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How we build the world's most accurate and granular environmental data

We combine the power of on-ground sensing and remote imagery from satellites. Our proprietary models measure, process and analyze data on environmental factors such as air quality, soil, microweather, pollen, and more.

Every breath you take is different!

Air quality is dynamic, changing every block and every minute. Up until now, the world has only had unreliable data, updated every few hours, for a few square feet in every city. You can't trust that kind of data to make any decisions. Like the man said, 'If you can't measure it, you can't fix it.' We need better information on what we breathe, so that we can build for better public health and a cleaner planet.

Know how India’s largest air
quality network works

Combining the power of on ground sensing and satellite imagery, we measure, process and analyze data about various aspects of the environment that comprises of Air, Pollen, Water, Soil, Weather and lots more.

Know how India’s largest air
quality network works

Air quality is extremely dynamic and changes hourly and from one street to another. Governmental stations report information with a delay of several hours, not in real-time. When it comes to air quality, unreliable data is worse than no data at all. As pollution reporting has a real-world impact on people’s health, relying on governmental station data alone is simply not enough.

A better way to measure the environment

Measure
Process
Analyze
Deliver
Measure

Geospatial Interpolation

On-ground sensor data, satellite imagery, open and governmental data sets, all help in increasing sample sets needed for geospatial interpolation

Physical & Chemical reactions

The data from sensors, satellites 7 Satellite Data, Aggregate Sensors (CPCB, OpenQ, AQICN, etc.) help in increasing the samples needed to perform Geospatial interpolation

Dynamic Human Activities

Human activity is a big indicator of variable air quality (no surprise!). We measure traffic patterns and density, construction zoning, and even have crowd-sourced reports on pollution incidents.
Process

Feature Engineered Neural Network

All the data we generate is fed into our neural network, to help improve the accuracy of our spatial interpolation
Analyze

A.I. based decision making Real-Trained ML Models

Trained ML Models with 10+ years of Data and Micro Services Best Practises & Error Correction
Deliver

Real-time analytics. Visualisation meaningful. Insights. Delivery.

Trained ML Models with 10+ years of Data and
Micro Services Best Practises & Error Correction

One of the world's largest IoT sensor networks

We own one of the world's largest IoT environmental sensor networks. And only we have access to the data from this network. Our incredibly dense, urban spread of sensors helps us build better, more accurate, and more hyperlocal models. We're able to apportion variations in air quality to specific actions and events, and derive intelligence and insights in real time. Imagine the solutions you can build with this data.

The difference that makes real difference.

US-EPA Benchmarked Sensors

Our sensors come calibrated from manufacturing, and are benchmarked by governmental agencies. They undergo continuous calibration, in real time, across their service life.

Hybrid Sensor Network

Collecting data from satellites like TROPOMI, Sentinel, Landsat, MODIS and other earth observatory satellites, we've built proprietary microservices and algorithms that turn scientific and meteorological data and images into readable information

Sensor agnostic, Data-centric

We aggregate all on ground sensors but focus on accurate data, availability, actionability and format.

ANN over Interpolation

With over a decade of data to train our models, we have proprietary ANN-GeSpaInt - Geospatial Interpolation which has amongst the highest ever published data accuracy.

Providing accurate environmental intelligence for the world.

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“Air pollution continues to take a toll on the health of the most vulnerable populations – women, children and the older adults.”
—
Dr. Flavia Bustreo
Assistant Director General,
World Health Organisation (WHO)

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Model of a cloud unit

Weather

Model of a pollen unit

Pollen

Air Quality

Model of a fire unit

Fire

Model of a water vapor unit

Water Vapour

Model of a soil unit

Soil

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