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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
- 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
- Develop explainable ML models and enable auto-retraining with AI Glass
- Cut model development time by 25% with applied ML components and reusablepipelines
- 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
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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How can advanced analytics deliver measurable ROI in enterprises?
By reducing model development time, enabling proactive decision-making, and automating workflows, our analytics solutions offermeasurable ROI across use cases—such as churn prediction, predictive maintenance, and real-time QoS.
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Which industries benefit most from advanced analytics?
Telecom, automotive, healthcare, and OTT lead adoption. Use cases include network optimisation, agent performance analysis, enginediagnostics, and content personalisation.
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How do we balance ML automation with human oversight?
Our platforms embed Human-in-the-Loop (HITL) mechanisms, ensuring model explainability and user feedback loops for responsible andadaptive AI.
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What are Tata Elxsi’s model governance and monitoring capabilities?
We offer AI Glass for real-time data drift detection, lineage tracking, model registry, and retraining triggers—aligned to enterprise-gradeMLOps practices.
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What security and compliance measures are followed?
Role-based access, data masking, encryption at rest and transit, and audit logs are foundational. We support hybrid architectures to align withclient-specific compliance needs.
