AI/ML Promotions Platform Engineering
The client is a New York-based decision science and data management solutions company that offers insights and intelligent platforms for retail establishments. The company has helped many retail customers by empowering them with actionable customer-centric insights for effective decision-making.
The challenge was to create an AI/ML-based platform enabling predictive analysis and forecast of sales for a retail store's items. This platform aimed to enhance inventory management, resource allocation, and overall strategy but demanded expertise in predictive analytics, AI/ML, and real-time promotion assessment. Key tasks included processing historical data, sales forecasting, and delivering actionable insights to plan promotions effectively.
Our AI/ML experts successfully created a robust data flow and processing pipeline for the solutions. The machine learning engine, we helped the client build, accurately calculated baseline and forecasted sales in relation to promotions, enhancing their sales strategy significantly. Additionally, our meticulously architected AI pipeline improved the quality, security, operating cost, and monitoring aspects of their operations. Lastly, our solution streamlined financial reporting, providing clear and accessible reports on the UI console and facilitating efficient management reviews. These achievements have not only met but exceeded the client's business goals, resulting in a transformational impact on their overall operations and decision-making processes.
Zymr's solution was a comprehensive AI-native application, incorporating the entire AI MLOps lifecycle. Hosted on Azure, it harnessed AI capabilities like time series forecasting, data classification, and other ML functions to accurately predict baseline and forecasted sales for our clients. Our data science team closely collaborated with the client, gaining a deep understanding of their business objectives. They then developed an ML model in consultation with the client's business stakeholders. Simultaneously, our MLOps team skillfully architected a data ingestion pipeline, storing raw, feature-engineered, and golden datasets in a SQL database. Utilizing classification algorithms, the data was categorized based on sales volume, distinguishing between fast-moving and seasonal items. For forecasting, we employed time series algorithms like ARIMA. This comprehensive solution empowered our clients to make data-driven decisions and optimize their business strategies effectively.
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