Shaip Cloud Features

Allows administrators to assign role-based user access for specific data sets

Hides and masks sensitive data automatically based on user permissions

Provides historical insights into original data sources and transformations made to data sets

Offers simple search capabilities to discover specific data sets

Allows users and administrators to easily clean data to maintain quality and integrity

Converts data formats of source data into the format required for the reporting system without mistakes

Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Allows users to comment on data sets to help future users better interact and interpret the data

Permits profiling of data sets for increased organization, both by users and machine learning

Creates a business glossary for faster understanding by the average business user

Indexes metadata descriptions for easier searching and enhanced insights

Automates recommendations for users based on machine learning functionality

Offers natural language querying functionality for non-technical users

Cleans data to improve quality via automation

Meets GDPR requirements for pseudonymisation under the Data Protection by Design and by Default requirements.

Meets de-identification requirements under the CCPA.

Offers traditional static de-identification (also known as consistent replacement), where the pseudonymized data uses the same pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox and the Robert Fox name is used multiple times. This type of pseudonymization carries some risks of re-identification if paired with enough datasets.

Offers dynamic de-identification (also known as random replacement), where the pseudonymized data uses different pseudonyms across multiple data sets. For example, John Smith is replaced with Robert Fox once, and then the next time the data is used the name changes to Michael Jones. This type of pseudonymization carries lesser risk of re-identification if paired with many datasets.

Offers methods to de-identify large volumes of data using batch files.

Connects to mobile platforms using a mobile SDK.

Offers APIs to connect products.

Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more.

Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more.

Ensures the data is of a high quality as compared to benchmark.

Gives user the ability to review and edit labels.

Uses models to predict the correct label for a given input (image, video, audio, text, etc.).

Automatically route input to the optimal labeler or labeling service based on predicted speed and cost.

Has the ability to place imaginary boxes or polygons around objects or pixels in an image.

has the ability to detect objects within images.

Track unique object IDs across multiple video frames

Supports a range of different types of images (satelite, thermal cameras, etc.)

Gives user the ability to extract entities from text (such as locations and names).

Gives user the ability to tag text based on its sentiment.

Gives user the ability to label and verify text data in an image.

Allows the user to transcribe audio.

Gives user the ability to label emotions in recorded audio.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Makes informed choices based on available data and objectives