AI that runs your
facilities.
Five purpose-built AI engines that automate operations, predict failures, and empower every technician — processing 10M+ work orders annually across 450M+ sqft of managed space.
How Reflexion AI Works
Data flows from IoT sensors and integrated systems through our AI platform to deliver real-time insights and automation.
Powered by AI · Connected by IoT
AI Knowledge Assistant
Technicians waste time searching for troubleshooting guides, calling experienced colleagues, or making trial-and-error repairs. Institutional knowledge is lost when experienced staff leave.
From the Reflexion Technician App, when a technician starts a work order, the AI provides a step-by-step guide tailored to the reported fault, previous knowledge base entries, asset history, OEM manuals, make/model, and known global issues. At each step, the technician can add notes and photos, and the AI adapts subsequent steps.
NLP Work Order Triage
Helpdesks spend hours manually reading tenant complaints, categorizing issues, looking up locations, assigning priorities, and routing to technicians. This creates bottlenecks, delays, and SLA breaches.
When a tenant submits a complaint like “AC is leaking in Meeting Room 3,” Reflexion’s NLP engine automatically identifies the requestor, maps the location (Tower 1 › Floor 5 › Meeting Room 3), classifies the fault (HVAC › Air Conditioners › Water Leakage), sets the priority (P2), and routes it to an available AC technician based on the duty roster.
“AC is leaking in Meeting Room 3”
Computer Vision — Auto WO Closure
Supervisors must manually review before/after photos for every completed work order to verify the work was actually done. This is time-consuming, subjective, and creates approval backlogs.
During work order completion, technicians upload before and after photos. Reflexion’s computer vision engine scans the images, calculates a visual completion score, and auto-approves for “Closed” status or flags for “Reopened.” It can also identify additional issues for corrective work orders.
Predictive Maintenance
Reactive maintenance means assets fail unexpectedly, causing costly emergency repairs, tenant disruption, and safety risks. Traditional scheduled maintenance is either too frequent or too infrequent.
By analyzing PPM work order history, reactive maintenance records, OEM-suggested MTBF data, failure data from similar assets, and IoT sensor/meter readings, Reflexion AI predicts that a specific asset has a 75% chance of failure in the next 30 days — enabling proactive maintenance before breakdown.
Technician Performance Analytics
Training decisions rely on subjective supervisor judgment. There’s no systematic way to identify which technicians need upskilling, what skills they lack, or how they compare to peers.
Reflexion AI monitors technician work order completion data — declared skills, time taken, satisfaction ratings, feedback remarks, reopened work orders, SLA breaches, and photo quality. Over time, it identifies patterns and pinpoints specific skill gaps for targeted training.
Connected to your buildings.
Integrated with your infrastructure.
Works with your existing VMS, IoT sensors, and building systems. Reflexion is the intelligence layer that connects it all.
See what Reflexion
can do for you.
Trusted by leading property developers and FM companies across the GCC — including marquee owners like Emaar, Dubai Sports City, and Al Mouj. Tell us about your portfolio and we'll show you exactly how Reflexion fits your operations.