Writesof is a young company, a small team of engineers, data scientists and computational linguists, each dedicated to developing advancements in our natural language generation and natural language processing technologies.  Our company’s founders have, for nearly ten years, been developing NLG and NLP applications that use machine learning to understand every possible nuance of human interaction and engagement with unique messages.  Our market objective is to license our software as a service and to also.  Founders and investors also have the luxury of using Writesof technologies in their own businesses.  Initially the company aimed to use our NLG internally, but as things have developed, the founders made a decision in 2018 to offer solutions as a service.  This is aimed to move two areas of our business forward.  First, revenues from client partnerships will serve as funding for furthering our expansion in computing and human resources.  Since we only build unique cloud computing machines, every new client will have the luxury of maintaining their own brand data in a secure client environment.  Then there is thins.  We determined a solution where we do not need access to raw data files.  Our systems do not require API with your raw data.  Instead, your data is processed, not necessarily stored, on your own secure cloud server, through your own connections.  The data is processed on our virtual machines where we extract audience and user behavior insights.  We then pull this data via API or scheduled transfers, into our NLG platform, where a unique machine designed specifically for the brand, begins to process the data scores and begins editing product descriptions.  We do not need access to   commercial possible, among founders and investors.   Five years ago, before Writesof was founded, our team aimed to build a single NLG platform to be used for online retail product and ad generation.  We later recognized that, like human writers, machines can be trained to take on a creative persona, or in our case, a brand voice.  We found that it is much more efficient to train NLG machines by their brand voice, building the system around qualitative linguistic attributes, for example, writing style, message tone, and word choice.  If you take, for example, every word published on Amazon, you would find that there is very little similarity in grammar and diction, even within the same product category.  On the other hand, if you run the same language processing on a site like Neiman Marcus, you would find far greater similarity in the writing style, despite the fact that dozens of Neiman Marcus writers have authored or contributed to the their site’s copy.  Whe, we i eceiving data insights and sending personalized messages.  Our natural language processing tools analyze metric data feeds such as audience insights, traffic data, and user engagement signals.  The system breaks down the data and other key performance indicators by relationships to linguistic attributes.  are broken into pieces  to our natural language processing machines.  These NLP machines use machine learning   produce engaging, informative, and persuasive text Our company is set apart from the other few natural language generation platforms in our unique ability to adapt messaging to certain audience features. At Writesof, our team is focused on physical product description generation that is precision-aimed at user groups and audiences. We call this technology Personalized NLG, language that adapts to audience behaviors, actions and inactions.