.png)
Letting Data Speak!
Case Study
AI Model for Retail Shelf Monitoring
About the Client
A grocery store or big mart chain seeking to optimize shelf management and improve customer experience.
Challenge
The client faced two primary issues that were causing revenue loss:
Product out of stock: Products were unavailable on shelves despite being in store inventory. The manual process of checking stock was labor-intensive and time-consuming.
Product misplacement: Disarranged or misplaced products negatively impact the customer shopping experience, particularly in high-end fashion outlets where presentation is crucial.
Key Results
Improved real-time stock monitoring, reducing out-of-stock instances by 15%
Enhanced product placement accuracy, increasing customer satisfaction
Reduced manual labor costs for inventory checks
Solution
JashDS developed a deep learning-based model to detect out-of-stock or misplaced products in real-time:
Implemented continuous video stream capture using existing CCTV infrastructure
Created a custom deep learning model to analyze the live video feed
Developed a system to predict and identify out-of-stock and misplaced items in real-time
Integrated automated alerts (SMS/email) to notify relevant personnel for immediate resolution
Utilized state-of-the-art learning-based models and custom datasets from local marts for training
Technologies Used
RetinaNet architecture with ResNet-101 backbone
Focal Loss function
Custom dataset collection and preparation
Computer Vision and Image Processing
Deep Learning frameworks (likely TensorFlow or PyTorch)
Video streaming and real-time processing
Automated alert systems (SMS/email integration)
Other Case Study Items
AI-Driven Candidate Screening Revolution
JashDS revolutionized a company's hiring process by developing a GenAI-powered candidate screener that reduced time-to-hire by 50% and improved hiring outcomes. The solution leverages advanced language models to conduct dynamic, role-specific interviews, automatically generating and adapting questions based on job descriptions and candidate responses.
AI-Powered Resume-Job Matching Engine
JashDS developed an AI-powered resume-job matching engine for a leading Indian job portal, processing over 1M resumes and 50k job descriptions. The solution employed BERT embeddings and HNSW algorithms to create personalized job recommendations for candidates and streamline resume screening for recruiters, significantly enhancing the job search and recruitment processes.
AI-Powered Tyre Dimension Extraction System
JashDS developed an AI-powered computer vision system for a leading automotive e-commerce platform, enabling accurate extraction of tire dimensions from images. The solution, which increased conversion rates by 25% and reduced customer support inquiries by 80%, utilized advanced technologies such as YoloV8 for instance segmentation and custom-designed augmentation techniques to simplify the online tire purchasing process.