Feature Scene is a comprehensive analytics platform that leverages PostHog, Neo4j, and AI to transform user behavior data into actionable product insights and feature recommendations.

This tool automatically analyzes user interactions, identifies pain points, and generates AI-powered recommendations for product improvements. It integrates seamlessly with your existing workflow through Jira and provides a user-friendly dashboard for visualizing insights.

• Comprehensive Event Tracking: Captures all user interactions via PostHog
• Graph-based Journey Analysis: Uses Neo4j to model and analyze user paths
• AI-Powered Insights: Leverages LLMs to generate actionable recommendations
• Automated Pain Point Detection: Identifies drop-offs, confusion patterns, and underused features
• Jira Integration: One-click ticket creation for recommended improvements
• Batch Processing: Daily analysis for scalable, cost-effective insights

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   PostHog API   │────▶│  Data Pipeline  │────▶│     Neo4j       │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                               │                          │
                               ▼                          ▼
                        ┌─────────────────┐     ┌─────────────────┐
                        │   AI/LLM API    │     │  Graph Analysis │
                        └─────────────────┘     └─────────────────┘
                               │                          │
                               ▼                          ▼
                        ┌─────────────────┐     ┌─────────────────┐
                        │    Dashboard    │◀────│ Insights Store  │
                        └─────────────────┘     └─────────────────┘
                               │
                               ▼
                        ┌─────────────────┐
                        │   Jira API      │
                        └─────────────────┘

• Backend: Node.js, TypeScript, Express
• Database: Neo4j Community Edition
• Frontend: React, TypeScript, Vite
• Analytics: PostHog
• AI: OpenAI GPT-4 (configurable)
• Integrations: Jira Cloud/Server

• Node.js 18+ and npm
• Neo4j Community Edition
• PostHog account (cloud or self-hosted)
• OpenAI API key
• Jira account (optional)

1. Clone the repository:

git clone https://github.com/lanemc/feature-scene.git
cd feature-scene

2. Install dependencies:

npm install

3. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration

4. Start Neo4j database

5. Run database migrations:

npm run migrate

Start the development servers:

npm run dev

This will start:

• Backend API server on http://localhost:5000
• Frontend dashboard on http://localhost:3000

Run tests for all workspaces:

npm test

npm run build
npm start

See .env.example for all available configuration options.

The analysis pipeline runs on a configurable schedule (default: 2 AM daily). Adjust the BATCH_SCHEDULE_CRON environment variable to change the schedule.

The backend exposes the following endpoints:

• GET /api/insights - Fetch latest insights
• GET /api/insights/:id - Get specific insight details
• POST /api/insights/:id/jira - Create Jira ticket from insight
• GET /api/analytics/summary - Get analytics summary
• POST /api/batch/run - Manually trigger batch analysis (admin only)

Please read our contributing guidelines before submitting pull requests.

This project is licensed under the MIT License.