Across industries, businesses are recognizing the tremendous potential of GenAI, yet many struggle to successfully integrate the technology. In many cases, the adoption of GenAI is sporadic across organizations, highlighting a need for a more comprehensive approach and robust governance. Such organizations face challenges in implementing GenAI at scale due to the prevalence of biases and flawed outputs from the powerful yet flawed nature of large language models. Fundamental tasks like training and output management pose significant challenges that, if not managed properly, will undermine the capabilities of these platforms, jeopardizing security, regulatory compliance and performance.

Wipro AI Control Center addresses these challenges directly, offering a robust accelerator for effective, responsible GenAI adoption.

Built on the Wipro Enterprise AI-Ready platform, the Wipro AI Control Center simplifies the infusion of GenAI into business operations. It uses historical data to generate precise, context-aware responses, optimizing resources while ensuring accuracy and relevance. This solution provides full control over model selection, hosting, and data governance, facilitating seamless GenAI deployment across various departments such as IT, customer care, and operations.

As part of our Wipro FullStride Cloud ecosystem, the Wipro AI Control Center ensures the holistic adoption of AI from initial pilot phases to full-scale production. 

Our Offerings

Data Fabric

Ensure context-rich outputs with enterprise data collection and retrieval augmented generation (RAG).

Validated FM and LLM Library

Choose from various open source and proprietary models, tailored to meet specific business needs.

Automation

Expedite time to value with automation blueprints, IaC scripts and AI tools.

End-to-End Data Observability

Track data pipeline performance from acquisition to final delivery through KPIs, events and metrics.

Cluster and Infrastructure Management

Optimize vCPU and vGPU management for greater efficiency and ROI.

Tooling and Framework

Leverage open source or proprietary MLOps tools to enhance insights and learning.

Use Case Catalogue

Access practical guidance on scenarios and resources to ensure a successful AI powered cloud journey.

Conversational AI Experience Layer

Enable users to interact with and use services in a way that’s customized to their individuals profiles or personas.

Microservices Integration

Ensure a seamless integration via APIs, linking various data sources to support RAG implementation.

Governance

Create AI guardrails to boost risk management and complaince posture.

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News/Awards and recognition

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