BRDGenie – AI-Powered Requirements Engineering Assistant

BRDGenie is an AI-driven Business Requirements Engineering Assistant designed to transform unstructured stakeholder inputs into structured, professional Business Requirements Documents (BRDs).

Modern software teams struggle with scattered emails, Slack logs, meeting transcripts, and vague stakeholder notes. BRDGenie solves this by using Large Language Models (Groq LLaMA 3.1) combined with structured dataset validation to automatically extract, classify, validate, and organize requirements into a complete BRD.

 Smart Document Ingestion

• Upload PDF, DOCX, or TXT files

• Extracts messy stakeholder conversations

• Handles meeting transcripts, emails, and chat logs

 AI-Powered BRD Generation

• Generates structured Business Requirements Document including:

  • Executive Summary

  • Objectives

  • Stakeholders

  • Functional Requirements

  • Non-Functional Requirements

  • Assumptions

  • Constraints

  • Risks

  • Success Metrics

 Intelligent Requirement Classification

• Automatically categorizes:

  • Functional Requirements

  • Non-Functional Requirements

• Uses Groq LLM + Kaggle dataset validation layer

 Conflict Detection Engine

• Detects:

  • Timeline mismatches

  • Contradicting performance requirements

  • Technology stack conflicts (e.g., SSO provider differences)

• Flags inconsistencies before development begins

 Traceability Matrix

• Auto-generates requirement IDs (FR-01, NFR-01)

• Maps each requirement to its source document

• Enables structured traceability

 AI Editor

• Natural language BRD updates

• Allows project managers to refine requirements dynamically

 Smart Dashboard

• Displays:

  • Total requirements

  • Functional count

  • Non-functional count

  • Conflict count

• Provides AI-based insights and validation alerts

  🏗 Architecture

  User Upload
  → Text Extraction
  → Groq LLaMA 3.1 AI Analysis
  → Kaggle Dataset Classification Layer
  → Conflict Detection
  → Traceability Mapping
  → Structured BRD Output

  This hybrid AI + Knowledge validation system reduces hallucination and improves reliability compared to pure LLM-based solutions.