The AI-Powered Relational BI Dashboard for Modern Creatives & Retailers
Uncover hidden connections in your data with a foundation model built for relational intelligence.
Hyperelational is a next-generation Business Intelligence platform that goes beyond traditional dashboards. Powered by KumoRFM, our proprietary relational foundation model, it analyzes deep, complex relationships within your data to provide predictive insights that were previously impossible to surface.
This isn't just about visualizing numbers—it's about understanding the intricate web of connections that drive your business. Whether you're in fashion e-commerce or the music & creative industry, Hyperelational offers a tailored experience to help you make smarter, data-driven decisions.
- Natural Language Queries: Converse with your data using plain English
- Predictive Query Language (PQL): AI-generated queries with confidence scores
- Agent-Based Workflows: Deploy autonomous AI agents for complex business tasks
- Predictive Analytics: Forecast customer churn, sales trends, and market opportunities
- 3D Relational Graph Explorer: Visualize complex data relationships in an interactive universe
- Dynamic Audience Builder: Create hyper-specific segments using AI predictions
- Fashion E-commerce: Customer LTV, churn prediction, trend forecasting
- Music & Creative: Fan engagement analysis, tour planning, merchandise insights
One-click connections to your favorite platforms:
- Shopify, WooCommerce, Magento
- Google Analytics, Facebook Ads
- Stripe, PayPal
- Spotify for Artists, Bandcamp
graph TD
A[Frontend - React/TypeScript] --> B[Supabase Backend]
B --> C[PostgreSQL Database]
B --> D[Edge Functions]
D --> E[KumoRFM API]
D --> F[Groq LLM]
B --> G[Authentication]
B --> H[Real-time Subscriptions]
Technology Stack:
- Frontend: React 18.3, TypeScript 5.8, Vite 5.4
- UI: shadcn/ui, Tailwind CSS, Framer Motion
- Visualization: Recharts, Three.js for 3D graphs
- Backend: Supabase (PostgreSQL, Edge Functions)
- AI Models: KumoRFM (Relational AI), Groq (LLM inference)
- Node.js 18+
- Bun (recommended package manager)
- Git
- Supabase CLI
- Docker (for local Supabase services)
-
Clone and Install
git clone https://github.com/your-org/hyperelational.git cd hyperelational bun install -
Setup Local Supabase
# Initialize Supabase supabase init # Start local services (Docker required) supabase start # Apply database migrations supabase db reset # Deploy edge functions supabase functions deploy
-
Configure Environment
cp .env.example .env.local
Update
.env.localwith your configuration:# Supabase (get from: supabase status) VITE_SUPABASE_URL=http://127.0.0.1:54321 VITE_SUPABASE_ANON_KEY=your-local-anon-key SUPABASE_SERVICE_ROLE_KEY=your-local-service-role-key # AI Services KUMO_API_KEY=your-kumo-api-key GROQ_API_KEY=your-groq-api-key
-
Start Development Server
bun run dev
Navigate to
http://localhost:8080
hyperelational/
├── public/ # Static assets
├── supabase/
│ ├── functions/ # Edge Functions (Deno)
│ │ ├── agent-workflow-executor
│ │ ├── retail-predict-churn
│ │ ├── artist-fan-engagement
│ │ └── sample-data-seeder
│ └── migrations/ # Database schemas
└── src/
├── components/
│ ├── ui/ # shadcn/ui components
│ ├── analytics/ # Analytics modules
│ └── dashboard/ # Dashboard widgets
├── contexts/ # React contexts
├── hooks/ # Custom hooks
├── integrations/ # Supabase client
├── lib/ # Utilities
├── pages/ # Route components
├── services/ # Business logic
└── stores/ # Zustand state
Our serverless backend handles complex AI operations through specialized Edge Functions:
| Function | Purpose | Input | Output |
|---|---|---|---|
agent-workflow-executor |
Orchestrates multi-agent workflows | Task config | Execution results |
retail-predict-churn |
Customer churn prediction | Customer data | Churn probability |
artist-fan-engagement |
Fan engagement scoring | Fan activity | Engagement metrics |
artist-forecast-demand |
Tour demand forecasting | Historical data | Demand predictions |
retail-recommend-products |
Product recommendations | User profile | Recommended items |
// Predict customer churn
const churnPrediction = await supabase.functions.invoke('retail-predict-churn', {
body: { customer_id: 'cust_123' }
})
// Get product recommendations
const recommendations = await supabase.functions.invoke('retail-recommend-products', {
body: { user_profile: customerData }
})// Analyze fan engagement
const fanMetrics = await supabase.functions.invoke('artist-fan-engagement', {
body: { artist_id: 'artist_456', time_window: '30d' }
})
// Forecast tour demand
const demandForecast = await supabase.functions.invoke('artist-forecast-demand', {
body: { venue_data: venues, historical_sales: sales }
})# Run all tests
bun test
# Run with coverage
bun test --coverage
# Run specific test suite
bun test --grep "Edge Functions"
# Test edge functions locally
supabase functions serve-
Setup Supabase Project
supabase link --project-ref your-project-ref supabase db push supabase functions deploy
-
Deploy Frontend
# Build for production bun run build # Deploy to your preferred platform # (Vercel, Netlify, etc.)
-
Environment Variables
VITE_SUPABASE_URL=https://your-project.supabase.co VITE_SUPABASE_ANON_KEY=your-production-anon-key SUPABASE_SERVICE_ROLE_KEY=your-production-service-key KUMO_API_KEY=your-production-kumo-key GROQ_API_KEY=your-production-groq-key
We welcome contributions! Please see our Contributing Guidelines for details.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Make your changes
- Add tests for new functionality
- Run the test suite (
bun test) - Commit using conventional commits (
git commit -m 'feat: add amazing feature') - Push to your branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Response Times: < 100ms for cached queries, < 2s for AI predictions
- Uptime: 99.9% SLA with automatic failover
- Monitoring: Built-in error tracking and performance analytics
- Scaling: Auto-scaling edge functions handle 10k+ concurrent requests
- Data Encryption: End-to-end encryption for all data in transit and at rest
- API Security: Rate limiting, authentication, and request validation
- Privacy Compliance: GDPR, CCPA compliant data handling
- Audit Logs: Complete audit trail for all data operations
- Q1 2025: Real-time streaming analytics
- Q2 2025: Mobile app (React Native)
- Q3 2025: Advanced ML model marketplace
- Q4 2025: Multi-tenant enterprise features
- Supabase: For their incredible open-source backend platform
- Kumo.AI: For the groundbreaking Relational Foundation Model
- Groq: For high-speed LLM inference powering our AI chat
- shadcn/ui: For the beautiful, accessible component library
This project is licensed under the MIT License - see the LICENSE file for details.
