Vectech
Vectech provides an AI-powered entomology platform that automatically identifies mosquito and tick species from images, supporting vector surveillance for public health. The system recognizes over 70 mosquito species and 30 tick species, lowering surveillance costs and reducing the training burden for accurate identification. The platform is used worldwide by mosquito control districts, public health organizations, and research institutions.
- Artificial Intelligence
- Data & Analytics
- Healthcare Technology
- Software Only
- Hardware
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
More than 700,000 people die each year from vector-borne diseases, which account for 17% of all infectious disease cases worldwide. Traditional vector surveillance relies on manual species identification by trained entomologists, a time-consuming and costly process that creates bottlenecks in detecting threats and deploying targeted control measures. This training burden and lack of standardized data hampers early detection and effective public health response.
Solution
Vectech provides an AI-powered entomology platform that automatically identifies mosquito and tick species from images, enabling fast and accurate vector surveillance. The system is the world's first AI designed for this purpose, recognizing over 70 mosquito species and 30 tick species. By lowering surveillance costs and reducing the training required for accurate identification, the platform makes professional-grade vector monitoring accessible to a wider range of organizations. The technology produces standardized species data that supports early threat detection, targeted interventions, and customized community responses for better public health outcomes.
Target Audience
Primary customers are mosquito control districts, public health organizations, and research institutions worldwide that require reliable vector surveillance data for disease prevention and outbreak response.
Features
- AI-driven species classification supporting 70+ mosquito species and 30+ tick species from standard images
- Computer vision and deep learning algorithms for species classification and object detection, continuously refined to expand geographic coverage
- Field-deployable hardware systems designed for high-quality image acquisition with streamlined data collection and minimal labor burden
- End-to-end data pipeline from image capture through AI analysis to actionable surveillance intelligence
- Validation and quality control overseen by professional entomologists to ensure taxonomic accuracy