🚀 AI Requirement Intelligence Copilot (MVP)

🧠 Overview

This project is a lightweight AI-powered system designed to transform raw meeting notes into high-quality, structured requirements.

Unlike basic AI generators, this system focuses on:

• Requirement clarity
• Validation
• Risk identification
• Test readiness

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🎯 Problem Statement

In real-world projects, requirement quality is a major bottleneck:

• Ambiguous user stories
• Missing edge cases
• Undetected dependencies
• Rework during development

This leads to delays, misalignment, and inefficiencies.

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💡 Solution

This project introduces a Requirement Intelligence Layer that:

✔ Converts meeting notes → structured user stories
✔ Identifies gaps and ambiguities
✔ Detects risks and dependencies
✔ Generates test cases
✔ Provides estimation with reasoning

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⚙️ Features

• 🧾 User Story Generation
• ✅ Validation Engine (ambiguity & completeness check)
• ⚠️ Risk & Dependency Detection
• 🧪 Test Case Generation
• 📊 Estimation with reasoning

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🏗️ Architecture (MVP)

Meeting Notes ↓ LLM (Local - Ollama / Phi3) ↓ Structured Output ↓ (User Story + Validation + Risks + Tests + Estimation)

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🛠️ Tech Stack

• Python
• Ollama (Local LLM)
• Phi3 Model (lightweight, runs on 8GB RAM)

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🚀 How to Run

1. Install Ollama

https://ollama.com/download

2. Pull model

ollama run phi3

3. Install Python dependencies

pip install ollama python-dotenv

4. Run the application

python ai_ba_copilot.py

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🧪 Sample Input

User should login using email and OTP. OTP expires in 5 minutes. System should handle invalid OTP. Integration with SMS service required.

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🔍 Sample Output

• User Story
• Validation Issues
• Risks & Dependencies
• Test Cases
• Estimation

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🧠 Key Differentiation

This is not just a generator.

It acts as a thinking layer between:

Raw Input → Execution (Devs / AI Agents like Devin)

It ensures: 👉 Better requirement quality before execution

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🚀 Future Enhancements

• Streamlit UI (web interface)
• RAG (context-aware generation using BRDs / past stories)
• Jira / Confluence integration
• Multi-agent workflow

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👨‍💻 Author

Aditya
AI + Product Enthusiast | Building AI-powered BA tools

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⭐ Why this project matters

Improving requirement quality = improving delivery outcomes

This project aims to bridge the gap between:

• Business understanding
• Technical execution

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