RAG Accelerator: Empowering Enterprises to Operationalise RAG with Databricks

As enterprises increasingly explore the potential of large language models (LLMs), they often encounter fundamental challenges, such as how to combine their private and unstructured data with generative AI capabilities in a securely scalable and governed manner.

Retrieval-Augmented Generation (RAG) offers a path forward by enhancing LLM responses with enterprise-specific knowledge. However, operationalising RAG across multiple data systems, governance frameworks and model endpoints is a complex task.

To address these challenges, Cloudaeon has developed the RAG Accelerator, an enterprise-ready web platform that simplifies and streamlines RAG adoption. Built using Databricks and complementary technologies, RAG Accelerator enables organisations to connect diverse data sources, vector databases and LLMs seamlessly. This empowers teams to query their data naturally while maintaining governance and performance.

The Challenge: Scaling RAG in the Enterprise

Enterprises adopting RAG often face recurring challenges that limit the scalability and impact of their initiatives:

• Fragmented integrations: Connecting to multiple LLMs, vector databases and data sources often requires extensive custom engineering.
• Lack of governance: Without centralised control, managing user access, workspace configurations and resource usage becomes difficult, which leads to inefficiencies and security risks.
• Opaque costs: Limited visibility into model and infrastructure usage makes it difficult to optimise cost and performance.
• Uncertain quality: Evaluating and monitoring the reliability and effectiveness of AI-generated outputs remains challenging.

The RAG Accelerator was designed to overcome these limitations by leveraging the Databricks Data Intelligence Platform as the unified foundation for data and governed AI workflows.

Solution: The RAG Accelerator 

The RAG Accelerator integrates the entire RAG lifecycle from data ingestion and vectorisation to retrieval and generation within a modular Databricks-native architecture. 

Key capabilities of RAG Accelerator: 

• Data ingestion and processing: Automates ingestion from structured and unstructured data sources such as webpages, SQL databases, ADLS, GCP Buckets and AWS S3.

• Vectorisation and indexing: Prepares embeddings from textual data and stores them efficiently in vector databases such as Databricks Vector Search, Pinecone, Chroma DB or Milvus DB. 

• Retrieval-Augmented Generation (RAG): Dynamically retrieves relevant data chunks to provide contextually enriched responses from LLMs. 

• Multi-LLM connectivity: Supports Databricks serving endpoints, Azure AI, OpenAI and Hugging Face, allowing flexible deployment and routing of inference requests. 
• Unified governance: Uses Databricks Unity Catalog for centralised data governance, lineage tracking and role-based access.

RAG Accelerator Architecture and Workflow

The RAG Accelerator is built around two primary architectural layers: Data ingestion & vectorisation pipeline and the RAG Query Orchestration Layer powered by Databricks technologies.

Data ingestion and vectorisation pipeline

This pipeline connects to enterprise data sources, processes content for embeddings and stores both intermediate and vectorised outputs for retrieval.

• Databricks volumes: Store web-scraped or processed multimedia such as PDFs, images, videos and text files.

• Unity Catalog delta tables: Maintain structured, pre-vectorisation data for governance and lineage tracking.

• Vector search index: Stores vectorised embeddings for high-performance semantic retrieval.

• Databricks jobs and clusters: Manage ingestion pipelines where each data source is represented as a dedicated Databricks Notebook, orchestrated via Jobs and executed on elastic clusters.

External vector stores such as Pinecone, Chroma DB and Milvus DB are also supported, enabling flexibility with hybrid deployments.

RAG pipeline and query orchestration

The RAG pipeline handles user queries by performing vector search, enriching context and interacting with multiple LLMs.

• RAG orchestration: Retrieves relevant knowledge chunks and constructs a context-enriched prompt for LLM inference.

• Databricks serving endpoints: Provide native access to Databricks-hosted and fine-tuned models.

• Cross-LLM routing: Dynamically directs inference requests to connected model endpoints, whether on Databricks, Azure AI or OpenAI.

