System Architecture.
Examine service boundaries, API contracts, data flows, and infrastructure components. Click to zoom and pan.
Demo Walkthrough.
Watch an end-to-end execution of the system in action.
Runtime Screenshots.
Interface states, developer logs, and monitoring views. Click any image to view in lightbox.
Technical Specifications.
Verified parameters and operational benchmarks for this release.
Tech Stack.
Features Checklist.
Core capabilities, verified deliverables, and technical assets included with this release.
- Executive ROI dashboard
- Average Revenue Per User (ARPU) calculation
- Unified Slot Score (USS)
- Inventory Quality Score
- Ad slot performance analysis
- Advertiser tier classification
- Tier A/B/C advertiser mapping
- CPM range recommendations
- Demand level analysis
- Revenue-first decision mode
- Viewer-experience decision mode
- Balanced/default decision mode
- Monetization policy engine
- Premium monetization recommendations
- Standard monetization recommendations
- Experience-protection recommendations
- Executive what-if simulator
- Revenue impact simulation
- Skip-rate impact estimation
- Churn-risk estimation
- Real-time quality monitoring
- Ad slot analysis APIs
- Video performance analytics
- Revenue breakdown analytics
- Interactive data visualization
- REST API
- React + TypeScript dashboard
- SQLite persistence
- Hybrid ML + rule-based prediction architecture
- Sample OTT/live-TV dataset
System Requirements.
- OS
- Linux, Windows, or macOS
- CPU
- 4+ cores recommended
- GPU
- Not required for the included dashboard and rule-based analytics
- RAM
- 8GB minimum, 16GB recommended
- npm
- 9+
- Python
- 3.10+
- Browser
- Modern Chrome, Firefox, Edge, or Chromium-based browser
- Network
- Required for frontend/backend communication
- Node.js
- 18+
- Storage
- 5GB+ free space
- Database
- SQLite included
About this Project.
AI Ad Break Optimization & Revenue Intelligence Platform
A full-stack analytics and decision-support platform designed for OTT and live-TV environments.
The system evaluates advertising inventory using viewer attention, stream quality, drop-off risk and revenue potential, then converts those signals into actionable monetization recommendations.
It provides an executive dashboard for understanding inventory quality, revenue performance and the trade-offs between monetization and viewer experience.
Core Capabilities
Revenue Intelligence
Track key platform and advertising metrics including:
- Total revenue
- ARPU
- eCPM
- Ad-slot performance
- Revenue by advertiser tier
- Demand levels
- Inventory quality
Unified Slot Score
Each advertising slot receives a Unified Slot Score (USS) based on:
- Attention level
- Quality stability
- Drop-off risk
- Revenue potential
Slots are then classified according to their overall commercial value.
Inventory Quality Scoring
The platform evaluates advertising inventory using:
- Viewer attention
- Stream stability
- Drop-off probability
- Prediction confidence
This produces a normalized quality score for each ad slot.
Advertiser Tier Mapping
Ad inventory is automatically categorized into:
- Tier A — Premium
- Tier B — Standard
- Tier C — Remnant
Each tier can be associated with different CPM ranges and monetization strategies.
Monetization Policy Engine
The platform supports multiple operating strategies:
Revenue First
Prioritize monetization and higher advertising density.
Viewer Experience
Reduce advertising pressure and prioritize engagement and retention.
Default
Maintain a balanced strategy between revenue and viewer experience.
The policy layer recommends an appropriate monetization strategy based on slot-level metrics.
Executive What-If Simulator
The simulator allows decision-makers to explore potential outcomes when changing revenue priorities.
It estimates changes in:
- Revenue
- Skip rate
- Churn risk
- Viewer experience
This allows teams to evaluate monetization strategies before applying them.
Real-Time Quality Monitoring
The architecture supports monitoring of video and advertising inventory quality, including metrics related to stream stability and viewer behavior.
Technology
Frontend:
- React 18
- TypeScript
- Vite
- Tailwind CSS
- Recharts
- Axios
- React Query
Backend:
- Python
- FastAPI
- SQLAlchemy
- SQLite
- NumPy
Decision Intelligence:
- Hybrid ML + rule-based prediction
- Scoring models
- Monetization policy engine
- What-if simulation
API
The backend provides REST endpoints for:
- Dashboard statistics
- Mode comparisons
- Tier distribution
- Quality analysis
- Revenue breakdown
- Top-performing slots
- Platform settings
- What-if simulations
- Ad-slot analysis
- Video metrics
- Revenue analysis
- Quality reports
Sample Data
The application includes sample OTT/live-TV data for demonstrating the dashboard, including:
- Multiple videos
- Advertising slots
- Viewer metrics
- Revenue information
- Quality metrics
- Advertiser tiers
- Demand levels
Intended Users
Suitable for:
- OTT platforms
- Live-TV platforms
- Ad-tech developers
- Media analytics teams
- Advertising technology startups
- Streaming analytics projects
- AI/ML developers
- Business intelligence prototypes
- Research and academic demonstrations
Important
The included implementation is a decision-support and analytics platform. The sample predictions and business rules should be validated against real platform data before being used for production advertising decisions.
Advanced real-time video analysis, HLS/RTMP ingestion and production-scale ML training are future extensions rather than included core functionality.
Included
- Complete source code
- React frontend
- FastAPI backend
- Database layer
- Analytics engine
- USS scoring system
- Inventory quality scoring
- Advertiser tiering
- Monetization policy engine
- What-if simulator
- REST API
- Sample data
- Setup documentation
- Deployment instructions