How AI Video Analytics Enhances Customer Experiences Across Industries
Read Blog
50% Reduction in Component Defect Rate with Video Analytics
Read Case StudyCreating Intelligent Cognitive Systems with AI
Augmenting Plant Monitoring and Road Safety with AI
Business Challenge
Manual workflows in content moderation, safety surveillance, and diagnostics are time-consuming, error- prone, inconsistent, and subjective. Enterprises must move beyond automation toward intelligent systems that understand context, adapt to new data, and act in real time. This requires scalable video intelligence that supports real-time detection, edge processing, and minimal data dependency without compromising on accuracy, compliance, or system integration.
Here’s How We Help
- Use AI-powered media platforms to generate reels, highlights, metadata, subtitles, and ads,cutting editorial effort by up to 80%.
- Enable multilingual dubbing, content moderation, and intelligent tagging with seamlessintegration of third-party tools like Microsoft Azure Content Moderator, ElevenLabs,Lingopal.ai, and Sesame AI.
- Achieve high-precision results using an ensemble-based approach with domain-specifictuning—delivering up to 99.7% accuracy in manufacturing and 90–95% in healthcare andbroadcast use cases, even under varied lighting and angles.
- Integrate AI-driven industrial video platforms at the edge for real-time inference andcontinuous learning, delivering sub-second alerting in industrial environments.
- Screen X-rays/Ultrasounds/Radiology images daily using low-data models with 75% loadreduction for radiologists.
- Detect musculoskeletal injuries using adaptive AI trained on significantly lower data volumes.
Service Framework
AI-Powered Video Intelligence
✓Object and event detection in real-time streams
✓Multimodal content analysis (video, audio, metadata)
✓Video-based anomaly and violation detection
Model Tuning & Self-Learning
✓Prebuilt model blocks via AIVA and IRIS
✓Adaptive learning from real-world video deviations
✓Hybrid AI using image processing and ML
Edge Deployment & Scalability
✓ Edge-first inference with support for on-prem and hybrid cloud
✓ Templatized pipelines for scalable, multi-stream video analytics
✓ Interoperability with third-party tools and enterprise ecosystems
Why Tata Elxsi?
- Up to 99.7% model accuracy delivered in real-world cognitive video deployments, driven by a continuous learning loop.
- 60–90% lower data requirement for training video AI models using ensemble pipelines combining ML and image processing.
- Scalable delivery models combining edge deployments, cloud orchestration, and platform engineering.
- Seamless integration with existing platforms and ecosystems.
- Proven deployments across Europe, India, and MENA regions.
In Focus
Information Hub
-
What makes intelligent cognitive video systems work—and where do they fail?
These systems combine computer vision, deep learning, and domain-specific context. But to be effective, they must handle occlusion, glare, camera misalignment, and inconsistent lighting—conditions typical in industrial and healthcare environments. The real challenge isn’t detection alone—it’s maintaining reliability across edge deployments with minimal human calibration.
-
Which use cases see real ROI—and which don’t?
Video AI works best in workflows that are repetitive, data-rich, and high-frequency—like quality checks, safety surveillance, and diagnostic triage. ROI is lower in low-volume or highly subjective tasks where rules change frequently or context is hard to learn. Success hinges on identifying stable decision loops that AI can own.
-
How does the system hold up under real-world noise?
Our pipelines are designed to adapt across varied lighting, camera angles, and motion blur. By fusing rule-based pre-processing with learning-based models, we minimize false positives and maintain high precision—even in uncontrolled, noisy environments. This ensures consistency across shifts, locations, and hardware setups.
-
How flexible is the deployment model?
We support edge-first, hybrid, on-prem, and cloud-native models. Edge is preferred for latency-sensitive, privacy-critical tasks. Cloud adds orchestration and retraining scale. Hybrid allows intelligent syncing between the two. We help clients design for real-world constraints—data gravity, infra readiness, and compliance.
-
How is data privacy and compliance handled?
We ensure GDPR-aligned anonymisation, secure video pipelines, and role-based access. Our architecture supports integration with existing compliance systems.