This architecture seamlessly merges Databricks governance and performance with the flexibility of multi-model orchestration.

Beyond RAG: Context and Multi-Agent Intelligence

To extend the capabilities of traditional RAG systems, the RAG Accelerator introduces two advanced components, the MCP server hub and the A2A server, enabling context-aware actions and multi-agent collaboration.

MCP server hub

The MCP (Multi-Context Protocol) server hub acts as a centralised connection registry within the RAG Accelerator platform. It allows users to connect to various external systems such as SQL databases, Confluence, email servers, file systems and use them as additional context providers or action endpoints during conversations.

When a user interacts with the RAG interface, these MCP server connections can be invoked to:

• Retrieve relevant information from enterprise systems (e.g., querying SQL databases or reading internal documentation).

• Perform contextual actions (e.g., sending an email, updating a record or retrieving a file).

This design transforms the RAG Accelerator from a passive Q&A system into an active enterprise assistant capable of securely acting across connected environments, all while maintaining full observability through Databricks governance layers.

A2A server: multi-agent collaboration framework

The A2A Server introduces agent-to-agent (A2A) communication capabilities, allowing enterprises to build modular, reusable and collaborative AI agents.

Within the RAG Accelerator, users can define agents by specifying their titles, instructions and associated MCP server connections. These agents are then registered within the A2A Server and can be reused across different workflows or combined into multi-agent systems.

The A2A protocol ensures standardised communication between agents, enabling them to coordinate and share context to collectively solve complex enterprise queries.

For example, an enterprise might create:

• A “Data Retrieval Agent” connected to SQL and file servers

• An “Analysis Agent” connected to Databricks Delta Tables

• A “Reporting Agent” connected to Power BI or email MCPs

Through the A2A Server, these agents can collaborate autonomously, leveraging shared context from the RAG pipeline and MCP connections by reducing redundancy, improving consistency and accelerating AI-driven decision workflows.

Integration with Azure Databricks

In the operational view, the RAG Accelerator integrates Databricks with surrounding Azure services for a complete, end-to-end experience:

• Front-end (AKS + React): A responsive React-based web UI hosted on Azure Kubernetes Service (AKS) enables users to manage connections, configure agents, including interact with RAG-powered chat interfaces.

• Python API layer: Handles orchestration, triggers Databricks Jobs, performs LLM calls and communicates with vector stores.

• Databricks notebooks & jobs: Execute ingestion, embedding and vectorisation pipelines on governed Databricks clusters.

• Unity Catalog and Lakebase (PostgreSQL): Manage user data configuration metadata and logs.

• LLM serving endpoints: Handle context-augmented inference requests.

This integrated stack unites Databricks’ governance and scalability with custom orchestration layers built by Cloudaeon that deliver production-grade RAG and a multi-agent solution.

Business Impact with RAG Accelerator

The RAG Accelerator empowers enterprises to move beyond proofs of concept toward production-grade, governed AI systems:

• Unified architecture offered centralised governance, orchestrated monitoring across RAG and multi-agent workflows.

• Faster time-to-value: Pre-built pipelines and integrations accelerate deployment.

• Enhanced context and accuracy: MCP integrations bring live enterprise context into every query.

• Reusability and extensibility: A2A server enables scalable, reusable agent ecosystems.

• Compliance and security: Unity Catalog and Databricks Volumes ensure governed data usage and end-to-end traceability.

Conclusion

The RAG Accelerator by Cloudaeon showcases how Databricks technologies, including Volumes, Unity Catalog, Vector Search, Jobs and Serving Endpoints, can form the foundation for next-generation RAG and multi-agent AI platforms.

By integrating Databricks’ unified data intelligence capabilities with advanced orchestration features like the MCP Server Hub and A2A Server, the RAG Accelerator enables enterprises to securely connect and act on their data at scale with full governance.

This platform represents a major step forward in bringing retrieval-augmented, multi-agent intelligence into the enterprise ecosystem, where data, governance and AI converge.

Here's how Cloudaeons RAG Accelerator worked for a retail chain.