NLP Based PLU Automation

Case Study

BeerBoard – NLP Based PLU Automation


US-based Beverage Enterprise


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Business Challenges

The client synthesizes Poured and Sold data available in a bar and give insights for their customer through SmartBar app. Due to unavoidable reasons, on a regular basis, the PLU(Product lookup) available at POS and SmartBar is not in Sync, which is leading to erroneous capturing of sold data in SmartBar.

Our Solution

We proposed to build an NLP based Machine learning algorithm which will identify incorrectly mapped PLU at POS and do correct mapping of PLU in SmartBar. It identifies the mismatches and
automatically suggests the correct mapping and sent will for experts review

Salient Features

  • NLP (Natural Language processing ) technique in identifying Miss mapped Products
  • The Algorithm gives 80% of detection accuracy,
  • Algorithm improves the accuracy by retraining using historic data.
  • Reduces human intervention by 10x times
  • Adaptive learning from experts correction.

Product / Service Description

We Built a model using machine learning algorithms and NLP Techniques. An application was developed in Python for Syncing PLU between POS and SmartBar & deployed on AWS for scalability. The Algorithm gives 80% of detection accuracy, and it can be further improved over some time by training the algorithm.


Highly accurate results

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