Pricing decisions were too manual
StoreSpace operated 50+ self-storage facilities with manual pricing decisions, inefficient space allocation, and high customer acquisition costs.
2023·14 weeks·4 engineers
AI-Powered Storage Operations
40% improvement in space utilization, 60% faster customer onboarding
Client:StoreSpace
40% improvement in space utilization, 60% faster customer onboarding
Overview
StoreSpace partnered with Boolean & Beyond to deliver intelligent storage management platform with predictive demand forecasting, dynamic pricing optimization, and automated facility operations for self-storage chains. The engagement focused on improving how the business operated day to day while creating a platform that could scale with demand.
The Problem
StoreSpace operated 50+ self-storage facilities with manual pricing decisions, inefficient space allocation, and high customer acquisition costs. Occupancy rates varied wildly between locations with no unified data strategy. StoreSpace needed a delivery partner who could translate that pressure into a product and systems implementation that improved speed, visibility, and reliability without adding more manual overhead.
Challenges
What made this hard
There was no unified operating view
Occupancy rates varied wildly between locations with no unified data strategy.
The business needed a more scalable foundation
StoreSpace needed an implementation path that solved the immediate bottlenecks while building a more scalable operating model for logistics & storage.
How we delivered
Our approach
Phase 01
Discovery & workflow mapping
We began by mapping the current workflow end to end, identifying the steps creating the most friction for StoreSpace, and prioritizing the journeys where better UX, cleaner data flow, or deeper automation would create the fastest business lift.
Phase 02
Architecture & experience design
From there, we translated the business goals into a delivery plan and architecture centered on Intelligent storage management platform with predictive demand forecasting, dynamic pricing optimization, and automated facility operations for self-storage chains, so the build could improve adoption, operator control, and room for scale at the same time.
Phase 03
Build, integrations & intelligence
The build was structured across the experience layer (React Native), core services and data (Python), the intelligence layer (TensorFlow, Computer Vision, and NLP), and production integrations and ops (AWS). We built an AI-powered operations platform that analyzes market conditions, competitor pricing, seasonal patterns, and customer behavior to optimize rates in real-time. Computer vision monitors facility occupancy and suggests space reallocation. An AI chatbot handles 80% of customer inquiries and bookings.
Phase 04
Launch hardening & optimization
Before and after launch, we tuned the workflows, operational guardrails, and instrumentation against live usage so the platform could reliably support 40% improvement in space utilization, 60% faster customer onboarding.
What we built
Solution highlights
01
Core product experience
Intelligent storage management platform with predictive demand forecasting, dynamic pricing optimization, and automated facility operations for self-storage chains.
02
Real-time operations and data flow
We built an AI-powered operations platform that analyzes market conditions, competitor pricing, seasonal patterns, and customer behavior to optimize rates in real-time.
03
Vision-driven monitoring
Computer vision monitors facility occupancy and suggests space reallocation.
04
Conversational automation
An AI chatbot handles 80% of customer inquiries and bookings.
The full story
Journey & Execution
AI-Powered Storage Operations
StoreSpace wanted to run self-storage operations with the rigor of a modern digital marketplace, not a network of facilities making disconnected local decisions. Pricing, occupancy, customer acquisition, and support all influenced each other, but the team lacked a system that could see those relationships in one place.
The goal was not just to add another reporting layer. The goal was to create an operating system for revenue, availability, and customer interaction that regional teams could trust every day.
Business Context
Individual locations were reacting to market shifts manually. That meant rates changed too slowly, promotions were inconsistent, and the business often filled the wrong unit mix while higher-margin inventory sat underutilized.
At the same time, onboarding and booking support created avoidable drag. Prospective customers asked the same questions repeatedly, while internal teams spent time on tasks that should have been automated.
StoreSpace operated 50+ self-storage facilities with manual pricing decisions, inefficient space allocation, and high customer acquisition costs. Occupancy rates varied wildly between locations with no unified data strategy.
How We Approached the Build
Discovery and workflow mapping
We mapped the path from lead to booked unit, reviewed how pricing was updated, and identified where occupancy data, competitor signals, and customer intent were breaking apart instead of informing one another.
Architecture and product design
The platform was designed around a shared operating view: a mobile and operations layer for facility teams, a service and data layer for inventory and customer workflows, and an intelligence layer that could continuously reevaluate pricing and space allocation recommendations.
