Sun Mobility
Operating a 24/7 battery swap network across 500+ stations demanded absolute reliability and instant coordination. Fieldproxy made it possible.
The Old System
Before Fieldproxy, Sun Mobility operated their expanding battery swap network using a mix of manual processes and disconnected tools. Station managers tracked battery health in Excel spreadsheets, technician dispatch happened over WhatsApp groups, and maintenance schedules were managed on physical calendars. When a station went down, operations teams scrambled through multiple systems trying to find an available technician while drivers waited and revenue was lost.
Each swap station had 50-80 batteries that needed constant monitoring for charge levels, health status, and maintenance cycles. Tracking which batteries were where, which needed service, and coordinating battery swaps between stations was a logistical nightmare. Station downtime averaged 4-6 hours because it took so long to diagnose issues remotely, dispatch the right technician with the right parts, and coordinate battery replacements if needed. With drivers depending on their network 24/7, every minute of downtime directly impacted customer trust and business viability.
Manual Battery Tracking
10,000+ batteries tracked in spreadsheets, no real-time visibility into charge status, health, or location
Fragmented Communication
Station issues reported via calls, technician coordination through WhatsApp, no single system of record
4-6 Hour Downtime
Manual coordination meant stations stayed offline for hours, turning away customers and losing revenue
No Predictive Intelligence
Reactive maintenance only, no way to predict battery failures or station issues before they happened
Why Generic Tools Didn't Work
Sun Mobility tried several field service and IoT management platforms before Fieldproxy. Each failed because EV infrastructure operations have unique complexities that standard software couldn't handle. The combination of 24/7 uptime requirements, complex battery lifecycle management, and multi-station coordination created problems generic tools simply weren't built to solve.
Generic FSM Software - No Battery Intelligence
Could manage work orders but had no understanding of battery ecosystems. Couldn't track battery health across stations, predict failures based on charge cycles, or coordinate battery swaps between locations. Treated batteries like generic assets instead of critical inventory with specific charge states and health metrics.
IoT Platforms - No Field Operations
Great at collecting sensor data but completely disconnected from field operations. Could see a station was having issues but couldn't dispatch technicians, track parts inventory, or coordinate repairs. Left operations teams manually bridging between monitoring dashboards and dispatch systems.
Asset Tracking Systems - Wrong Model
Built for static assets like machinery, not dynamic battery networks. Couldn't handle the complexity of batteries constantly moving between stations, swapping in vehicles, and requiring different maintenance based on usage patterns. No concept of charge cycles, degradation curves, or optimal rotation strategies.
How AI Made the Difference
Sun Mobility didn't spend months configuring IoT integrations or setting up asset tracking rules. They simply described their operation: "We run 500 swap stations with 10,000 batteries that move constantly. Need to track health, predict failures, coordinate maintenance, and keep uptime above 99%. Batteries degrade based on charge cycles, not time."
From those conversations, Fieldproxy's AI configured an entire battery network management system. It set up charge cycle tracking, built predictive maintenance models, configured multi-station coordination logic, and established dynamic inventory optimization. The base FSM system transformed into specialized EV infrastructure management through natural language descriptions, not technical configuration.
AI Configured Battery Intelligence
When Sun Mobility explained battery behavior, the AI didn't just create asset records. It understood that batteries are dynamic inventory with health metrics. The system automatically configured charge cycle tracking, built degradation prediction models, set up maintenance triggers based on usage patterns, and created rotation optimization logic. Describing battery lifecycle generated sophisticated asset intelligence without manual modeling.
Complex battery management configured from operational descriptions, not data schemas
AI Built Uptime Optimization
Sun Mobility said 'we need 99%+ uptime, stations can't go offline during peak hours.' The AI configured intelligent monitoring with predictive alerts, built dispatch logic that prioritizes based on station demand, set up parts inventory prediction, and created backup coordination between stations. One requirement generated multi-layered uptime management that learns optimal intervention timing.
Mission-critical uptime logic generated from business requirements
AI Generated Network Coordination
Instead of manually programming inventory rules, they described network dynamics: 'batteries move between stations based on demand, transfer when one station has excess and another needs more.' The AI configured dynamic transfer logic, built demand prediction models, set up automatic coordination workflows, and created optimal distribution algorithms. The system continuously learns better distribution patterns from operational data.
Network-wide coordination configured through operational descriptions
AI Adapted Predictive Models
Sun Mobility described what they wanted to predict: 'battery failures before they happen, which stations need attention, optimal maintenance timing.' The AI configured machine learning pipelines, set up anomaly detection, built pattern recognition from historical data, and established alert thresholds. As the network operates, the system refines prediction accuracy automatically without manual model tuning.
Predictive intelligence configured from desired outcomes, not ML expertise
The Transformation
Hired developers to build custom battery tracking system, took 8+ months and still had gaps
Described battery behavior in conversation, AI configured complete lifecycle management
IoT platforms collected data but couldn't connect to operations, manual bridging between systems
Told AI "predict failures and dispatch maintenance," system configured integrated monitoring and response
Manual coordination of battery transfers between stations, constant phone calls and confusion
Described optimal distribution strategy, AI configured automatic coordination across network
Reactive maintenance only because predictive models required ML expertise they didn't have
Described what to predict, AI built and continuously refined prediction models automatically
System changes required developer time, operations team couldn't adapt configurations
Operations team describes needed changes, AI reconfigures system logic instantly
The Results
AI-powered battery intelligence eliminated 85% of unplanned failures through predictive maintenance
Intelligent dispatch reduced station downtime from 4-6 hours to under 45 minutes
Network uptime increased from 94% to 99.8%, ensuring drivers can always find a working station
Battery lifecycle optimization extended average battery life by 30%, reducing replacement costs
Multi-station coordination improved battery utilization 40% while cutting inventory 25%
Automated workflows freed operations team from manual coordination, allowing network expansion
Real-time visibility replaced fragmented spreadsheets and WhatsApp groups
Predictive intelligence enables proactive operations instead of constant firefighting
Why This Worked
The transformation wasn't about replacing Excel with software. It was about bringing intelligence to an inherently complex operation. Generic tools tried to force Sun Mobility into predefined categories. Fieldproxy's AI learned how battery swap networks actually operate, with all their unique challenges around 24/7 uptime, battery lifecycle management, and multi-station coordination.
The system understood from day one that batteries aren't static assets. They move constantly between stations and vehicles, degrade based on charge cycles and usage patterns, and require intelligent rotation strategies. By configuring itself around these realities instead of fighting them, Fieldproxy delivered 99.8% uptime, extended battery life 30%, and gave Sun Mobility the operational foundation to scale their network with confidence.