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Skypoint AI Platform Glossary


It is a group of profiles characterized by a defined set of attributes-based filters. You can schedule audiences to be auto-updated every day or update them manually for one-time use.

Custom Models

The feature allows the user to bring their own machine learning model and utilize the data from Skypoint AI Studio to get the quality score.


Data import, export, connections, types (semantic labels) management, data transformations, and data quality management.

Data Lake

A Data Lake is a repository of data that stores a large amount of structured, semi-structured, and unstructured data.

Data Maps

Mapping different entities in a meaningful manner to formulate better insights with the data. It also provides an easier and powerful way to process, manage and secure the data with minimal complexity.

Data Subject Requests

This allows the customer to create a new Data subject request and shows the DSRs that are currently in review.

Data Warehouse

A data warehouse is a centralized repository for integrated data from numerous source data systems (Finance, Sales, Operations, etc.) for reporting and data analysis.


An all-in-one automation solution that integrates consent and preference management with data residency and privacy protection to ensure compliance with GDPR, CCPA, and other state laws.


Each data source ingested from the dataflows is added as an entity in the entity section. Entities are further added from Stitch Process which is the Profile entity, from Audiences , from Enrichment and from Metrics.


It exports the entities to several destinations using export connectors provided by the platform.


You can consider them as subsidiaries of a single client. As soon as you create an account on the platform, a default instance is created for you namely Sandbox.


Lakehouse brings together the benefits of a data lake and data warehouse to create a new open data management architecture based on Delta Lake.


It defines the profile data in your entities by choosing the profile attributes, primary key, and types (datatypes defined by Skypoint AI).


It identifies the unique profiles in your entities by matching records based on certain rules.


It creates an entity of profile records by combining duplicate attributes and removing attributes you don’t need.


Helps in tracking the performance of an organization by providing insights such as business metrics, profile metrics, and profile attributes.

Skypoint AI Studio

Skypoint AI Studio employs generative AI to collect, integrate, organize, and analyze your data using automated pipelines. By sourcing data from various sources, it uncovers valuable insights that offer users instant answers grounded in industry and organizational context, aiding in informed business decisions.

Skypoint AI Copilot

Skypoint AI Copilot helps users with instant insights about their own organization's data through natural language.


It consists of notifications, schedule, instance, product, activity stream.

  • Tenant : It displays all the basic details of the tenant with all the services assigned to it.
  • Instance : It displays the various details of the instance such as Tenant Name, Tenant website URL, Tenant identifier, Instance name, Instance identifier.
  • Notifications : It shows the notifications about dataflows and background processes. This sub-section helps to cover the area where we need to check the status of an iteration.
  • Schedule : It allows you to set a schedule to refresh all dataflows and autorun platform processes for the selected instance.
  • Activity Stream : It displays all the activity which was performed in the application.


Anticipate customer needs with real-time data and deliver meaningful experiences during every customer touchpoint using custom models and built-in AI.


Profiles depict unified customers. Multiple entities ingested are stitched together with Identity resolution and ML model to generate a 360 view of unique customers.


It is used to create associations/relationships between entities which can be further used when creating Audiences and Metrics.


Establish a single source of truth with our proprietary ML-based identity resolution algorithm that produces rich and precise 360-degree customer profiles.


Data Processing is performed in this section. It consists of three sub-sections: Map, Match & Merge.


It depicts the entire customer's journey consisting timeline details of specific entities and attributes in the customer profiles


Each customer is called a Tenant.