A leading American fiber to the home (FTTH) broadband services provider began exploring Generative AI to improve customer experience, enhance employee productivity, and accelerate operational efficiency. While multiple teams initiated GenAI proof of concepts, the organization faced challenges scaling these initiatives into secure, production ready solutions. Fragmented development across business units led to duplication of effort, delayed deployments, and inconsistent governance.
The lack of standardized frameworks and production expertise significantly increased time to market, while the absence of centralized logging, monitoring, and privacy controls created operational and compliance risks. Existing solutions were unable to scale reliably to handle enterprise workloads, limiting broader adoption.
Infosys partnered with the client to design and implement a RAG (Retrieval Augmented Generation) as a Service platform on Google Cloud. The solution provides a generic, elastic, and production ready foundation for onboarding multiple GenAI use cases rapidly. Built with security, observability, and governance by design, the platform enables faster experimentation, seamless deployment, and continuous optimization.
The resulting platform empowers teams to move from POC to production with confidence—accelerating innovation while ensuring scalability, reliability, and compliance across enterprise GenAI initiatives.
Faster
Time to market
Lower
Operational costs
Enterprise-scale
RAG workloads
Built-in
Governance & security
Key Challenges
- Fragmented GenAI development across business units
- Lack of frameworks to move from POC to production
- Delayed time to market for AI use cases
- Missing observability, security, and privacy controls
- Inability to scale GenAI workloads reliably
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Infosys Approach
- Designed a generic, elastic RAG platform for enterprise adoption
- Built production ready architecture using Google Cloud services
- Standardized data ingestion, processing, and retrieval pipelines
- Embedded end to end logging, monitoring, and privacy guardrails
- Implemented governance with RBAC, audit trails, and policy enforcement
- Enabled continuous model optimization and lifecycle management
The Solution
Production ready RAG as a Service enabling secure, scalable GenAI adoption
Infosys delivered a robust RAG as a Service platform that enables rapid onboarding, deployment, and governance of GenAI use cases at enterprise scale. The solution provides a generic, elastic foundation that minimizes rework while supporting diverse RAG workflows.
A dynamic UI framework abstracts individual use cases and encapsulates sensitive data within the application layer, ensuring security and compliance. High throughput data ingestion pipelines support structured, semi structured, and unstructured data through real time and batch processing. End to end request and response logging delivers full observability, supported by monitoring dashboards and privacy guardrails.
The platform integrates natively with Google Cloud’s AI ecosystem, leveraging Gemini models and Vertex AI for reliability and performance. Continuous optimization capabilities—including fine tuning and retraining pipelines—enable evolving business needs, while governance frameworks ensure auditability, role based access, and regulatory compliance across all GenAI workloads.
Business Outcomes
Accelerated time to market for GenAI use cases
Reduced productionization and operational costs
Enabled scalable, enterprise ready RAG workloads
Improved reliability with built in monitoring and observability
Strengthened compliance with privacy and governance guardrails
Increased flexibility to support evolving RAG use cases
Benefits
Enterprise RAG platform enables faster innovation, scalable AI adoption, reduced costs, and secure operations.
- Faster provisioning of GenAI use cases with reduced time to market
- Lower operational and productionization costs
- Flexible platform supporting dynamic RAG workflows
- Scalable architecture for high volume enterprise workloads
- Built in governance with audit trails and access controls
- End to end observability with privacy guardrails
- Continuous model optimization for evolving business needs
