Upjao develops portable, smartphone-powered machines that utilize AI algorithms for real-time quality assessment of various agri-commodities, enabling precise measurement of defects such as fungus and insects within 30 seconds. This technology provides institutional buyers with accurate traceability and eliminates human bias in quality evaluation, ensuring consistent product quality throughout the supply chain.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional methods of assessing the quality of agri-commodities are often subjective, time-consuming, and prone to human error, leading to inconsistencies in quality control and pricing disputes. This can result in inaccurate traceability, rejection of produce, and financial losses for farmers, traders, and institutional buyers.
Solution
Upjao provides AI-powered, portable grain analyzers that deliver rapid and objective quality assessments for a variety of agri-commodities. Using patented AI models and a robust HPC backend, the devices can identify and quantify defects such as fungus, insects, and discoloration in under a minute. This technology enables standardized quality control processes, reduces fraud and tampering, and facilitates quality-linked pricing. The Upjao platform also offers features like geotagging and a centralized dashboard for traceability and tracking.
Target Audience
Upjao's primary customers include farmers, traders, APMC markets, mandis, FMCG companies, processors, wholesalers, and F&B manufacturers involved in the agri-commodity supply chain.
Features
- Portable, smartphone-powered devices for on-site quality assessment
- AI-driven analysis mimicking expert quality assessors
- Rapid assessment within 30-45 seconds
- Measures defects such as fungus, karnal bunt, insects, broken grains, and foreign material
- Patented AI models (UpjaoNet) for various crops including wheat, rice, maize, soybean, and pulses
- HPC backend infrastructure for deployment and production
- Centralized dashboard for data analysis and reporting
- Geotagging for supply chain traceability