SaveIt AI / Energy IoT Platform
A real-time energy management platform with telemetry pipelines, operational dashboards, and anomaly/loss detection direction.
This is a private/internal product currently in development. This case study focuses on architecture, product thinking, and technical direction.
Problem
Operations teams received delayed consumption data with no unified view, making real-time waste detection difficult.
Constraints
The platform had to ingest multiple telemetry streams at different rates while keeping data reliable and response times fast under load.
Architecture / Approach
I implemented an event-ingestion layer, time-series oriented storage patterns, and dashboard APIs for current status, anomalies, and investigation signals.
Key decisions
I chose an event-driven, asynchronous architecture to decouple ingestion from user-facing delivery and keep the system stable.
Outcome / Current status
Teams gained a much fresher operational view and reduced the time needed to spot loss points and unusual consumption patterns.
Lessons
In IoT products, operational accuracy depends on end-to-end dataflow design, not only on the final dashboard UI.
Interested in a similar build?
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