Technical Proposal For Infrastructure Monitoring
ENTERPRISE
Lancer:
<= 2 resources OR <=3 weeks effort OR <=$3000 USD
Growth:2 - 4 resources AND upto 4 months OR
upto $30000 USD
Enterprise:Everything else
(Development)
Project Description
This AI-powered infrastructure monitoring platform leverages machine learning to detect anomalies, predict equipment failures, and improve operational visibility. Designed for industries managing large-scale physical and digital infrastructure, the system automates performance tracking, fault prediction, and maintenance scheduling to reduce unplanned downtime and extend asset lifespan.
Scope Of Work
The client required a scalable monitoring system capable of processing real-time data from IoT devices, system logs, and video feeds. Key objectives included anomaly detection, predictive maintenance, incident alerts, and dashboard-based reporting. The solution needed to minimize manual intervention while improving infrastructure reliability and operational efficiency.
Our Solution
Oodles delivered the solution in the following way:
- Designed AI models to detect performance anomalies and forecast infrastructure faults using historical and real-time data
- Built a real-time data pipeline to collect and analyze inputs from sensors, cameras, and system logs
- Integrated predictive maintenance modules that trigger automated alerts and recommended actions
- Developed visual dashboards and heatmaps to display infrastructure status, uptime metrics, and fault patterns
- Deployed an adaptive, scalable architecture that learns and improves over time with new data