Optimizing AI Food Waste Monitoring: Hugo's Impact on Accuracy and Efficiency
The Client
A technology company providing an AI-powered food waste monitoring solution for commercial kitchens worldwide. Their system combines computer vision and smart scales to identify discarded food, measure its weight, and assess financial and environmental impact. Deployed in hundreds of kitchens across 40+ countries, the solution helps chefs and managers reduce food waste by up to 70%.
The Challenges
The client needed a solution that could accurately identify diverse food items in different preparation states, angles, and lighting conditions. This required precise labeling across thousands of images with consistent tagging. The system also needed to improve continuously through rapid feedback, effectively manage edge cases, and operate efficiently to deliver timely waste analysis reports.
The Solution
Hugo implemented a tailored strategy to address the Client's food recognition challenges:
Specialized Team
Hugo assembled a team of annotators with relevant domain expertise. The team ramped up in one week and received focused training on labeling requirements, including distinctions between raw vs. cooked, whole vs. cut, and regional food variations. Annotators used reference materials and a database of pre-validated food images to ensure consistent, accurate labeling.
Precision-Focused Quality Assurance
To uphold high annotation standards, Hugo introduced a multi-layered quality control system featuring standardized guidelines, calibration exercises, tiered reviews, and systematic audits. Performance metrics ensured that precision rates exceeded 95%.
Accelerated Feedback Implementation
Hugo conducted regular review sessions with the Client’s AI team to refine guidelines and address edge cases. Clear communication channels enabled rapid escalation of complex items, improving annotators' understanding of the Client’s requirements.
Adaptive Workflow Management
Hugo implemented scalable processes and priority-based queuing to manage fluctuating data volumes. Custom API integrations and quality checks ensured efficient data transfer and timely report delivery.
Measurable Outcomes
94% accuracy maintained across diverse food categories
8,000+ weekly images processed during peak periods
65% faster annotation time achieved through process optimization
250+ new food categories successfully integrated
Lasting Outcomes
Hugo’s annotation and feedback systems enhanced the Client’s food waste monitoring, delivering actionable insights that helped restaurants optimize budgets and reduce environmental impact.
"Hugo's team became increasingly proficient with our data thanks to their effective feedback implementation. Their collaborative approach quickly transformed them into experts on our specific needs." — Schacht J., AI Team Lead
