Accelerate yourengineeringrequirementsALMwith AI.

Analyze requirements against industry standards to generate quality scores with detailed feedback, with recommendations for wording enhancements that raise requirement quality scores.

Use a natural language interface for requirements, enabling conversational queries, topic-based searches, summaries, and translations.

AI agents are used for tasks that enable automated processing or deployment, such as uncovering inconsistencies, such as when a requirement contradicts a standard or norm in the knowledge graph, or the generation of requirements and test cases based on text specifications.

Extraction from data sources like PDFs and documents. Databases, etc. Involve regulatory sources, attachments, and other relevant data. Extract, classify, and convert relevant requirements.

Find duplicates easily even if they are written differently. This helps ensure your data stays clean and consistent across all records.

The purpose of a feature box is to provide concise and relevant information, such as key points, quick facts, or important details, in a way that stands out. This helps users easily identify and focus on the most important aspects of the content.

Create your own database objects and display them exactly the way your business needs. Whether you manage customers, technical assets, requirements, risks, products, or any other structured information, you define the data model, fields, and relationships. 

Generate concise synopses of complex, long-running tasks, defects, features, and other work items, highlighting context, key details, status, working notes, and risks.

By avoiding the need to scan lengthy descriptions, comments, etc., engineers and managers can:

Work Item Synopsis is an agent designed to handle the complexity of long-running tasks, defects, and features.

In large projects, work items often span weeks or months, accumulating dozens or even hundreds of comments, updates, and status changes.

The agent generates concise, readable summaries that highlight key context and objectives, current status and progress, risks and blockers, and important notes and decisions.

This helps to:

• Improve team productivity and communication across teams, reducing the need for status meetings and manual updates
• Accelerate workflow through smoother handoffs, making engineering processes more fluid and transparent
• Reduce ramp-up time when switching tasks or joining new initiatives
• Provide managers with clearer visibility into ongoing work, enabling faster and more informed decisions

Ultimately, it improves team efficiency by saving time, surfacing patterns, and identifying risks or dependencies early.

AI-based Test Case Automation accelerates the creation and maintenance of test cases by generating them directly from requirements and system specifications. It ensures better coverage and consistency while reducing manual effort in test design. By continuously aligning tests with evolving requirements, it helps teams improve quality and speed up validation cycles.

• Automatically generate test cases from requirements and changes
• Improve coverage and traceability between requirements and tests
• Reduce manual effort and accelerate testing cycles

Various AI Models & technologies

Leverage switchable AI models that can be dynamically selected or combined based on the task or agent, ensuring optimal performance, flexibility, and control. This allows you to adapt processing strategies on demand.

Link documentation with test artefacts to support traceability between requirements, validation activities, and quality processes

Link documentations, notes, design decisions, and blogs with requirements artifacts in DNG. Wiki flexibility is empowered with configuration management  of ELM

Native Open Services for Lifecycle Collaboration (OSLC) support enables standardised, bi-directional linking between engineering artefacts across tools and platforms.