Each of your customers are telling a unique story that you have yet to discover. Writesof’s natural language generation personas are individual virtual machines, tuned to create unique messages that are delivered to an individual (profile) or a user group.  Each of our over 100 machines has “learned” a unique writing style.  This writer “persona”, as we call it, is primarily dependent on diction and grammar usage features that are extracted and modeled by processing text from actual human writers.   All of our machines can create narratives aimed at several types of audiences, within their subject and topic expertise.  However, some machines, as a result of their writing style, will generate messages that perform well with some user groups and perform poorly with others.   Each persona therefore has a unique set of writing skills, for example, subject-topic knowledge, persuasion tones and angles, and vocab.  These natural language generation machines are capable of computing, 24/7, millions of relationships between the linguistics and the user.  This enables Writesof to deliver a personalized shopping experience to each and every shopper that visits your websites and channels.

 

There are hundreds of widely varying linguistic attributes and metrics associated with each writer, for example “reading comprehension level”, or “command verb usage”.

 

Our cutting-edge NLG technology utilizes consumer behavior data signals to autonomously train each virtual machine persona to communicate with your audience on an individual basis.  At Writesof, we deliver personalized messaging with user-based precision.  We call it audience-driven natural language generation, but it is actually user-driven, in its most advanced operating state.  With precisely personalized messages ready to be delivered at a moment’s notice, your firm can publish millions of unique goal-oriented narratives, in text or code, that are strategic, descriptive, persuasive, searchable, and quantifiably unique.  In a matter of hours, our system can generate over 100 unique machine personas.  Each persona is trained to model its writing style after the collective written works of one or more human authors or brand content.

Natural Language Generation 2.0
Writesof is an author-tuned NLG System that uses audience data to drive messaging.
Audience data is the fuel of cognitive writing that makes it possible for a machine to learn how to writetells havior to stay on message. , with audience behavior. cognitive audience engagement. NLG 2.0 is a type of AI that adapts to audience feedback, creating hundreds, even thousands of narratives or text iterations. descriptive text iterations. Like other Natural Language Generation systems, Writesof can put repetitive writing tasks on autopilot by using a wide range of data, typically structured data. empowering your organization with communications and messaging, on demand. Time. Resources. Data. These are the greatest challenges for writing departments, the latter being a curve ball that grows larger and complex at an exponential rate. Let’s face it; your data analytics are rich with insight, but putting audience data into action is not exactly a seamless process with text churn and adaptive messaging needs. Yet, your audience is a moving target that never lacks for writing that compels, motivates, or informs.

time-sensitive needs.
Like other NLG systems, this one works best when the desired content output is rich with subject matter data, facts, and figures. Writesof works with both structured and unstructured data sources. We also provide supplemental subject matter and audience-level data as “training” documents. Writesof AI can learn from thousands of demographic and behavioral characteristics, as well as subject-level statistics that train linguistic features of narratives. We provide data, systems, messaging and linguistics consulting, and seamless system integration that enables the client to securely connect their data feeds and structured data files to the Writesof user interface.
1. Personalized Language Generation Fueled by Data
2. Writesof, a Natural Language Generation Platform that transforms data into strategic messaging.
3. NLG 2.0: Audience Data Drives the Message
4. NLG 2.0: Audience Feedback Drives the Message.
5. Cognitive Writing and Machine Learning
6. Compelling Copy With Personalization
7. NLG 2.0 can pinpoint what is working and where there’s room for improvement, tracking message delivery with audience behavior
8. With NLG, machines look deep into audience data and formulates messages that inform, compel, and inspire, on a personalized level.
9. Writing millions of narratives adds new meaning to your writing department.
Time. Data. Resources. Your audience is sitting out there waiting to be informed, compelled, and inspired. Making use of your data and 3d party data to deliver messages that result in action. Leave them alone, and they will be misdirected, opportunities lost to thin air. With recent advances in AI and machine learning, Natural Language Generation is no longer relegated to dynamic templates. Today, NLG can produce narratives that learn from your audience and adapt, on message, to shifts in behaviors and interests. The data part is easy. The human element, however, is the critical limitation. Software that interprets data is only as useful as the writer’s ability to uncover insights and construct narratives that consider the myriad of audience engagement metrics that assign valuation to the overall message, as well as each component of a message. Writesof can easily interpret the KPIs that correlate with every aspect of a narrative, comprehensively, in real-time. Every message delivered to an audience has value. Was there a response? What was the response duration? What was the resulting action by the respondent? How did respondent behavior compare with prior responses? Audience analytics data is like a torrent of water that can’t be contained. Much of this data is not utilized simply because we don’t have the time or resources to interpret it, but with data cognition systems, a type of AI, systems can easily find billions of correlations between the message and the audience. But for a writer to look at billions of correlations on a spreadsheet or dashboard would be an impossibility. This technology gap can only be bridged with AI that not only interprets the data, but also takes on the role of the writer. Otherwise, you would need to sift through mountains of data, one narrative at a time, to formulate an optimal messaging strategy. And if your messaging is time sensitive or market sensitive, you would need to continuously adapt to the audience, analyzing incoming data feeds while simultaneously formulating new messages that meet audience needs. Enter Writesof NLG.

We also use proprietary methods for monitoring product appeal, which our systems can measure in real-time and execute autonomous edits, driven by our proprietary linguistics performance index.
Product pages with established performance benchmarks, such as adjusted sales conversions or bounce rate, are far more useful to our machine learning models. If a product is selling well, for example, our AI will seek to understand how each word published, channel by channel, has influenced the increase in sales, also comparing and validating correlations with other similar language used in other product descriptions published globally. To make this possible, we analyze performance KPIs of millions of product narratives, including ratings and reviews and social media content. The number of words typically used within a product category is actually a small assortment, and incredibly small, when weighted by word-usage frequency. In fact, computational linguistics, when drilled down to audience and subject / topic levels, is not exactly rocket science. it’s just understanding the text ingredients of an e-commerce assortment of brands and products and evaluating for performance. The difficult part, what makes our technology unique, is how we train our machines to make continuous improvements to content by analyzing content performance, with exponentially diminishing human oversight required.

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olny taht the frist and lsat ltteres are at the rghit pcleas. The rset can be a toatl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae we do not raed ervey lteter by ilstef, but the wrod as a wlohe.

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Custom Image Frame with Right Alignment

Aoccdrnig to rscheearch at an Elingsh uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are,

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olny taht the frist and lsat ltteres are at the rghit pcleas. The rset can be a toatl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae we do not raed ervey lteter by ilstef, but the wrod as a wlohe.

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Custom Image Frame with Center Alignment (image as a link example)

Aoccdrnig to rscheearch at an Elingsh uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are,

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olny taht the frist and lsat ltteres are at the rghit pcleas. The rset can be a toatl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae we do not raed ervey lteter by ilstef, but the wrod as a wlohe.

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