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Completed Case Study
Multi-agentic AI inference pipelines for enterprise warehouse demand prediction
Integrated custom neural networks and distributed training nodes with secure storage platforms to predict inventories.
The Challenge
Inflow Warehouse experienced supply chain disruptions and high overhead costs due to unpredictable seasonal inventory demands and manual logistics routing.
Our Solution
We developed a custom multi-agentic AI inference pipeline. Utilizing PyTorch and LangChain, we built collaborative agents that forecast demand, optimize warehouse space, and automate restocking queries.
The Results
Warehouse demand forecasts achieved 98% prediction accuracy. Stockout occurrences were reduced by 60%, and overall operational efficiency improved by 25% within the first quarter of deployment.