When you read the reviews of 5-star rated restaurants you realize that it’s not just the food that matters, it’s also the consistency, the ambience, the service, the price, the experience, even the music and parking – in fact, it’s the whole package that matters. The reason I can say this with confidence is that Bewgle surfaces these insights from customer reviews! Food, in the grand scheme of things, is the most important part of the experience (there’s no restaurant without food, duh). However, it takes an establishment to make the dining experience stand out.
In many ways, the Bewgle machine learning models are the recipes for making our analysis tick. Natural language processing, machine learning models that we build, fundamentally analyze the text and output the answers to questions, the insights, topics, sentiment, adjectives and other key features that we promise to our customers (actually there are multiple models at work, but we can keep it simple here). And yet … a model would not ‘just work’. After all, there are so many real life nuances that are involved in making the model deliver the experience.
Let’s consider these few issues:
Trends change and users start using new words that were neither seen before nor have been in the dictionary. Words like bitcoin, metaverse and phrases like android oreo, true wireless headphones and many many acronyms that we see today would have made no sense a few years ago. Such new phrases will continuously keep coming into vogue. Since we can’t possibly have humans keeping track of all the new words out there, how does a model evolve to learn these new phrases automatically? At Bewgle, we have built dictionary-learning modules that learn new words, borrowed phrases from other languages, n-grams and words that acquire new meaning over time.
As users interact with Bewgle analytics, they will occasionally find some issues and provide us with feedback (after all, no machine learning model is perfect!). How does the model then incorporate the feedback? Unless incorporated, the model will continue to make the same mistake over and over again. Capturing user feedback, implicitly learning from user actions and building a model that is continuously learning and continuously evolving has been one of the key reasons to building Bewgle as a whole product as opposed to building only a model.
How does one provide quality assurance (QA) for the output? After all, a model will constantly come across data that it hasn’t seen before. Since the input is unpredictable, the output too will be unpredictable. Any kind of a QA system needs to address this future quality in addition to ensuring the current quality. Keeping a tab on the quality that we deliver on any unseen data is a challenging task that we have made significant progress on at Bewgle.
What is the right user experience to make the analysis easily consumable and customizable? How does dense data become a visual delight while solving real world problems faster?
If the model is the engine of the car, the systems built around the model are the parts that offer you safety and comfort while keeping the engine running. Bewgle’s innovations and efforts span the entire spectrum to not only deliver the analysis of today but also to evolve and give even better quality analysis tomorrow, in a much faster, customizable, reliable and predictable way.
With a growing number of customers who use our analytics, we see more and more users experiencing the delight of using the entire product and interacting with it.
Talk to us to learn more about how we can solve real world problems for you at info@bewgle.com
