Client: Multinational Industrial Services Provider
Partner: Ryse Technologies
Solution: AI-powered Bill of Materials (BOM) Forecasting Platform
The Challenge
Planning high-volume, equipment-intensive turnarounds is risky when:
- Lead times exceed 30 weeks
- Projects span 1000+ SKUs
- Only 400 past jobs exist for training
- Data is siloed across Salesforce + homegrown trackers
Traditional ML models failed due to data sparsity, complex BOMs, and fragmented systems—resulting in costly last-minute orders and overstock.
The Solution
Ryse Technologies implemented an Azure-native AI platform combining a fabric-based data lake, fine-tuned LLM, and retrieval-augmented generation (RAG) to deliver daily demand forecasts.
Key Components:
- Data Ingestion: Azure Data Factory, Databricks
- Storage: Fabric Lakehouse + Azure SQL
- AI Intelligence: Fine-tuned Azure OpenAI model
- Orchestration: Azure Functions, Power Automate
- Visualization: Power BI inventory heatmaps
How It Works:
1. Unified Salesforce and historical BOM data into a curated lakehouse
2. LLM interprets upcoming opportunities + auto-generates draft BOMs
3. Daily Azure Functions update 12-month SKU-level demand curves
4. Power BI alerts procurement when items near safety thresholds
Results & Impact
Qualitative Wins:
✅ A single, trusted forecast used by Sales, Ops & Procurement
✅ LLM + RAG outperformed ML in low-data, high-complexity environments
✅ Future-proof Azure stack that scales across facilities
What’s Next (Phase 2+)
- Feedback-driven model tuning (Git-integrated coaching)
- Warehouse space forecasting
- Sentiment-adjusted deal probabilities
- Photo & diagram ingestion
- AI-driven procurement suggestions
Takeaway
When data is shallow but context is rich, LLMs + RAG on Azure can outperform traditional AI—delivering smarter, leaner, and faster decisions across the enterprise.
