A top-5 U.S. restaurant chain ($10+ billion in sales) was growing fast — and so were its wait times at the counter and the drive-thru. They routinely experience massive volume at peak times, during which they need to continue providing the extremely personable, high-touch customer service for which they’re known, even while serving a much higher number of guests.
To keep wait times low and service levels high, the restaurant decided to think outside the box — turning to technology and machine learning (ML). They had already implemented a successful computer vision project (in partnership with phData) to maintain product quality across their hundreds of locations; now, they began to explore how ML might similarly transform the ordering experience.
The idea was to use natural language processing (NLP) to listen to customers’ verbal orders and automatically put them through to the kitchen — automatically transcribing their words and using ML models to interpret the resulting text, translate them into specific order requests according to an index of terms and menu items.
This kind of NLP solution would not only shorten wait times and improve order accuracy, but free up restaurant associates to focus on improving one-to-one service (e.g., by making more eye contact). However, implementing and maintaining such a solution across their hundreds of restaurants — and proving its feasibility within their own business — was going to be a tall order.
To overcome these challenges, the restaurant chain again put their faith in phData; they were confident, based on the success of the computer vision project, that phData’s ML team could bring the required multidisciplinary expertise across NLP, ML, and data engineering.
And the phData ML team delivered. Leveraging the same flexible MLOps Framework they had relied on for the computer vision project, they developed a solution that embeds the NLP software SpaCy into a streamlined Apache Airflow pipeline, with technology-specific configuration best practices and ML engineering standards built-in.
Now the restaurant’s data science team is empowered to configure business rules without worrying about the underlying technology. With a pipeline in place to automate processes like generating SpaCy rules or repopulating Elasticsearch indices, the customer can now self-serve to rapidly iterate through experiments — easily generating indices in order to test and tweak existing rulesets, or to assess brand new ones — until they arrive at the best working solution to accurately interpret and submit customers’ food orders.
With the new automated NLP workflow developed by phData, the restaurant chain has overhauled their tiny NLP prototype into a reliable, repeatable, and highly efficient ML assembly line with the potential to dramatically improve the ordering experience for their customers.