The Challenge
Peak Produce, a regional dairy manufacturer, was struggling with a classic agricultural problem: unpredictable demand leading to massive waste and missed opportunities.
Their existing forecasting methods were based on traditional statistical models that, while functional, were significantly less accurate than modern AI approaches.
The result? They'd either overproduce premium dairy products that would spoil, or underestimate demand and disappoint customers. With dairy's short shelf life and complex production cycles, every forecasting mistake was expensive.
The specific pain points:
Manual demand planning taking weeks to complete
Product waste due to overproduction
No way to optimize production across multiple product lines
Disconnected data sources making decision-making slow and error-prone
The Solution
Phase 1: Process Reimagination + Predictive Analytics
What we built:
Intelligent demand forecasting system that analyzed 2+ years of historical sales data
Web-based dashboard for real-time demand insights and production planning
Multi-agent AI architecture that optimized across the entire supply chain:
Demand analysis agent for pattern recognition
Production facility optimization agent
Sales opportunity identification agent
Production scheduling agent
In phase 2, we expanded the product with a fully automated forecasting pipeline using Azure Data Factory and Databricks, enabling real-time model updates that improved predictions as new sales data arrived. The new prediction data was then available via API-first architecture that integrated seamlessly with their existing systems.
The Tech Stack
FRONTEND
React-based dashboard for intuitive demand planning
BACKEND
Python with advanced ML libraries (NIXTLA, Prophet, scikit-learn)
CLOUD INFRASTRUCTURE
Azure Data Factory, Databricks, and Data Lake Storage
AI/ML
Multi-model forecasting with automated selection and hyperparameter tuning
INTEGRATIONS
REST APIs connecting to existing production systems
The Results
Improved forecast accuracy by 88% compared to their previous statistical models
Enabled the reduction of product waste through more accurate production planning
Enhanced demand prediction reliability across all product lines
Why This Worked
Process-First Thinking Instead of just adding AI to broken processes, we redesigned how demand planning actually works.
Integrated Data Strategy We connected previously siloed data sources (sales, production, inventory) into one intelligent system.
Human-Centered Design We designed the system to provide clear recommendations with explanations, keeping humans in control of critical decisions.
Scalable Architecture We built the solution on cloud infrastructure that grows with the business and automatically improves over time.