Asia 6| Transforming fresh food supply chain with data.


Globally, 40% of fresh produce is lost post-harvest. India alone lost ~$15 billion in 2019.
Quality assessment and management of fresh food is a huge problem in retail owing to manual decision making based on intuition, leading to high inventory loss and suboptimal margins.
Qzense is an IoT solution employing a unique combination of NIR and olfactory sensors to capture the internal quality of food and proprietary machine learning models to give an accurate assessment of internal quality parameters like ripeness, brix, spoilage, and shelf Life. Enabling retailers to reduce losses and capture optimal margins from the same produce.
The product currently counts two customers in two indian metro cities including India’s largest fresh retail chain.

Meet The Founders

Rubal is an Electronics Engineer with 8+ yrs of experience in building IoT products. She has worked at Havells R&D, managing a research project with IIT Delhi. She has been a core developer in building IOTA, India’s First Smart Bulb. She brings a deep expertise in R&D team building, Proof of Concept development for IoT products and manufacturing of hardware products at scale.

Dr. Srishti has a PhD in computational biology & chemical ecology from TIFR-NCBS specialising in biosensors & olfaction and a BS+MS from IISER Mohali. She has worked extensively in the area of agriculture research and biotechnology with 11+ years of research experience in biology data analysis across top universities like Max Planck institute & University of Minnesota.

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