Enterprises who have adopted Amazon Personalize solution have seen an uplift in their conversions & revenue. You can find case studies on Amazon Personalize here: https://aws.amazon.com/personalize/customers/

Recommendation engines are not optional in the new millennium. Every business can benefit from a retrofitted, scalable customer engagement platform. While building your own recommender system to deliver the individual customer can be costly and time-consuming, we make it simple and affordable.

Systech’s Recommendr offers organizations an advanced and sophisticated recommendation engine powered by Amazon Personalize that enables elevated user interaction, enriched shopping potential, enhanced customer engagement, and converted and satisfied customers.

Implementation begins through the construction of data pipelines to capture your enterprise’s internal and external data, from multiple data sources. This data is used to train (& retrain) the Amazon Personalize model using our on-and-offline metrics. Once completed, the data pipeline and model can be deployed and integrated with your other applications. The solution thereafter will produce Amazon Quicksight dashboards that allows you to monitor solution performance, and incorporate post-production, self-learning capabilities to adapt to changing user engagement.

Our end-to-end architecture not only integrates Amazon Personalize with CDP solutions, but also is equipped with Systech’s deep ML capabilities and experience to take the “guess,” out of “guessing” how to get somewhere profitable. This offering offers a low-cost entry point, hands-on experience from a member of our Systech Recommender team, and optimal results (equipped with Machine learning Models with Hyper Parameters).

Try our Recommendr POV to get hands on experience with the product. Once you are satisfied with the POV, we can operationalize it for you through this product.

Please click here for more information on our POV.

Recommendr Implementation includes…

• Implementation of POV:

• Refine POV Use Case
• Deploy Data Engineering in production
• Implement Feature Engineering in production
• Map Data Elements to Personalize Schema
• Feed Data into Personalize
• Deploy Model in production mode
• Integrate model predictions with website