Cleanlab Features

Correctly identify inaccurate, incomplete, or duplicated data from a data source.

Utilize deletion, modification, appending, merging, or other methods to correct bad data.

Standardize data formatting for uniformity and easier data usage.

Clean data as it enters the data source to prevent mixing bad data with cleaned data.

Finds duplicates using the fuzzy logic technology or an advance search feature.

Receive data and information in real time

Provide follow-up information after data cleanings through a visual dashboard or reports.

Automatically run data identification, correction, and normalization on data sources.

Schedule automated audits to identify data anomalies over time based on set business rules.

Gives a view of the entire data quality management ecosystem.

Allows user role-based access and actions to authorization for specific tasks.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Use AI to generate content in the form of text, images, videos, etc.

Enables smart selection of data for annotation to reduce overall training time and costs.

Allows for automatic retraining of models with newly annotated data for continuous improvement.

Facilitates the setup of an active learning process tailored to specific AI projects.

Allows users to establish a feedback loop between data annotation and model training.

Provides the ability to identify and address edge cases to enhance model robustness.

Enables efficient triaging of training data to identify which data points should be labeled next.

Streamlines the data labeling process with tools designed for efficiency and accuracy.

Automates the detection of anomalies and outliers in the training data for correction.

Offers tools to optimize the selection of data for labeling based on model uncertainty.

Provides actionable insights into data quality, enabling targeted improvements in data labeling.

Delivers in-depth insights into factors impacting model performance and suggests enhancements.

Enables model improvement at the lowest possible cost by focusing on the most impactful data.

Integrates the handling of edge cases into the model training loop for continuous performance enhancement.

Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases.

Offers advanced tools to analyze label outliers and errors to inform further model training.

Indexes metadata descriptions for easier searching and enhanced insights

Provides a channel for team members to share media files, communicate, and work together

Search and filter data across systems to locate required information by entering keywords or certain criteria

Create, design and manage workflows for repetitive tasks

A virtual assistant that uses AI to pursue goals and complete tasks on behalf of users

Set up connections to third-party platforms to improve business processes

Synchronizing data between two or more devices/systems and automatically updating changes to maintain consistency

Import and export data to and from software applications

Rules followed by the software/AI can be configured by users to suit their specific needs

Verify, organize, manage & store enterprise data assets in a centralized repository

Observe and track the demand, usage, progress or quality of a system, product, or user

Translate EDI formats into data suitable for use with company applications.

Allows users to manage data from a number of sources

Online portal through which end users can access the system, manage tasks, or obtain information

A collection of customer information such as contact details, demographics, previous interactions, etc.

Process of checking different types of data for accuracy to avoid errors or inconsistencies

Move from one database to another, or upgrade the version of database software being used

Manage and support multiple languages

Allow users to access multiple services after entering their login credentials once

Compare new data with existing records to detect duplicate entries

Extract email addresses from multiple data sources

View and track pertinent metrics to find patterns and gain insights from data

Examines, analyzes, and organizes data to create relevant summaries or graphs.

Automatically retrieve and pull information from documents, websites, images, data sets, and other sources

Track the management and flow of data throughout the organization

Verifies and confirms the accuracy of an address

Looking at data from different sources and finding possible duplicates, or identical matches

A set of indicators that tracks the performance of networks, applications, systems, teams, etc.

Interact with data visualization elements, such as charts and graphs, to drill down into data

Track revisions and updates made to files and navigate between different versions

Application programming interface that allows for integration with other systems/databases

Import, collect, and capture data from multiple sources

Define levels of authorization for access to specific files or systems

Track and manage adherence to policies for any service, product, process, or supplier

Discover and connect variety of data sources to the application for analysis