
Architecting for Change: How We Helped a Leading U.S. Insurer Cut Technical Debt by 97% and Modernize at Scale
Case Study
Restaurants

In order to provide a better customer experience, one of the world’s largest full-service dining companies required a solution that leverages machine learning to predict how long customers will have to wait for their order to be served.
Predicted order wait time with 87% accuracy
Improved customer experience
Increased customer base
Bitwise created a prediction model to help the client give accurate wait times to customers who have ordered food.
test (4)
Strategy to distinguish real-time (high frequency) and static (low frequency) factors
feature (1)
Implementation of caching technique for static factors in DynamoDB
support (7)
Development of statistical standardization methods and advanced feature engineering for online and table-top order segregation
synchronize (1)
Real-time predictions using end-to-end pipelines on AWS Lambda and API gateway
Group 3
Predicted order wait time with 87% accuracy
relationshipCreated with Sketch.
Improved customer experience
target (8)Created with Sketch.
Increased customer base
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