Requirements Engineering

Customer requirements documents are extracted, classified, and validated against standards to reduce manual effort and errors.

Business impact

• Manual effort in requirements extraction reduced by up to 8x — AI automates extraction and classification, drastically cutting manual workload and errors
• Engineering efficiency improved by 20% — AI copilot reduces inconsistencies and eases engineers' workload, boosting productivity
• Time to market accelerated — Faster requirements processing shortens development cycles and speeds product launches

Data requirements

• Unstructured customer requirements documents (Text) — Primary input for AI to extract and classify requirements from natural language text
• Standards and ISO norms documentation (Text) — Used to validate and check requirements compliance automatically
• Historical requirements and feature mappings (Structured) — Reference data to map new requirements to existing features and detect duplicates

AI methods and techniques

• Generative AI — Generates and refines requirements documents and checks compliance with standards
• Predictive AI — Predicts classification labels and maps requirements to existing features

AI models and model families

GPT-4o, Claude, Llama

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: Continental Automotive, German OEM.