By applying rule-based logic to segment messages, ensuring consistent category boundaries for labeling.

By using unsupervised learning techniques that analyze semantic similarity, allowing users to understand latent themes and define relevant business concepts.

By scanning messages metadata for frequency of sender and sorting by timestamp, which helps eliminate duplicates before training.

By comparing messages to an existing labeled dataset to suggest pre-built clusters for rapid deployment.

Machine learning (ML), natural language processing (NLP), and employee-led supervised learning

Generic algorithms, deep learning, and unsupervised learning.

Blockchain technology, artificial intelligence (AI), and supervised learning.

Predictive coding, deep learning, and unsupervised learning.

To enable Robots to communicate with other third-party systems.

To prevent Robots from operating on unsupported applications.

To provide an alternative development environment to Studio.

To manage and monitor Robot deployments and executions centrally.

The AI model continuously learns from the descriptions you submit, allowing it to refine and improve its performance only in real-time sessions.

The AI model continuously learns from the error corrections you make, allowing it to refine and improve its performance over time.

The AI model continuously learns from the descriptions you submit, allowing it to refine and improve its performance over time.

The AI model continuously learns from the workflow executions you run, allowing it to refine and improve its performance only in real-time sessions.