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“At Nexilo, we believe every business deserves the power of AI—whether you're just starting or scaling fast. My team and I are here to support your vision and help you move forward with confidence.”
Challenge
- PCI DSS certification and FCA approval, ensuring full regulatory compliance
- Ultra-low latency transactions under 250 milliseconds, delivering a seamless user experience.
- Successful integration of multiple third-party APIs, including GPS, Xero, and CashFlows
Solar farms generate large volumes of operational data but turning that data into timely action is difficult. Manual monitoring, delayed fault detection, and inefficient maintenance workflows can directly impact energy output and operational cost.
The challenges were systemic:
- Continuous ingestion of IoT sensor data from distributed assets
- Early detection of faults before performance degradation
- Coordinating maintenance teams across sites
- Shifting from reactive to predictive operations at scale
Enerlytics required a platform that could combine AI, operational automation, and infrastructure reliability into a single production ready system.
Client Review
- PCI DSS certification and FCA approval, ensuring full regulatory compliance
- Ultra-low latency transactions under 250 milliseconds, delivering a seamless user experience.
- Successful integration of multiple third-party APIs, including GPS, Xero, and CashFlows
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Key Objectives
- Build an AI driven asset management platform for solar farms
- Enable automated fault detection and diagnostics
- Support predictive and proactive maintenance workflows
- Integrate field operations through a tablet-based application
- Establish a scalable DevOps and MLOps foundation
Solution
Innovify partnered with Enerlytics to design and deliver a full stack platform, combining data engineering, machine learning, and operational tooling.
IoT Data Ingestion and Platform Engineering
We built systems to ingest and process continuous data streams from solar assets, forming the backbone of the platform.
AI Driven Diagnostics and Prediction
Machine learning models were implemented to identify anomalies, predict faults, and surface actionable insights for operators.
Operational Automation
The platform automated issue diagnosis and maintenance scheduling, enabling engineering teams to respond proactively rather than reactively.
Field Operations Application
A tablet-based application was delivered to support on site engineers, providing access to diagnostics, schedules, and operational workflows.
DevOps and MLOps Foundations
The system was designed with strong DevOps and MLOps practices to support reliable deployment, model iteration, monitoring, and long-term scalability.
Results
- PCI DSS certification and FCA approval, ensuring full regulatory compliance
- Ultra-low latency transactions under 250 milliseconds, delivering a seamless user experience.
- Successful integration of multiple third-party APIs, including GPS, Xero, and CashFlows
Enerlytics launched a production ready AI driven platform that enabled intelligent monitoring and management of solar farm assets.
The platform shifted operations from reactive fault resolution to predictive and proactive maintenance. More importantly, it established a robust technical foundation where infrastructure, data pipelines, and machine learning models work seamlessly together. This allowed Enerlytics to operate with greater confidence, efficiency, and scalability in a high impact renewable energy environment.
This engagement demonstrates Innovify’s ability to deliver complex AI systems in the real world. It shows how DevOps and MLOps are not standalone services, but essential foundations for operating machine learning driven platforms where reliability, automation, and scale matter.
