Pollen Sense utilizes automated particulate sensors and machine learning to provide real-time data on airborne pollen, mold, and dust, enhancing air quality monitoring for various industries and public health initiatives. By delivering precise, hyperlocal counts and forecasts through an API and dedicated apps, Pollen Sense enables users to make informed decisions regarding allergen exposure and environmental conditions.
Funding
Funding not disclosed
Founders
Product
Problem
Current methods for monitoring airborne pollen, mold, and dust often rely on manual collection or modeling, which can be inaccurate and lack the real-time, hyperlocal resolution needed for effective decision-making. This can lead to inadequate information for managing air quality, mitigating allergy symptoms, and conducting environmental research.
Solution
Pollen Sense provides real-time, hyperlocal data on airborne pollen, mold, and dust through an automated sensor network and machine learning algorithms. The system utilizes automated particulate sensors to continuously sample the air, identifying and quantifying various airborne particles. This ground-truth data is then processed using AI to generate accurate counts and forecasts, delivered via an API and dedicated applications. By offering high-resolution environmental intelligence, Pollen Sense enables informed decisions for industries, public health initiatives, and individuals seeking to manage allergen exposure and air quality.
Target Audience
The primary target audience includes air quality programs, medical practices, allergy sufferers, environmental researchers, and industries requiring precise air quality monitoring.
Features
- Automated particulate sensors that continuously sample air for pollen, mold, and dust.
- AI-powered analysis providing real-time counts and 3-day forecasts.
- Hyperlocal reporting with a resolution of up to 30km.
- API access for integration into existing applications and research projects.
- Identification of various pollen species, including grass, weed, pine, and elm.