Webinar: RAG as a Service
How Enterprises Can Build & Scale Real-World RAG Assistants
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Our latest webinar, “RAG as a Service: Build, Evaluate & Scale Production-Ready Enterprise Assistants,” drew an exceptionally engaged audience from across industries. It is a clear signal that enterprises are no longer exploring RAG out of curiosity, but out of urgency. Questions poured in throughout the session, it was evident that leaders are actively searching for practical, engineering-led ways to take AI beyond experiments and into production.
While interacting with so many leaders, we noticed, Retrieval-Augmented Generation has rapidly become a strategic priority. However, they quickly discover a hard truth: while a proof-of-concept feels simple, transforming RAG into a production-ready system is an entirely different challenge. And you cannot miss out on governance and scalability.
In this webinar, our leaders, Raj Manoharan (Chief Architect), Amol Malpani (Co-Founder & CTO), and Ashutosh Suryawanshi (Lead AI Engineer), explained the challenge and shared a proven path forward.
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A content marketing professional with 8+ years of experience across multiple domains. I turn complex technical concepts into compelling narratives. I help tech and data companies drive engagement, build authority, and generate demand through content-led strategies.
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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged.
The Reality: RAG Experiments Are Easy. Enterprise RAG Is Not.
Raj opened the session with a sentiment that resonated across industries:
"A POC looks simple, but when you move to production, it’s not. That’s where the real complexity begins" - Raj Manoharan
This distinction matters. While POCs operate on tiny datasets with no governance, enterprise environments demand accuracy, data lineage, permission-aware retrieval, and real-time adaptability.
Amol captured this complexity precisely:
"Your data isn’t sitting in one nice single file. It lives in many places, in many formats, with embedded information, and it changes every single day.” - Amol Malpani
Why Production RAG Is Complex than POC was explained so well:
The Challenge: Internal Builds Take 16–20 Weeks Before Value Appears
One of the most referenced moments in the webinar was Amol’s explanation of internal engineering timelines:
“If you build everything from scratch, you’ll spend the first 16 to 20 weeks just on plumbing, not delivering business value.” - Amol
This plumbing includes ingestion frameworks, embedding pipelines, vector databases, access governance, caching, prompt patterns and evaluation tooling. Without these, enterprise RAG cannot scale beyond a demonstration.
Cloudaeon’s RAG Accelerator significantly reduces this runway. He explained how foundational engineering is prebuilt, tested and ready to deploy. The accelerator shrinks delivery timelines from months to weeks.
Amol summarised the impact clearly:
“We’ve already engineered the heavy lifting. Instead of 20 weeks, you start delivering value in under four, while keeping full control of your data, your IP and your system.” - Amol
The Solution: A Production-Ready RAG Platform Designed for Scale
The webinar introduced Cloudaeon’s end-to-end RAG lifecycle: ingestion, chunking, embeddings, vectorisation, retrieval, prompting, evaluation, UI and AI Ops. All integrated into a repeatable architecture.
From ingestion accelerators to synthetic Q&A, from governance to monitoring, every layer is designed for production, not just demos.
Amol contextualised its importance in the broader market:
“RAG is the number-one priority for data and AI leaders in 2026. They want to be tech-agnostic and avoid platform lock-in, which means they must build the right foundation.” - Amol
Live Demo
Ashutosh led the full live demonstration, showcasing not just retrieval but a production-ready workflow: model connections, vector database setup, ingestion pipelines and the Q&A playground with citation-backed responses.
The highlight was the Evaluation Engine, especially the synthetic Q&A database generation, a critical requirement for enterprise reliability.
Ashutosh explained its value with precision:
“Synthetic Q&A helps you understand whether your RAG application is truly ready for wider use, or if it needs refinement before going live.” - Ashutosh Suryawanshi
Synthetic questions, expected facts and LLM-as-a-judge scoring combine to create a continuous accuracy framework.
Scaling Across the Enterprise: From One Assistant to Many
Enterprises rarely stop at one use case. As Amol noted, once teams see value, they scale quickly, which is why shared ingestion, permission-aware retrieval, workspaces, audit logs and drift detection become essential.
Closing Thoughts: A Clear Path from Experimentation to Impact
The central message of the webinar was clear: RAG is no longer a lab experiment. It’s becoming a core enterprise capability, but only when built on the right engineering foundations, governance frameworks and operational discipline.
Cloudaeon’s accelerator-led approach provides that foundation, enabling enterprises to move fast without compromising control and security.
If you're exploring your own RAG use case, Cloudaeon offers an optional, no-commitment discovery session to help you validate feasibility and expected value.
