Client: Australian multi-brand retail group
Sector: Retail / Consumer Goods
Engagement Type: AI & Data Platform Build + Strategy

Challenge

The client was experiencing inefficiencies in demand forecasting and inventory allocation across 70+ stores nationally. Their legacy systems relied heavily on lagging indicators, resulting in frequent stockouts, markdowns, and missed revenue.

Cavari’s Solution

We designed and implemented a real-time AI analytics engine powered by streaming data from POS systems, online sales, supply chain logistics, and external signals (e.g., weather, events).

• Deployed a cloud-native streaming architecture using AWS Kinesis and Apache Flink.

• Trained custom machine learning models to predict SKU-level demand by store with 92%+ accuracy.

• Developed interactive dashboards for executives and merchandising teams, enabling near real-time decisions.

Results

• 14% reduction in stockouts across key categories

• 21% improvement in inventory turnover rate

• Estimated $4.2M in revenue uplift within 9 months

• Established internal AI capability via co-building with the client’s data team.