Advanced Analytics and Machine Learning for Real-Time Intelligence

Industries today demand real-time intelligence, domain-specific models, and outcome-driven solutions that can be rapidly deployed and scaled. The focus is now on achieving measurable business outcomes through AI-powered interventions that are explainable, secure, and seamlessly integrated into operations.
 
Tata Elxsi helps enterprises operationalise AI with modular offerings spanning applied ML, MLOps & DataOps,  bespoke model development, and industrial analytics. By combining domain-specific expertise, reusable IPs, and cloud-agnostic platforms, we help businesses derive real-time insights, reduce time to market, and drive ROI at scale.
 
Case Study

Transforming Digital Content Engagement with AI-Driven Recommendation Engine

Business Challenge

Organisations often operate with massive volumes of data but lack clarity on how to translate it into actionable insights. Misalignment between business objectives, data availability, and model maturity leads to stalled AI initiatives. Teams work in silos, return on investment is unclear, and production-grade deployment of ML models remains a challenge due to poor data governance, absence of retraining pipelines, and limited model explainability. Without an integrated MLOps & DataOps approach, scaling from pilot to enterprise-wide rollouts becomes time-consuming and inconsistent.

Here’s How We Can Help
Here’s How We Can Help

Here’s How We Can Help

Data Engineering & Integration
  • Aggregate structured, unstructured, and streaming data from CSV, SQL, MQTT, CRM,and AV sources
  • Enable Data Lake and Data Mesh setups across AWS, Azure, GCP, and hybridenvironments
Accelerate Machine Learning Outcomes
  • Develop explainable ML models and enable auto-retraining with AI Glass
  • Cut model development time by 25% with applied ML components and reusablepipelines
Make Insights Accessible & Actionable
  • Deliver real-time, interactive dashboards via Power BI, Tableau, and Grafana
  • Enable low-code visualisations and analytics consumption using React and D3.js

Service Framework

Service Framework

Core Features Enabling Scalable AI

ML Lifecycle Management

✓ Continuous monitoring, drift detection, and retraining pipelines
✓ Integrated model registry, version control, and explainability tooling
✓ No-code dashboard for business and technical users

DataOps Acceleration

✓ Automated data ingestion, profiling, and lineage tracking
✓ Cloud-agnostic orchestration across hybrid environments
✓ Built-in metadata and schema management

Domain-Tuned Model Libraries

✓ Pretrained models for telecom, healthcare, media, and automotive
✓ Rapid contextualisation and deployment using reusable assets
✓ Fine-tuning and benchmarking aligned with business KPIs

Why Tata Elxsi?

  • 30–40% faster go-to-market with reusable ML assets, pre-trained models, and cloud-native deployment accelerators
  • Plug-and-play analytics frameworks reduce model development cycles from 6 months to 1.5 months, supporting bothapplied ML and statistical model development.
  • Domain-calibrated AI solutions fine-tuned for telecom, automotive, media, and healthcare use cases.
  • Platform-agnostic IPs like AI Glass and TEDAx support seamless integration across AWS, Azure, GCP, hybrid, and on-prem. 
  • 15–25% productivity boost achieved through DataOps & MLOps automation and real-time monitoring tools.

Information Hub

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