AI-Powered Self-Storage Operations Platform
Transforming self-storage operations with AI-driven pricing, computer vision occupancy monitoring, and conversational AI for customer service
Overview
StoreSpace operates one of the largest self-storage networks with 50+ facilities across major metropolitan areas. We built an end-to-end AI platform that revolutionized how they price units, monitor occupancy, forecast demand, and serve customers—turning a traditional real estate business into a data-driven operation.
The Problem
The self-storage industry faces a unique challenge: inventory (storage units) is fixed, but demand fluctuates seasonally, by location, and by unit size. StoreSpace was leaving significant revenue on the table with static pricing, while simultaneously struggling with high customer service costs and inefficient space allocation.
Key Challenges
Revenue Leakage from Static Pricing
Prices were set manually once per quarter. During high-demand periods (summer moving season, college move-ins), units were underpriced. During low periods, prices didn't adjust to capture price-sensitive customers. Competitor pricing was tracked manually via spreadsheets with 2-week lag.
Blind Spots in Facility Operations
Facility managers couldn't see real-time occupancy without physical walkthrough. "Available" units were sometimes occupied by overstaying customers. Climate-controlled units had inconsistent monitoring. Security footage was reviewed only after incidents.
Customer Service Bottleneck
Call center handled 5,000+ inquiries daily—80% were repetitive questions about pricing, availability, and unit sizes. Average handle time was 12 minutes. Customer acquisition cost was $150+ per new rental. After-hours inquiries went unanswered, losing potential customers to competitors.
No Demand Forecasting
Marketing campaigns were planned without data on upcoming demand. Facilities couldn't prepare for seasonal surges. No ability to identify at-risk customers likely to vacate. Move-out predictions were based on gut feeling, not data.
How We Built It
Data Foundation & Integration
Connected all 50+ facilities to a unified data platform. Integrated property management systems, payment processors, CRM, and marketing tools. Built real-time data pipelines ingesting competitor prices, local events, weather, and economic indicators. Established clean data models for ML training.
Dynamic Pricing Engine
Developed ML models analyzing 47 pricing factors including competitor rates, local demand signals, unit characteristics, customer segments, and seasonal patterns. Implemented A/B testing framework for price elasticity experiments. Built approval workflows for managers to review AI recommendations before publishing.
Computer Vision Deployment
Retrofitted existing CCTV systems with edge AI devices for real-time occupancy detection. Trained custom object detection models to identify occupied vs empty units, unauthorized access, and maintenance issues. Created facility heat maps showing utilization patterns.
Conversational AI & Automation
Built an AI chatbot handling inquiries, bookings, and contract completion. Integrated with scheduling, payment, and access control systems. Implemented voice AI for phone-based interactions. Created escalation workflows for complex cases requiring human agents.
Solution Highlights
Real-Time Dynamic Pricing
AI adjusts prices across all unit types and locations multiple times daily based on demand signals, competitor movements, and occupancy levels. Managers see recommended prices with confidence scores and can approve, modify, or override.
Predictive Demand Forecasting
90-day demand forecasts by location, unit type, and customer segment. Models incorporate local events (college move-ins, corporate relocations), seasonal patterns, and economic indicators. Enables proactive marketing and capacity planning.
AI-Powered Customer Service
Conversational AI handles 80% of customer interactions including availability checks, pricing inquiries, reservations, and contract completion. 24/7 availability captures after-hours leads. Seamless handoff to human agents for complex situations.
Computer Vision Monitoring
Real-time occupancy tracking via existing cameras. Automatic detection of unauthorized access, overstays, and maintenance needs. Facility dashboards show live utilization with alerts for anomalies.
Technical Deep Dive
The pricing engine uses a gradient boosting model (XGBoost) trained on 3 years of historical data across all facilities. Features include: rolling 7/14/30-day booking velocity, competitor price deltas, local event proximity scores, unit-specific attributes (size, climate control, floor level), and customer segment propensity scores. The model outputs price recommendations with prediction intervals, allowing managers to understand confidence levels. Computer vision models were built using YOLOv8 for object detection, fine-tuned on 50,000+ labeled images from StoreSpace facilities. Edge deployment on NVIDIA Jetson devices enables <100ms inference with no cloud dependency for privacy-sensitive footage. The conversational AI uses a RAG (Retrieval-Augmented Generation) architecture with facility-specific knowledge bases, integrated with booking and payment APIs for end-to-end transaction completion.
AI Capabilities
Dynamic Pricing Optimization
ML models analyzing 47 factors to optimize unit pricing in real-time
Demand Forecasting
Time-series models predicting occupancy 90 days ahead with 94% accuracy
Computer Vision Analytics
Real-time occupancy detection and anomaly identification via CCTV
Conversational AI
GPT-powered chatbot with RAG for accurate, context-aware responses
Customer Churn Prediction
Identifying at-risk customers 30 days before likely move-out
Automated Lead Scoring
Prioritizing inbound leads by conversion probability
Technology Stack
Machine Learning
Data Engineering
Backend
Frontend
AI/LLM
Infrastructure
Results & Outcomes
+40%
Space utilization improvement
Across all 50+ facilities within 6 months of deployment
+28%
Revenue per square foot
Through dynamic pricing capturing demand elasticity
60% faster
Customer onboarding
AI-assisted bookings complete in under 5 minutes
-70%
Support ticket volume
Chatbot resolves most inquiries without human intervention
94%
Demand forecast accuracy
90-day predictions enabling proactive capacity planning
$2.1M
Annual savings
Reduced customer acquisition costs and operational overhead
“Boolean and Beyond didn't just build us software—they transformed how we think about our business. The AI pricing engine alone paid for the entire engagement within the first quarter.”
Operations Director
StoreSpace
Services Used for the Client Product
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