LoopOS Water Intelligence Platform

Overview

LoopOS is an IoT-driven platform designed to modernize refillable water delivery by transforming it from a manual, guess-based process into a data-driven, automated system.

At its core, LoopOS uses a smart hardware device placed beneath water coolers or refillable jugs to continuously measure weight and determine real-time water levels. This data is transmitted to the cloud, where it is processed to track consumption patterns and predict when a customer will need a refill.

Instead of relying on fixed schedules or customer calls, the system enables distributors to anticipate demand, optimize delivery routes, and ensure customers never run out of water. By combining embedded hardware, cloud infrastructure, and predictive analytics, LoopOS turns a traditionally reactive service into a proactive, intelligent operation.

The platform includes both a distributor-facing dashboard and a customer-facing interface, creating transparency across the entire delivery ecosystem while enabling automation at scale.

What I worked on

• Designed and built a hardware device using load cells and microcontrollers like ESP32 to accurately measure water weight and infer remaining volume in real time.
• Implemented firmware to collect sensor data, handle noise filtering, and transmit readings to cloud services over Wi-Fi.
• Developed logic to detect key events such as jug replacements, abnormal usage patterns, and near-empty thresholds.
• Built a cloud-based data pipeline to ingest, store, and process time-series sensor data for each deployment location.
• Designed predictive algorithms to estimate future water consumption and determine optimal refill timing.
• Created a backend system to manage devices, users, and consumption analytics.
• Developed API endpoints to support integration with dashboards and mobile applications.
• Designed and built a distributor dashboard for monitoring all client locations, water levels, and refill predictions.
• Created a customer-facing interface to display current water levels, estimated days remaining, and delivery status.
• Optimized system reliability with reconnection logic, heartbeat monitoring, and fault tolerance for IoT deployments.

Outcome

• Eliminates guesswork in refillable water delivery by providing real-time visibility into customer usage.
• Reduces delivery inefficiencies by enabling data-driven route optimization and demand forecasting.
• Improves customer experience by ensuring water is refilled before it runs out.
• Increases revenue opportunities through usage-based upsells and subscription services.
• Demonstrates a scalable model for transforming traditional logistics services using IoT and predictive systems.