"Works just like a custom Machine Learning model... without the tedious training and fine-tuning"
This food discovery startup was rapidly gaining popularity with GenZ, thanks to a novel navigation approach and viral customer acquisition on platforms like TikTok.
They reached a tipping point for content management and needed a more automated data pipeline to expand their national coverage and speed up business growth.
They evaluated three different options to identify and extract relevant text.
Option 1 - build custom code that would apply rules based on certain food-related keywords.
Option 2 - label data and generate a custom ML model.
Option 3 - leverage a third party NLP tool like ENTITYZE.
Option 1 and 2 were deemed too long and costly. Only ENTITYZE could fulfill some of the business requirements, thanks to the off-the-shelf ability to assign accurate meaning and context to text. Other NLP tools would in fact have required a similar effort as in option 1.
In a mere matter of days, Entityze set up a secure API endpoint allowing them to run custom text analysis and extraction rules.
From survey verbatims to call center transcripts and social media conversations, our team has been at the forefront of text analytics for almost two decades.
Over the last few years, we focused our efforts on the puzzling problem of word sense ambiguity and entity linking - which in turn, led to the development of technology that can process and organize large volume of unstructured text data such as Wikipedia. It eventually became apparent that these were in fact the two sides of a same coin...
ENTITYZE brings you the best of two technological worlds: on one hand, text analytics to bring out accurate, detailed meaning and on the other hand, a relationship graph that links and organizes this meaning.
ENTITYZE is headquartered in New York City, with team members in two slightly more exotic locations.
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