Case Study

AI-Powered Sales Forecasting Platform

Leveraging Azure AI and agentic architecture to transform pharmaceutical sales forecasting through natural language interactions.

Project Overview

Our client, a leading pharmaceutical company, needed to modernize their sales forecasting capabilities. The solution leverages multiple LLM models hosted in Azure AI foundry, with TimeGEN GPT at its core for processing dynamic time series data based on natural language queries from business executives and data scientists.

Key Challenges

  • Complex sales data across multiple product lines and regions
  • Need for real-time forecasting based on dynamic market conditions
  • High latency and load on production databases
  • Limited accessibility of insights for business users

Results Achieved

43%

Increase in Forecast Accuracy

90%

Reduction in Processing Time

24/7

Automated Data Refresh

100%

Business User Adoption

Technical Solution

Azure AI Integration

Implemented TimeGEN GPT models hosted in Azure AI foundry for advanced sales forecasting capabilities.

Data Architecture

Established nightly data sync from on-premises SQL to Azure SQL using Power Automate with comprehensive monitoring.

User Interface

Developed a Power Apps chat interface with persistent history stored in SharePoint lists for continuous improvement.

Agentic Architecture

Evolved from single AI process to multi-agent system using Power Automate flows for enhanced accuracy and reliability.

Implementation Journey

Key Learnings & Evolution

The project's most significant evolution came with the transition from a single AI process to an agentic architecture. Initial deployment in November 2023 revealed challenges with prompt stuffing and model hallucinations. The introduction of new models and agentic architecture concepts enabled a rapid transformation over just a couple of sprints.

Architecture Improvements

  • Split responsibilities across multiple specialized agents
  • Implemented reusable Power Automate flows for AI model interactions
  • Created centralized services for prompt catalogs and logging
  • Established comprehensive chat history tracking

Data Flow Architecture

  • Nightly data refresh from on-premises SQL to Azure SQL
  • Automated email notifications for job status and errors
  • SharePoint integration for chat history and observations
  • Optimized data staging to minimize production impact

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