Sapling uses state-of-the-art technology to make the most advanced and relevant suggestions.
For a long time, the only way to provide writing feedback to a large audience was through a prescriptive list of rules.However, it's difficult to strike a balance between high-level principles and low-level suggestions that may not apply in all cases. Language is also extremely complex—it's difficult to come up with a list of all possible cases.
To tackle these issues, Sapling instead takes a descriptive approach by using machine learning. It learns from examples of what the preferred, on-brand writing style is, and then tries to transform writing that users type into that style.
What are the advantages of doing this?
We'll now consider the third point in more detail.
Instead of taking a one-size-fits-all approach, Sapling adapts to each user, across diverse settings. Teams at different companies, in different business roles, and with different audiences should receive feedback catered to their particular company, role, and audience.
A support agent at a hospitality company that takes pride in having a warm customer tone, for example, may want very different suggestions than a representative for a financial services company. By using machine learning, Sapling is able to do this. Sapling can learn different suggestions and feedback for specific use cases and user/audience profiles.
Wanna be casual? Be casual.
We're very excited about the future of AI writing assistants. We believe that it'll be one where suggestions are learned, adaptive, and personalized to different scenarios.
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