
Webinar
RAG-as-a-Service:Instant AI-Powered Q&A on Your Enterprise Data
A golden chance to learn live from the data and AI experts.
Tuesday, December 09, 2025
14:00 GMT / 15:00 CET / 09:00 EST
Join this cutting-edge webinar to:
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RAG-as-a-Service reduces engineering overhead & enables AI-powered Q&A.
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See how to unify multi-source data and evaluate responses with LLM-as-a-Judge and feedback scoring.
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Live demo of Cloudaeon’s RAG-as-a-Service in action.
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Live Q&A with speakers.
Block your calendar:

In just twelve months, Retrieval-Augmented Generation evolved from an emerging approach to the dominant architecture for grounding AI in trusted enterprise data.
In this webinar, you’ll see in action:
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RAG-as-a-Service, a fully managed framework that removes the engineering burden and lets you instantly enable AI-powered Q&A on your unstructured data.
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How to connect multi-source data, generate high-quality embeddings, index them in a vector DB and evaluate responses using techniques like LLM-as-a-Judge and feedback scoring.
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In the live demo, see RAG-as-a-Service in action, from data ingestion to accurate, real-time Q&A on your enterprise knowledge.
If your teams are exploring AI assistants or enterprise knowledge solutions, this session will show you exactly how to make them real, fast, accurate and production ready. Don’t miss the chance to see how RAG-as-a-Service can accelerate your AI roadmap in weeks, not months.
Reserve your seat and take the first step toward operationalising AI in your own environment.

Rajkumar Manoharan
Chief Architect
Cloudaeon

Amol Malpani
Co-Founder
& CTO

Ashutosh Suryawanshi
Lead AI Engineer
Cloudaeon
Meet your expert panel
Live Demo:
See the RAG-as-a-Service in Action
You’ll see how to ingest enterprise data from multiple sources, generate high-quality embeddings, and activate AI-powered Q&A through a fully managed RAG pipeline.
Explore how our RAG framework delivers:
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Multi-source ingestion
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Vector DB indexing
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Automated evaluation with LLM-as-a-Judge + feedback scoring
Join us for a practical, production-ready blueprint to deploy RAG faster.


