Manual Effort

The automated approach reduces the manual effort required to generate, validate and check the product descriptions and ensures that the resulting copy doesn’t have any grammatical or spelling errors. A trained NLP model generates the descriptions using only the product images as inputs, by detecting the key features of the product and describing them appropriately.

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Types of


The model has been trained to generate different types of copy for the product, including its name, a short description and a longer description that builds on top of the short description by elaborating on the different elements captured in the short description. This ensures that all product related copy on the site is generated automatically and increases the speed with which the client can refresh its online inventory.



As the model was trained on the existing samples of the merchant’s product descriptions, it ensured that the automated descriptions were in line with the merchant’s style guide and brand positioning; this reduced the time and effort spent on onboarding new copy writers. Existing manpower was deployed to reviews and to generate copy for products that the model wasn’t yet trained on.

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Deep-learning Model

A bespoke deep learning model, pre-trained on ImageNet, was used to learn the product descriptions associated with product images.

Natural Language Processing

NLP was leveraged to learn to describe the extracted product features and stitch together the feature descriptions into the product copy.


The resultant TensorFlow model was deployed to the customer’s on-premises infrastructure and the generated descriptions were pushed to their website, deployed on the cloud.

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