Delivery and integration
We treated revenue optimization, booking experience, and support automation as one connected system so StoreSpace could improve occupancy, conversion, and operational efficiency together.
What We Implemented
The implementation focused on a few core surfaces that changed how teams ran each facility day to day:
- A dynamic pricing workflow that reacted to occupancy, local demand patterns, seasonality, and competitor pressure instead of relying on static monthly updates.
- Space utilization views that highlighted underperforming unit categories and recommended where reallocation or promotion would improve yield.
- A customer-facing booking and inquiry experience that reduced handoffs and made it easier to complete onboarding without staff intervention.
- Cross-location performance reporting so leadership could compare facilities and act on the same operating signals across the portfolio.
AI and automation layer
Predictive models helped the team move before occupancy or pricing problems became visible in monthly reports. Rather than producing abstract forecasts, the system surfaced concrete actions: where to adjust rates, where to shift attention, and where support automation could absorb repetitive demand.
That made the AI useful in operations, not just interesting in dashboards.
Stack and engineering decisions
The implementation used Python, TensorFlow, React Native, AWS, Computer Vision, and NLP across the experience, service, data, intelligence, and integration layers.
- Experience & channels: React Native
- Core services & data: Python
- AI/ML & automation: TensorFlow, Computer Vision, and NLP
- Cloud, integrations & ops: AWS
Rollout and measurable outcomes
The rollout prioritized the facilities with the biggest occupancy swings first, then expanded once pricing behavior, booking conversion, and support deflection were stable enough to standardize across the wider network.
- Space Utilization: +40%
- Revenue per sqft: +28%
- Customer Onboarding: 60% faster
- Support Tickets: -70%
Why This Delivered Business Value
The result was a stronger commercial loop: better pricing decisions, faster onboarding, less repetitive support work, and a clearer way to manage profitability at portfolio scale.
Taken together, the engagement delivered 40% improvement in space utilization, 60% faster customer onboarding by aligning product experience, workflow design, and implementation detail around the same business objective.
Under the hood
Technical Deep Dive
We treated the implementation as a product system rather than a single feature build, with React Native on the delivery surface and Python behind it. We built an AI-powered operations platform that analyzes market conditions, competitor pricing, seasonal patterns, and customer behavior to optimize rates in real-time. Computer vision monitors facility occupancy and suggests space reallocation. An AI chatbot handles 80% of customer inquiries and bookings. Intelligence was embedded through TensorFlow, Computer Vision, and NLP, giving StoreSpace automation and decision support inside the workflow instead of forcing teams into separate tools. Deployment, integrations, and production operations were supported by AWS, which helped the team roll out reliably and keep improving after launch.
Intelligence layer
AI Capabilities
Computer vision and image analysis
Visual intelligence was used to detect patterns, hazards, or clinical signals faster than manual review alone and to surface the findings inside the operational workflow.
Conversational automation
Natural-language interfaces were connected to the underlying systems so users could get answers, take action, or move through key workflows without waiting on manual intervention.
Predictive operational intelligence
Forecasting and prediction models turned historical and live signals into recommendations teams could use for planning, pricing, staffing, or resource allocation.
Tech Stack
Technologies used
Experience & channels
React Native
Core services & data
Python
AI/ML & automation
TensorFlowComputer VisionNLP
Cloud, integrations & ops
AWS
Impact at a glance
Results that speak for themselves
Every metric shown is measured in production — not projected, not estimated. These are the real numbers from the deployed system.
Measurable outcomes
+40%
Space Utilization
Improvement driven by AI-optimized space allocation and predictive demand forecasting across 50+ facilities
+28%
Revenue per sqft
Dynamic pricing algorithms adjust rates based on demand, seasonality, competitor pricing, and occupancy trends
60% faster
Customer Onboarding
Automated KYC, contract generation, and unit assignment reduced onboarding from 3 days to same-day move-in
-70%
Support Tickets
AI chatbot handles billing inquiries, access requests, and unit information — escalating only complex issues to staff
“
"Dynamic pricing alone increased our revenue per square foot by 28%. The AI chatbot handles 80% of customer inquiries, and the occupancy predictions let us plan expansions with data instead of gut feeling."
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