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TechAhead collaborated with Jones Lang LaSalle (JLL), a Fortune 500 global commercial real estate services leader, to develop a robust, cutting-edge mobile and web-based enterprise application called Intellicommand. This AI and IoT-powered app is a commissioning platform that enables users to proactively identify problems and anomalies in 5.4 billion square feet of property and facilities worldwide using real-time data monitoring and predictive machine learning algorithms.
While performing equipment maintenance, it’s very critical for the technician to have access to equipment performance history, its current status, and data about any operational issues. TechAhead with its proptech solution empowered maintenance technicians to access real-time, updated information about the equipment installed in 5.4 billion square feet of property and facilities worldwide.
TechAhead system engineers and mobile app developers successfully incorporated next-gen IoT technologies, which enabled technicians to access real-time data of building equipment. Transfer of critical, time-sensitive information was made swift, based on user authentication and access rights.
Our engineers incorporated several data security and user privacy measures since both JLL and 3rd party technicians are using this equipment maintenance application. For example, only those technicians who are registered and within the vicinity of the equipment and property can have access to the equipment’s health data. And the supervisors of the technicians can monitor and track the users who are accessing these sensitive equipment data, with detailed user logs and time stamps.
Our UX/UI experts and designers at TechAhead collaborated with the JLL team to figure out how the user journey can be made seamless, simple, and without complexities. This is the reason we incorporated a color-code-based user interface, which helps the maintenance engineers and technicians to quickly understand the health of the equipment, and take prompt action without any delay. Real-time dashboards, bigger fonts, and seamless display of technical data are the USPs of this next-gen app.
We embedded Agentic AI agents capable of analyzing live IoT sensor feeds, historical performance logs, and contextual factors like weather and occupancy. These agents proactively predicted equipment degradation and automatically generated JAX Codes for components nearing failure—giving facility teams precise foresight into what would fail, when, and why. Instead of Case Study Updates 4 reacting to breakdowns, the system now orchestrated preventative interventions well in advance
For technicians who need to maintain and monitor different, complex types of equipment at residential and commercial establishments, the biggest professional roadblocks are real-time information access and seamless data access about the equipment’s health. Most of the time and resources are consumed for these two activities, for which this new IoT based application was developed for building automation by TechAhead engineers effectively resolved.
Once the access is granted, the technicians can swiftly scan NFC tags and/or QR codes on the equipment, and get swift access to all aspects of equipment health data within seconds. Since the technician is already in the process of maintaining and optimizing the equipment, access to the equipment health data in real-time has saved tons of time for the technicians and made them more productive.
Critical equipment health data such as temperature, setpoints, thresholds, current, time scales, geolocation coordinates, etc have been designed to showcase in a simple, immersive manner with dedicated color codes for every data set, for easy identification and monitoring. This helps the technicians to understand and acknowledge the issues pertaining to the equipment(s) in a swift, and seamless manner, leading to precise maintenance operations, and ensuring the best results.
The vision went beyond automation—Agentic AI turned the building into a self-monitoring, self-healing ecosystem where autonomous agents predicted issues, scheduled interventions, and dynamically balanced energy and maintenance costs.”
Agentic AI agents continuously scanned incoming inspection data, assigning JAX Codes to assets and projecting their failure probability and timeline. This predictive insight empowered managers to prioritize maintenance with surgical precision.
Instead of raw data, AI agents synthesized device streams into actionable intelligence—alerting facility managers when hidden patterns indicated early-stage deterioration.
AI agents autonomously created and optimized maintenance schedules by correlating predicted failures, technician availability, and tenant usage patterns—reducing downtime and maximizing asset life.
With agentic orchestration, every predictive maintenance activity was tied to financial models—allowing real-time allocation of repair costs, energy savings, and lifecycle extension metrics.
Every pump, HVAC unit, and electrical circuit became part of a living digital ecosystem—monitored by AI agents that anticipated failures, auto-assigned JAX Codes, initiated service orders, and balanced energy loads in real-time. This predictive, autonomous framework revolutionized how commercial real estate is maintained, shifting from manual scheduling to AI-driven foresight
We collaborated with a Fortune 500 enterprise to optimise maintenance operations for its worldwide commercial real estate portfolio, resulting in a 20% decrease in equipment downtime and a 30% increase in equipment lifespan. The app’s predictive maintenance boosted JLL’s efficiency, cutting emergency repair costs by $10M annually, proving its proactive approach delivers substantial financial gains.
By harnessing predictive JAX Code analysis and Agentic AI-driven orchestration, JLL transformed facility management from reactive firefighting to intelligent foresight. This cut unplanned downtime by over 40%, optimized energy use, and ensured uninterrupted tenant comfort—unlocking millions in savings while future-proofing building operations.
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