RAG over company data

Last updated: 12. 05. 2026

RAG Architecture in an Enterprise Environment

In the era of generative artificial intelligence, organizations face a fundamental challenge: how to leverage powerful language models (LLMs) such as GPT while ensuring that responses are based on closed, verified corporate know-how rather than public training data. The answer is RAG (Retrieval-Augmented Generation) architecture.

Pro technické lídry a datové architekty nepředstavuje RAG pouze aplikaci AI, ale především komplexní úlohu v oblasti Data Engineering a Data Architecture. Tento článek rozebírá technické aspekty implementace RAG v kontextu moderní datové platformy postavené na ekosystému Microsoft Azure a Microsoft Fabric.

For technical leaders and data architects, RAG is not just an AI application, but above all a complex task in the field of data engineering and data architecture. This article discusses the technical aspects of RAG implementation in the context of a modern data platform built on the Microsoft Azure and Microsoft Fabric ecosystem.

When Data Science Meets Search

The fundamental problem with pre-trained models is the lack of context about your private data and a tendency to hallucinate. RAG solves this problem with a hybrid approach. The process consists of two phases:

1. Retrieval: Based on the user's query, the system searches for relevant fragments of information in the internal knowledge base (vector database, data warehouse).

2. Generation: The found context, together with the original query, is passed to the LLM, which synthesizes the answer based on it.

From an architectural perspective, this is not a "black box" but a sophisticated pipeline that requires a robust data foundation.

Data Lakehouse as the foundation for AI

The traditional data warehouse has been optimized for structured data and SQL queries. For the needs of GenAI, which works with unstructured text (PDF guidelines, documentation, call transcripts, logs), it is necessary to switch to the Data Lakehouse paradigm.

In Microsoft Fabric and Azure environments, this unification is provided by the OneLake layer. Data Lakehouse allows data to be stored in its native format, while we build analytical and AI services on top of it without the need for costly duplication.

Data processing pipeline (Ingestion & Chunking)

The quality of RAG output is directly proportional to the quality of input. Data engineering plays a key role here. The process of preparing data for LLM includes:

• Extraction: Obtaining data from various sources (SharePoint, Blob Storage, SQL DB).

• Cleaning: Removing formatting noise.

• Chunking: Dividing text into logical segments (windows) that fit into the model's context window.

• Embedding: Converting text chunks into vector representations (arrays of numbers) using embedding models (e.g., text-embedding-ada-002).

These vectors are then stored in a vector index (e.g., in Azure AI Search), which enables semantic search—that is, searching by meaning rather than just by keywords.

Azure Cloud Solution Architecture

Security, scalability, and governance are critical for enterprise deployments. A typical reference architecture uses the following components:

1. Orchestration: Frameworks such as LangChain or Semantic Kernel control the application flow.

2. Vector Store / Search Index: Azure AI Search, which supports hybrid search (a combination of keyword search and vector search) and re-ranking of results for higher relevance.

3. LLM Endpoint: Azure OpenAI Service. It is crucial here that the data does not leave the customer's tenant and is not used to retrain public models.

4. Data Foundation: Microsoft Fabric for data flow unification.

The Role of Data Mesh in the Context of AI

As AI becomes more widespread across the company, it makes sense to apply the principles of Data Mesh. Instead of a single monolithic data lake, we can access domain-oriented „data products“.

For example, the HR department manages its guidelines and provides them as a vectorized data product. The sales department provides product data. The RAG application can then access these decentralized but standardized data products. This increases accuracy (data is managed by the domain owner) and security (access control at the domain level).

Cloud analytics and feedback

The work doesn't end with deploying a chatbot. Modern cloud analytics must monitor the AI's operation itself:

• Latency: How long does retrieval and generation take?

• Cost Management: Monitoring token consumption.

• Quality Assessment: Evaluation of the relevance of responses (e.g., using the RAGAS methodology – RAG Assessment).

Using tools within Azure Monitor and Application Insights, we can tune search parameters and model prompts in real time.

From data purity to hallucinations

When implementing RAG, we often encounter situations where companies' data architecture is not prepared for unstructured data. Documents are outdated, duplicated, or contain sensitive information that the model should not see (PII).

The solution is to implement strict ACLs (Access Control Lists) at the index level—the model should only have access to those documents that the user making the query is authorized to access. This is a standard that Azure AI Search natively supports.

AI is only as good as your data

Implementing RAG over corporate data is not magic, but a sophisticated engineering discipline. It requires a firm hand in data governance, modern Data Lakehouse infrastructure, and deep knowledge of cloud services.

At Data Mind, we help clients not only with the deployment of the models themselves, but above all with building a robust data base, without which AI cannot function effectively. Whether it's data warehouse optimization, cloud migration, or custom RAG application development, the key to success is always scalable architecture.