Include Sherpa.ai Recommendation and Predictive AI in your products and services and personalize the information displayed and offered to users
Sherpa.ai Recommendation and Predictive AI is a service for creating recommendations and making predictions, using general purpose technology. With a selection of prebuilt domains, as well as the ability to develop private, custom domains that can be integrated with any model, it provides real business value by allowing you to anticipate users' needs.
With Sherpa.ai, you can build ad hoc product and service recommendations and parameterize your business rules. Thanks to Sherpa.ai’s general purpose technology, you can build recommendations within your catalog of products and services, such as movies, music, news, books, or clothes.
Sherpa.ai provides a collection of domains for common case studies, with its own data and content. This functionality allows you to incorporate recommendations from Sherpa.ai’s collection of domains and integrate them into your operational systems. Employing our domains and selected content gives you the ability to offer users taste- and location-based recommendations about:
Use Sherpa.ai to predict the next position of a user, based on their daily routine. With Sherpa.ai’s context-aware location prediction technology, you can deduce points of interest (Home, Work, or different Leisure points of interest) using historical and habitual information, allowing you to make predictions based on user location.
Sherpa.ai Recommendation and Predictive AI allows the automation of the end-to-end process for building, deploying, and maintaining Artificial Intelligence at scale in businesses systems and devices. The ability to choose from prebuilt domains, as well as create private, custom domains, makes it easy to adapt to fit different business needs.
Sherpa.ai Recommendation and Predictive AI is based on hybrid models that have been developed to combine Machine Learning, Bayesian, and Computational techniques, such as Neural Networks, Random Forest, and Latent Variable Models. World-renowned market analysts, including Gartner and CB Insights, consider Sherpa.ai to be one of the leading companies in the virtual assistant and Artificial Intelligence sectors.
By working with information provided by users, Sherpa.ai Recommendation and Predictive AI becomes more precise, as more data is added. In order to give companies the ability to provide helpful recommendations and predictions, while also preserving their data and their users, Sherpa.ai allows full ownership of customer data.
By using Sherpa.ai Custom Content Recommendations, a television service provider that offers cross-device viewing options can add value to their business through the creation of personalized recommendations for their users. If a user tends to watch the local news on weeknights, sitcoms on Friday evenings, and a mix of documentaries and international movies on the weekends, then the television service provider can use Sherpa.ai to determine what programs to suggest, based on the user profile and their viewing habits.
By using Sherpa.ai Prebuilt Recommendations, a personal computing device company with users across multiple devices and platforms can add value to their business through the creation of personalized news recommendations. If a user tends to read national and international news first thing in the morning on their smartphone, sports news on their computer at lunchtime, and cultural articles in the evening on their tablet, then the organization could use Sherpa.ai to determine what articles to suggest, based on the time of day, location, their reading habits, and the device they are using.
By using Sherpa.ai Next Place Prediction technology, an automotive brand with entertainment systems installed in their vehicles can add value to their business through the creation of personalized, predictive, in-car suggestions for their users. If a user tends to travel to certain destinations at specific times, the in-car entertainment system can use that knowledge to predict the destination and directions. For example, if a user who lives in Sunnyvale, CA tends to go to the gym on Tuesdays when it is rainy, then upon starting their car around 6 PM on a rainy Tuesday, the car could display a predictive suggestion for the gym.