Horizontal Use Cases

Automated capabilities to build content specific to different persona and product / campaign combinations.  Then, use vast data sources to determine optimum channel distribution for content, including data aggregation and analysis for continuous improvement. 

Process large datasets, extract insights, and generate human-readable reports or visualizations. Interpret structured and unstructured data (e.g., spreadsheets, text, or images) to identify trends, anomalies, or predictions 

Enhance robotic process automation (RPA) by adding cognitive capabilities, enabling it to handle unstructured data and make context-aware decisions.  

Chatbots and virtual assistants that engage in human-like conversations, answer queries, and perform tasks. These Agents can handle complex dialogues, provide recommendations, or execute commands across platforms. 

Assists in writing, debugging, and optimizing code, making it a powerful tool for developers and non-coders alike.

Analyze old codebases (e.g., COBOL, Fortran, or early Java) to generate detailed documentation, flowcharts, and summaries in natural language. Agents use natural language processing (NLP) and code comprehension models to reverse-engineer applications, identifying business logic, data flows, and integration points. 

Automates the conversion of legacy code into modern languages or frameworks, such as translating COBOL to Java, C++ to Python, or monolithic apps to microservices. Refactors code to improve readability, performance, and maintainability, addressing technical debt caused by outdated or inefficient coding practices. 

Generate test cases, simulates user inputs, and validates outputs, reducing the risk of errors during migration. It also identifies edge cases that human testers might overlook. 

Enhances knowledge bases by summarizing information, answering queries, and organizing unstructured data (e.g., internal documents, wikis, or emails). The Agent acts as an intelligent search engine that understands context and intent.