Smart Retail – Where Innovation Meets Convenience
Elevating Retail with AI-Powered Solutions
Smart Retail is redefining the shopping experience by blending physical and digital spaces to create seamless, personalized, and efficient customer journeys. From real-time inventory visibility to AI-powered customer behaviour analytics, modern retail environments are becoming more responsive, data-driven, and operationally intelligent.
MulticoreWare addresses these evolving needs of smart retail by enabling real-time decision-making through parallel inference across multiple AI models. A key aspect of our work in smart retail includes enabling product validation through YOLOv8-based object detection, accurate barcode verification with ResNet-based similarity matching, and low-latency fraud detection at the edge powered by LSTM and Vision Transformers. We enhance personalization through Llama2-based product recommendations and optimize checkout operations with synchronized multi-sensor data fusion.
By deploying scalable, high-performance AI systems that operate seamlessly across embedded hardware, we help retailers reduce revenue loss, improve shopper experience, and prepare for future-ready retail automation with integrations like, RGB, LiDAR, and voice-enabled AI assistants.
Key Solutions & Offerings
Self-Checkout and Point of Sale Intelligence
Self-checkout and point-of-sale systems are redefining front-end operations by streamlining transactions and minimizing errors. We enable this by synchronizing data from multiple sensors such as barcode scanners, image inputs, and weight sensors in real time. This coordination allows the system to adapt to user behaviour, detect mismatches instantly, and reduce checkout wait times, ultimately enabling faster service and improving store flow during peak hours.
In-Store Analytics & Customer Insights
In-store analytics unlock real-time visibility into shopper movement and behavior. This is powered by integrating multi-sensor data streams including cameras, sensors, and face recognition, processed through a robust Kafka pipeline from the cloud. This enables precise tracking of dwell times, heatmaps, and traffic patterns, empowering retailers to make smarter decisions on store layout, product placement, and promotional zones. The result is enhanced customer engagement and higher conversion rates.
Smart Shelves & Inventory Monitoring
Smart shelves combine vision AI and sensor technologies to maintain accurate inventory tracking without manual checks. These systems monitor stock levels continuously, trigger timely replenishment alerts, and ensure planogram compliance, helping staff focus on customer service while keeping shelves organized and fully stocked.
AI-Powered Customer Engagement & Personalization
AI-powered personalization enhances in-store interactions by delivering timely, relevant content using real-time customer data. Going beyond face recognition and object detection, advanced models analyze purchase patterns, gaze direction, product dwell time, and demographic cues to power dynamic recommendations and digital signage. This creates a tailored shopping experience that boosts satisfaction, loyalty, and average basket size.
Technology Stack and Capabilities
WHY CHOOSE MULTICOREWARE?
Retailers face constant pressure to improve efficiency while reducing shrink and enhancing the customer experience. At MulticoreWare, we bring hands-on experience and a practical approach to solving these challenges using AI. Our team has worked closely with retail environments to deploy solutions that spot fraud at self-checkouts, track shopper behavior in real time, and support smarter inventory and staffing decisions.
By combining proven deep learning models with on-the-ground understanding, we help stores run smoother, respond faster, and deliver more personalized experiences, without disrupting existing operations.
A Real-World Application:
Client & Problem Statement:
The client faced frequent challenges with barcode mismatches, pricing errors, item skipping, and undetected shoplifting during self-checkout. These issues led to increased revenue loss, reduced customer trust, and inefficiencies in inventory tracking.
Solution:
We implemented an AI-powered self-checkout and theft detection solution that combined multi-sensor data fusion (barcode, vision, and edge sensors), real-time fraud detection, and behavioural analysis using CNN-LSTM and Vision Transformers. The system enabled accurate object identification, personalized recommendations with LLMs, and edge-based alerts for suspicious activities, all running with low-latency inference. This strengthened security, accelerated transactions, and enhanced the overall in-store experience.
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