Company Profile
Company Type
Private university
Team Size
100-500 employees
Industry
Education
Key Challenge
Struggling with inefficient manual lead qualification processes that were slowing growth and increasing operational costs. Their primary concern was student engagement.
Tools Connected
The Challenge
Before implementing Arahi AI, this private university was drowning in unqualified leads. Their sales team of 100-500 employees was spending an average of 4.5 hours per day manually reviewing and scoring incoming leads. With hundreds of new prospects entering their pipeline weekly, the team could not keep up. Hot leads went cold while reps were busy sorting through low-quality inquiries, and there was no consistent scoring framework across the team.
The lack of systematic qualification was costing them real revenue. Their education sales cycle averaged 45 days, but follow-up on qualified leads often didn't happen until 24-48 hours after initial contact — well past the critical response window. They estimated that 30% of their qualified leads were being lost to competitors who simply responded faster. The manual process also meant zero visibility into why leads were or weren't converting, making it impossible to optimize their education marketing spend.
The Solution
After evaluating several options, the team chose Arahi AI to automate their education lead qualification process. The implementation started with connecting their existing tools — Canvas, Slack, and Notion — to Arahi AI's no-code platform. Within two hours, they had an AI agent that could automatically score, enrich, and route every incoming lead based on their specific education ideal customer profile criteria.
The AI agent was configured to evaluate leads across 15+ qualification signals including company size, budget indicators, technology stack, and education-specific buying triggers. Leads scoring above threshold were instantly routed to the appropriate sales rep via Notion with a complete profile, score breakdown, and AI-generated talking points. Below-threshold leads were automatically added to nurture sequences, while disqualified leads were archived with clear reasoning. The team also set up automated follow-up emails that went out within 60 seconds of a lead submitting a form — ensuring they were always first to respond.
The Results
Measurable improvements across key education lead qualification metrics.
Lead Response Time
99% faster
Before
4.5 hours average
After
< 2 minutes
Lead-to-Opportunity Rate
3x improvement
Before
8%
After
24%
Sales Rep Productivity
88% time saved
Before
4.5 hrs/day on qualification
After
< 30 min/day oversight
Qualified Lead Coverage
100% coverage
Before
65% of leads scored
After
100% of leads scored
Cost per Qualified Lead
74% reduction
Before
$47 per lead
After
$12 per lead
“We went from spending half our day on lead qualification to having it just happen automatically. The AI agents handle the routine work perfectly, and our education team can focus on the strategic decisions that actually move the needle. I wish we had done this a year ago.”
VP of Operations
Private university
Key Takeaways
The most important lessons from this education lead qualification automation project.
Automating lead qualification 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 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 lead qualification: 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 lead qualification 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
3 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating lead qualification in education.
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