Why Levity models work with unstructured data
When we talk about structured, or tabular data, the name gives it away - it's relating to data that comes in easy-to-search formats that generally resides in databases. Usually in text format, with certain structures in place - dates, phone numbers, customer names or addresses, or even transactional information.
Example: When you search the web and get results such as opening hours or reviews in a neat little callout box, these rich results are an example of structured data.
Even though our website may give the impression that Machine Learning is the answer to every problem that exists - a word of caution. If you can write rules for tasks, you probably don't need to add Machine Learning to your processes, and are much better served by RPA tools.
If that is the case, we suggest that you take a look at Google AutoML Tables - it may be a bit less intuitive to use, but in the long run will benefit you much more.
Here at Levity we are focusing on unstructured data, such as text, images, or documents, and automating that extra layer of cognitive element. A typical application would be sorting data into pre-defined ‘buckets’ and setting up workflows for what should happen depending on the outcome. In case you haven't seen this yet, here are some examples of how other companies are using Levity.