A leading US telecommunications provider set out to embed Generative AI across its enterprise to enhance customer and employee experiences, optimize operations, and unlock new revenue opportunities. However, fragmented experimentation, inconsistent governance, and manual AI development processes limited scalability and slowed adoption. The client needed a secure, standardized platform to responsibly operationalize GenAI across business functions.
Infosys partnered with the client to design and build a single, enterprise-grade GenAI platform on Google Cloud, enabling teams to experiment, build, deploy, and govern AI use cases at scale. The platform was designed to support the complete lifecycle of GenAI solutions—from data ingestion and model experimentation to deployment, monitoring, and continuous optimization—while embedding responsible AI principles at every stage.
The solution provided self-service access to reusable GenAI components, Retrieval Augmented Generation (RAG) pipelines, agentic frameworks, and LLMOps capabilities. By standardizing workflows and accelerating experimentation, Infosys enabled faster innovation cycles and enterprise-wide adoption. The platform now supports dozens of AI use cases, delivers measurable cost savings, improves productivity, and positions the client with a future-ready AI foundation built for scale, security, and governance.
$20M+
Annual cost savings
50+
GenAI use cases onboarded
100K+
Documents ingested
92%
RAG accuracy
Key Challenges
- Fragmented AI experimentation without enterprise governance
- High manual effort limiting operational efficiency and productivity
- Slow GenAI innovation cycles and limited scalability
- Inconsistent security, compliance, and lifecycle management
- Lack of reusable components across AI initiatives
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Infosys Approach
- Designed a unified GenAI platform on Google Cloud for enterprise-wide adoption
- Embedded responsible AI, security, and governance across the AI lifecycle
- Enabled self-service access to reusable GenAI components and services
- Implemented LLMOps for model tracking, monitoring, and compliance
- Accelerated innovation through configurable pipelines and automation
- Partnered closely with client teams to co-create agentic AI solutions
The Solution
Enterprise GenAI platform enabling secure, scalable innovation with governance by design
Infosys delivered a comprehensive enterprise GenAI platform on Google Cloud that supports experimentation, development, deployment, and governance of AI use cases at scale. The platform offers multi-modal Retrieval Augmented Generation (RAG) capabilities, supporting text, tables, images, video, and audio across multiple file formats. A self-service document ingestion and generative retrieval framework enables rapid onboarding of new knowledge sources.
The solution includes a Python-based data processing framework, dynamic DAG execution, and configurable pipelines for rapid experimentation and tuning. An integrated LLM Studio provides access to model gardens, evaluation frameworks, fine-tuning services, and deployment pipelines. LLMOps capabilities ensure lifecycle tracking, performance monitoring, drift detection, and governance.
Infosys also enabled agentic AI as a service, supporting automated prompt tuning and single or multi-agent orchestration. Multiple production-grade agents were co-developed with the client to optimize operations, case management, and network incident triage.
Business Outcomes
Delivered $20M+ annual cost savings through automation and reduced manual effort
Enabled 50+ GenAI use cases across business and operations teams
Improved knowledge accessibility with 100K+ documents ingested
Achieved 92% RAG accuracy, improving response quality and reliability
Increased productivity through self-service AI development and deployment
Established enterprise-wide AI governance and lifecycle visibility
Benefits
Unified GenAI platform drives faster innovation, operational efficiency, responsible AI adoption, and measurable business impact.
- Faster GenAI experimentation through reusable, self-service components
- Reduced operational costs via automation and AI-driven workflows
- Scalable platform supporting enterprise-wide AI adoption
- Strong governance with end-to-end AI lifecycle visibility
- Improved accuracy and relevance through advanced RAG frameworks
- Accelerated time to value with configurable pipelines and LLMOps
- Secure and responsible AI embedded by design
