Avada Landing Product
Avada Landing Product

Recommender

The ecosystem.Ai Recommender Module is the essential package for any size business. Packed with all the Business and Data Science functionality you need to begin building your very own human centric recommenders.

Don’t get lost using rudimentary recommenders that don’t account for the human in your system. Use the Recommender Module to always make the right offers to your customers, in a way that best suits them.

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Description

The ecosystem.Ai Recommender Module is the essential package for any size business. Packed with all the Business and Data Science functionality you need to begin building your very own human centric recommenders.

For the Business user: Create offer recommender unlike any other, using customer behavior, human sciences, and other sophisticated pre-built elements, in order to truly personalize customer offerings. Create engagement recommenders that find the right message for each of your customers, rather than choosing a ‘one-message-fits-all’ approach.

For the Data Scientist: You can create different models for more accurate recommendations, such as multi-model and single-model configurations. You can also experiment with your recommenders, testing out a host of different models in one recommender, stringing multiple recommender together, and so much more!

About The Recommender Module

  • Access to the full ecosystem.Ai SaaS Platform – The Workbench, Notebooks, Dashboards, Server, and Client Pulse Responder[s].
  • Selection of pre-configured recommenders for you to use, edit, and replicate
  • Comes in four sizes*
  • Pay as you Predict pricing structure, based on size and usage.
  • Build your own recommenders, use pre-built models and configure your own deployments.

Don’t get lost using rudimentary recommenders that don’t account for the human in your system. Use the Recommender Module to always make the right offers to your customers, in a way that best suits them.

 

What you get

  • Full No-Code functionality with the ecosystem.Ai Workbench to:
    • Create and edit any recommender project
    • Add, edit, ingest and view your own data
    • Build and test your recommenders in pre-deployment environments
    • Create recommender configurations and tweak in real-time
  • Full Low-Code functionality with the ecosystem.Ai Notebooks to:
    • Do all of the above, and
    • Test out recommender configurations with Simulations
  • View Real-Time results of any project [simulations and deployments] using the ecosystem.Ai Grafana Dashboards
  • View and analyse Business tracking based results using the ecosystem.Ai Superset Dashboards
  • Run all recommenders, with Behavioral predictions, in your chosen production environment using The Client Pulse Responder
  • Links to Get Started lessons and other documentation to help you along your journey

 

*Size details:
  1. A Small instance is good for running low computational requirement projects. Such as small customer-base testing, single recommenders, and performing enrichments on small datasets. Specs: 4CPU + 8g RAM + 32 Storage
  2. A Medium instance is the perfect size for most uncomplicated deployments. Using this you can run accurate real-time deployments and monitor comfortably, perform enrichments on most average sized datasets, and run up to three experiments / recommenders / builds at the same time. Specs: 8CPU + 16g RAM + 128 Storage
  3. Large instance is better for most above average data science requirements. With the large, you can perform enrichments on almost any sized datasets, and deploy multiple recommenders and experiments at once. Specs: 16CPU + 42g RAM + 128 Storage

Base price here is an estimate, calculated according to a Small instance with 50/50 training and deployment time per month.

For more information, contact our Sales Guru Rob here

Additional information

Size

Small, Medium, Large

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