Built an AI personalization and operational intelligence layer across a multi-location restaurant chain.
- Personalization
- Across digital channels
- Operational insight
- Unified
- AI foundation
- Scalable architecture
What we shipped
A personalized menu recommender plus a unified operational analytics layer across POS and customer data.
Restaurant chains face rising pressure to deliver personalized customer experiences while running efficient operations across many locations. Most groups rely on traditional POS systems and manual processes.
Blueprint → AI Pilot → Production launch → Scale and operate.
We followed the Datablooz Delivery Model. See our process.
- Blueprint
Worked with executive leadership to define high-impact AI areas across customer experience and operations.
- AI Pilot
Built the personalized menu recommender on historical order data and behavioral signals.
- Production launch
Deployed recommendations into digital ordering channels and a centralized data analytics layer.
- Scale and operate
Extended to demand forecasting, dynamic pricing, kitchen optimization, and voice ordering.
Business, technical, and governance outcomes.
- Personalized menu recommendations per customer.
- Unified operational insights across locations.
- Data-driven marketing and menu strategy.
- Scalable AI foundation for future capabilities.
- Python
- Scikit-learn
- Spark
- Snowflake
- Airflow
- React
Centralized data pipelines with versioned models, analytics dashboards, and data quality monitoring across locations.
Working on something similar?
Schedule a call. We will tell you honestly whether AI is the right move.
Reference calls available under NDA after the second working session.