# Oizom: Predicting Pollution Levels with TimeGPT

> Oizom leverages Nixtla's TimeGPT to predict pollution levels, boosting user engagement by 40% and optimizing resource allocation for proactive environmental management

Canonical HTML: https://www.nixtla.io/success-stories/oizom

## Customer

- Name: Oizom Air Quality Monitoring System
- Industry: Environmental Monitoring, IoT

Oizom integrates Nixtla's TimeGPT to forecast environmental pollution, transforming decision-making for cities and industries

## Key results

- **40% Engagement Increase:** User engagement on the Oizom dashboard increased by 40% because of TimeGPT integration
- **1 week Rapid Implementation:** TimeGPT was integrated with Oizom's platform in just 1 week, an unprecedented time-to-value speed for state-of-the-art AI

## Overview

Oizom is a cleantech IoT company specializing in ambient environmental monitoring systems. Founded in 2015 in Ahmedabad, India, Oizom has deployed over 1,000 sensor-based air quality monitoring devices across 47+ countries

Their product suite includes the Polludrone (ambient air quality monitor), Odosense (odour monitor), Dustroid (dust particulate monitor), AQBot (industrial gas monitor), Weathercom (meteorological station), and the cloud-based Envizom software platform
Oizom's mission is to democratize air quality data by making environmental monitoring affordable, scalable, and accessible, thereby enabling data-driven decision-making for communities, industries, and city authorities
By partnering with Nixtla, Oizom integrated TimeGPT, transforming their platform by offering accurate 24-hour pollution forecasts that enable preemptive action

## The Challenge

Oizom needed a reliable method to predict environmental pollution spikes during critical time intervals to enable preemptive measures
- Difficulty forecasting regular pollution spikes from traffic congestion and industrial activities
- Inconsistent pollution levels during peak hours and specific industrial release times
- Limited in-house resources to develop complex forecasting models

## The Solution

Nixtla's TimeGPT provided a streamlined integration that uses a 7-day historical dataset to forecast pollution levels 24 hours ahead. This 'monitoring + forecasting' approach combines continuous sensor monitoring with advanced predictive analytics, enabling automated alerts and proactive management (Oizom)
- Leveraged an hourly 7-day historical dataset to accurately predict next-day pollution levels
- Seamless integration with Oizom's existing dashboard, boosting user engagement
- Implemented within 1 week, significantly reducing development complexity

## Business Outcomes

Increasing dashboard engagement, accelerating deployment, and optimizing resource usage
- **Enhanced Engagement**
  - 40% increase in dashboard engagement
  - Greater user interaction for proactive monitoring
- **Rapid Deployment**
  - Nixtla's ease of use enabled integration in the time of 1 week
  - Increased value to our customers with accurate and fast forecasts that enable proactive management
- **Proactive Management**
  - Automated alerts and citywide action triggers
  - Optimized scheduling of high-emission tasks

## Testimonial

> TimeGPT has transformed our forecasting capabilities, enabling near-instant, highly accurate predictions that empower proactive decision-making in environmental monitoring

Bhumik Nayak, Product Manager, Oizom