Custom Machine Learning for Creators

Machine learning models tailored
to your audience.

What we do

Give your content that expert-edge and drive more quality links month-over-month with our custom Machine Learning services.

Content only works if it's visible. Our turnkey data analysis and custom algorithms can help create shareable content that’s both visible and engages your audience. From facial similarity simulations to real estate text analysis tools, our machine learning models can be tailored to your needs to take your content to the next level. We can even create custom models regularly to keep your audience constantly engaged and returning for more.

Examples of our work

5

If you work with any kind of content at all for any reason, that alone is a good reason to use machine learning. Not sure what to do? We can help brainstorm.

4

If your content is stale, predictable, or unengaging, bolster it with insights from machine learning.

If your content is about health and wellness, use machine learning to explore new diets and foods that are trending and health concerns people are talking about.

3

If your content is to sell a product, use machine learning to gather more insight about your target audience or competitors.

2

If your content is about food, use machine learning to determine unique preferences, cultural nuances, and other insightful consumer data.

1

5 Examples of when to use machine learning for content

Sometimes services share data that we can use to uncover new insights. For example, Data Brights has used Audio Features by Spotify’s software developer team to match popular songs with the lullabies that they sound like.

Music

We created an analyzer tool to pull text from Zillow property descriptions into the website’s "Zestimate" estimator feature. Our text analyzer allowed the "Zestimate" tool, which only analyzes numerical values for its calculation, to be able to also read non-numeric text. Text descriptors like renovation status, for example, could then be accounted for in the adjusted estimated property value.

Real Estate

Data Brights has used machine learning for determining positive and negative sentiments on an array of topics. For example, machine learning has been used for scanning tweets, reviews, and other digital posts to detect complaints and negative feedback about the airline industry, fast food, hotels, and more. Similarly, we have used machine learning to discover positive emotions people express online about particular brands, remote work, and other topics of interest.

Sentiment Analysis