Enhancing Route-to-Market Strategies with Computer Vision & AI/ML Solutions
97%
Accuracy in detecting and analyzing FMCG products on store shelves is achieved by SoluteLabs' AI-powered image recognition solution.
Less than 3-minute
Processing Time - The AI system processes images and generates insights in under 3 minutes, enabling faster decision-making for retail execution.
Industry
Retail
Funds Raised
Oct 22 - present
Recognizing FMCG Products and their parameters
Ivy Mobility is a leading provider of intelligent Route-to-Market (iRTM) solutions for the Consumer Packaged Goods (CPG) industry, focusing on automation, productivity, and sales growth. Since 2017, Ivy Mobility has partnered with SoluteLabs on multiple initiatives to drive innovation and efficiency.
In this initiative, we collaborated to develop a sophisticated end-to-end Computer Vision pipeline, incorporating advanced technologies such as Object Detection, Image Classification, and Optical Character Recognition (OCR).
Problem Statement
We a part of our initial deliverables and ongoing product engineering, we helped the client by creating a dedicated team that span across Design, Development, QA, and DevOps. We also created four core interfaces, two web apps for the business users and Mobile Apps for end customers
It was a very, very fruitful engagement because we were successfully able to roll out the product, as in any product organization time-to-market is more important, and that's where SoluteLabs helped us so we were able to build that mobile solution in a record time and roll it out for the customers.
Reliable Partner for Fast Time-to-Market
High-Quality Talent & Strong Backend Support
Long-Term, Trustworthy Partnership
Solution
Ivy Eye is an Android & IOS application enabling merchandisers to track products on shelves using their mobile devices and sync data for supervisor action.
Point of Sales Materials (POSM)
Display Tracking
Assortment Tracking
Price Monitoring
Promotion Tracking
IvyEye Image Recognition
Ivy Eye is an Android & IOS application enabling merchandisers to track products on shelves using their mobile devices and sync data for supervisor action.
AI Tech Stack
The Ivy Eye project leverages a sophisticated AI tech stack, utilizing advanced frameworks and models to deliver high-accuracy object detection, image classification, and OCR capabilities.
ML Frameworks
We utilized TensorFlow and PyTorch for building and training deep learning models. These frameworks provided robust libraries and tools for developing and fine-tuning our neural networks, ensuring optimal performance in object detection and image classification tasks.
AI Models
The project utilizes specialized AI models, either pre-trained for relevant tasks like object detection and image classification, or custom-built for specific needs. These models are fine-tuned with Ivy Eye's data to achieve high accuracy in identifying and classifying products on shelves captured through mobile device cameras.
MLOps
To streamline the deployment and maintenance of our AI models, we adopted MLOps practices. This included using Docker for containerization, Kubernetes for orchestration, and CI/CD pipelines for automated testing and deployment. These practices ensured efficient model updates, scalability, and reliability of the AI components within the Ivy Eye application.
There is always a good communication rhythm that happens, and there is a good positive mindset in imbibing this whole agile base of development.
Strategic & Agile Partnership
Successful High-Impact Implementations
Seamless Remote Collaboration & Integration
