Executive Summary

Client: StudentTenant.com - UK student accommodation platform serving 3,000+ landlords and 100 private halls

Challenges:

• Previous AI (Intercom's Fin) was limited to basic FAQs

• After-hours lead loss and no lead segmentation

• Slow resolution times and 300% peak season surges

Solution:

• Omnichannel integration (web, WhatsApp, email) with context preservation

• Autonomous 24/7 AI agents with intelligent re-engagement

• One-week implementation across four strategic phases

Results:

• 83% ticket automation (32% fully automated)

• Zero first response time and 67% increase in lead engagement

• 3x faster resolution, 95% lead classification accuracy

• 500+ daily interactions handled during peak season

Expansion:

• Landlord automation, group matching, and enhanced property discovery

About Student Tenant

StudentTenant.com is a leading UK student accommodation platform serving over 3,000 landlords and 100 private halls as an official housing partner for multiple universities. The platform processes thousands of monthly inquiries across the entire tenant journey—from property searches to tenancy agreements and maintenance.

The Challenge: Complex Lead Management Across Multiple Channels

Operational Inefficiencies in Lead Processing

Student Tenant faced significant challenges in managing their high-volume, multi-source lead pipeline:

1. Inadequate AI Capabilities: Intercom's Fin could only handle basic FAQs. It couldn't manage complex conversations about property specifications, group bookings, pricing negotiations, or eligibility assessments—requiring human intervention for most meaningful interactions.

2. After-Hours Lead Loss: Student Tenant was missing significant leads outside standard operating hours. Gen Z students typically search for accommodation late at night, but inquiries made after hours received delayed responses, causing potential tenants to move to faster-responding competitors.

3. No Lead Segmentation: All leads—students, non-students, and landlords—entered a single queue without automated classification. This created routing inefficiencies and delayed specialized handling based on lead type.

4. Slow Resolution Times: Manual processes and high inquiry volumes meant complex queries took days to resolve. Multiple touchpoints and information gathering from various sources created bottlenecks that impacted conversion rates.

5. Peak Season Overload: Summer inquiry volumes surged by 300% before the academic year. The existing infrastructure couldn't scale to meet demand, resulting in missed opportunities and estimated revenue losses in the hundreds of thousands of pounds.

The Solution: Comprehensive AI-Powered Lead Automation

Agentic AI Architecture

Angoor AI implemented a sophisticated agentic AI system that goes beyond traditional chatbot capabilities:

• Omnichannel Integration and Context Preservation: Unified communications across web widgets, WhatsApp Business API, and email with complete context preservation—students can start on website, continue on WhatsApp, receive documents via email with full conversation history maintained

• Autonomous 24/7 Operations: Fully autonomous AI agents handling complete customer journeys without human intervention, achieving 83% ticket automation with 32% requiring zero human involvement

• Intelligent Re-engagement Systems: Sophisticated follow-up mechanisms identifying conversation drop-offs and initiating personalized re-engagement based on conversation stage, inquiry type, and previous interactions

• Human-AI Collaboration Framework: Seamless handoffs with comprehensive conversation summaries, identified intent, and suggested next actions for human agents, plus real-time alerts for high-priority escalations with complete context

Implementation: Strategic Deployment Approach

Rapid One-Week Implementation

The implementation followed a structured approach that achieved full deployment in just one week—remarkably fast for a comprehensive CRM system:

Phase 1: Business Process Analysis and Mapping: Comprehensive analysis of Student Tenant's workflows, customer journeys, and SOPs to create AI agents aligned with existing business processes.

Phase 2: Technical Integration and Data Migration: API integration with property management systems and establishment of data pipelines from multiple third-party platforms with real-time synchronization.

Phase 3: Agentic Flow Configuration: Configuration of complex agentic flows for lead qualification, property matching, group bookings, landlord inquiries, and automated document generation.

Phase 4: Analytics and Performance Monitoring Setup: Implementation of comprehensive dashboards tracking AI resolution rates, lead conversion funnels, response times, and channel-specific performance metrics.

Results: Measurable Business Impact

Quantitative Outcomes

• Zero first response time for all incoming organic leads

• 67% increase in initial lead engagement rates

• 83% overall ticket automation rate

• 32% complete end-to-end automation (no human intervention required)

• 24/7 availability with consistent service quality

• 3x faster resolution time (from days to hours for complex queries)

• 12 different lead sources unified in centralized repository

• 95% lead classification accuracy

• 500+ daily interactions handled during peak season without degradation

Qualitative Improvements

• Higher student satisfaction with immediacy and accuracy of responses

• Successful handling of complex multi-part queries (group bookings, payment plans, property comparisons)

• Consistent service quality across all channels strengthening brand perception

• Human agents focused on high-value interactions requiring empathy and negotiation

• Improved job satisfaction and reduced turnover from elimination of repetitive tasks

• Increased agent engagement and effectiveness in their roles

Strategic Impact: Enabling Business Transformation

• Landlord Automation Student Tenant is extending AI capabilities to landlords for automated property onboarding—including registration, documentation upload, and pricing setup through AI-guided conversations with automatic compliance validation and listing publication.

• Group Matching The platform now facilitates shared accommodation by matching students based on preferences (study habits, lifestyle, budget) and facilitating group formation—eliminating manual coordination.

• Enhanced Property Discovery AI-powered recommendations based on proximity to university facilities, transport links, local amenities, and academic term availability.

Conclusion

Student Tenant's implementation of Angoor AI's agentic platform demonstrates the transformative potential of intelligent automation in the PropTech sector. The achievement of 83% ticket automation while maintaining service quality proves that AI can handle complex, nuanced customer interactions traditionally requiring human agents. The rapid implementation timeline—one week from initiation to deployment—challenges conventional assumptions about AI project complexity. As Student Tenant continues to expand AI capabilities across their operations, they're not just improving efficiency; they're redefining what customers expect from property platforms. This case study serves as a blueprint for B2C companies seeking to implement sophisticated AI-powered customer engagement without compromising service quality or customer satisfaction.