AI-RADA - AI-Powered Requirement Engineering
An LLM-powered requirements analysis platform that classifies, validates, and rewrites raw requirement documents - detecting ambiguity, generating architecture diagrams, and producing executive-level quality reports automatically.
Key Outcome
Automated requirement validation
Raw requirement docs classified, scored, and converted to architecture diagrams without manual review.
Requirement documents handed over by clients are rarely clean. Manual classification of functional vs non-functional requirements, detection of ambiguous "shall" statements, and generation of architecture models is time-consuming and inconsistent.
Built AI-RADA to automatically parse uploaded requirement documents using an LLM, classify each statement as functional or non-functional, flag ambiguous or invalid items with plain-language reasoning, rewrite weak requirements into testable statements, and generate use case diagrams and architecture models in PlantUML or Mermaid - with an executive quality dashboard and analysis archive.
Key Features
LLM-based classification: Functional vs Non-Functional per requirement
Ambiguity detection with a Clear / N/A verdict and plain-language explanation
Requirement quality scoring: total processed, valid baseline, average score, noise level
Verified % donut chart showing overall requirement accuracy at a glance
Automated use case diagram generation: actors, system boundary, use cases
Architecture model export as PlantUML, Mermaid, or vector assets
Requirement rewriting - turns vague statements into well-formed, testable specs
Analysis archive - every document saved with timestamp, restorable in one click
Technology Stack
Project Visuals
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