Smarter Networks,
Streaming Intelligence,
Personalized Media
ML-driven unified intelligence layer that predicts, adapts, and personalizes across network routing, video delivery, and user recommendations in real time.
Three Interconnected
TMT Challenges
The TMT sector faces complex problems that traditional approaches cannot solve alone
Network Overload & Inefficiency
With 5G rollout and increasing demand for high-bandwidth services (gaming, AR/VR, video calls), networks face unpredictable congestion. Operators still rely on manual network tuning, leading to frequent slowdowns, dropped calls, and underutilized bandwidth.
Streaming Experience Degradation
Despite high connection speeds, users often experience buffering, low resolution, or poor audio quality. The issue isn't connectivity — it's inefficient video delivery pipelines that don't adapt to user context (device type, network speed, or location).
Content Discovery & Personalization
Viewers face an ocean of content, yet most recommendation systems repeat the same items, fail with new users, or don't measure true satisfaction. Ads are often mis-targeted, leading to poor ROI and wasted impressions.
Tensorblue's Solution: An ML-driven unified intelligence layer that predicts, adapts, and personalizes across all three domains — from network routing to user recommendations — in real time.
Three Pillars of
TMT Intelligence
CORE
Intelligent Network Optimization
Predict congestion, automate routing, minimize downtime
Adaptive Video Streaming
Best possible quality under any network condition
Personalized Recommendations
Increase engagement, watch time, ad efficiency
Intelligent Network
Optimization
Predict congestion, automate routing, and minimize downtime with ML-driven network intelligence
Data Inputs
Methodology
Technology Stack
Pilot Network Results
Real-time Status
Adaptive Video
Streaming Optimization
Deliver the best possible video quality under any network condition with intelligent adaptation
Streaming Intelligence Pipeline
Data Inputs
ML Prediction
Optimized Stream
Core Methodology
Predictive Buffering Prevention
Predict when a user might face buffering and dynamically lower bitrate before it happens
CDN Routing Optimization
Send each user's video from the nearest edge node using demand forecasting
Perceptual ML Quality
Apply VMAF-based perceptual ML to maintain consistent visual quality even at lower bitrates
Technology Stack
Streaming Customer Results
Quality Metrics
Personalized Recommendations
& Ad Targeting
Increase engagement, watch time, and ad efficiency through intelligent personalization
Personalization Engine Architecture
Data Collection Layer
ML Processing Layer
Output Generation Layer
Causal Ad Measurement
Ads are optimized for incremental conversion, not just CTR, using causal inference models
Technology Stack
Platform Performance Results
Engagement Metrics
Real-World
TMT Success Stories
Proven results across telecom operators, streaming platforms, and OTT services
South Asian 5G Network
Large-scale 5G deployment with sports event traffic spikes
European Mobile Platform
High-latency issues on mobile networks
US Content Platform
Repetitive recommendation algorithms
Cross-Platform Performance Summary
Network Optimization
Streaming Quality
Personalization
Technical
Architecture
Unified ML data lake processing network, streaming, and user behavior data in real-time
TMT Intelligence Architecture Flow
Data Sources
Central ML Data Lake
S3 + Spark for unified processing
Model Training
TensorFlow/PyTorch for predictive models
Model Serving APIs
FastAPI + Docker for real-time inference
Live Actions
Dashboards
Grafana / Streamlit for monitoring and insights
Unified Data Strategy
Real-time Decision Making
Proven Results
Across Deployments
Measurable improvements across network performance, streaming quality, and user engagement
Network Performance
Streaming Quality
User Engagement
Operational Efficiency Gains
Before vs After: Key Metrics
| Metric | Before | After | Improvement |
|---|---|---|---|
| Network Downtime | 5.2% | 2.8% | ↓46% |
| Average Latency | 45ms | 28ms | ↓38% |
| Video Buffering | 12% | 4.8% | ↓60% |
| Ad ROI | 1.2x | 1.8x | ↑50% |
| Watch Time per Session | 18 min | 24 min | ↑33% |
Broader Value
Proposition
Comprehensive benefits that transform TMT operations into intelligent ecosystems
Unified Data Strategy
One analytics layer for network, streaming, and user data eliminates silos and enables cross-domain insights
Faster Decision Cycles
Models update every 15-30 minutes using live data, enabling rapid response to changing conditions
Better ROI Tracking
Each optimization is linked to measurable business KPIs with clear attribution and impact measurement
Plug-and-Play Integration
Works with any 5G operator, CDN, or media platform API with minimal configuration required
Tensorblue turns TMT operations into intelligent ecosystems
Where every network node, video stream, and ad placement becomes a data-driven decision point. By applying machine learning to predict demand, reduce latency, and personalize content, we help telecoms and media companies cut costs, increase engagement, and scale globally without sacrificing user experience.
Cost Reduction
10-20% operational cost savings through intelligent automation
Performance Boost
25-40% improvement in key performance metrics
User Experience
15-25% increase in engagement and satisfaction
Technology
Stack
Comprehensive technology stack for end-to-end TMT intelligence platform
Data Engineering
ML Training
Deployment
Analytics
Specialized TMT Tools
Video Processing
Recommenders
Storage & Infrastructure
Transform your TMT operations into intelligent ecosystems with unified ML-driven intelligence
Every network node, video stream, and ad placement becomes a data-driven decision point. Cut costs, increase engagement, and scale globally without sacrificing user experience.