Digital agri marketplace solution development

Case Study

Their client’s current quality checking process was also having a lot of issues like,

They also wanted to develop an agri-marketplace solution that combined listing, trading and quality inspection and reporting.

AI-based quality checking: Developed the QC module that uses computer vision and AI/ML algorithms to detect and classify grains, ensuring high accuracy.
Custom quality standards: Users can create quality standards for different commodities and variants, adding required quality parameters.
Instant report generation: The app generates instant quality reports in PDF or other formats.
Commodity trading and Llsting: Designed modules for commodity trading and listing agriculture products and services.
High availability and security: Implemented high-availability solutions with load balancers, auto-scaling, and Kubernetes orchestration for security.
Efficient development: Utilized Azure DevOps and Git Flow branching strategy for faster product development and delivery.
Database Integration: Integrated the app with the client’s database and middleware for efficient data management.

99% efficiency improvement: On-the-spot assessment reports within 3-4 seconds, saving time and eliminating the need for additional resources.
Enhanced user experience: Local language support, offline functionality, and minimal environment requirements.
Expanded market reach: Farmers and businesses reached a larger audience, expanding their potential market and getting better prices.
Real-time accessibility: Users can access information and updates in real-time, enabling better decision-making.
Faster software shipping: Easy scaling and containerization technologies freed up DevOps teams for innovation.
Cost savings: Saved 20% of costs on resources with effective scaling on AWS.
Quality improvement: Reduced time and cost associated with quality checking, ensuring regular quality checks and 90% accuracy compared to lab data.

Sophos MDR for next-level cybersecurity