Enterprise RAG: AI That Reaches Enterprise Data With Permission

What is enterprise RAG?

Enterprise RAG is the RAG architecture applied together with enterprise data sources, user authorization, metadata, the data catalog, governance, lineage and security policy.

The word “enterprise” here is not a claim about scale but a list of responsibilities. The retrieval method can stay the same; what changes is on whose behalf, and under which rules, that retrieval runs.

How RAG works

A basic RAG architecture works like this:

When a user asks a question the system first finds content related to it. That content is given to the LLM as context, and the model composes its answer using it.

This lets a general-purpose LLM work over an organization's own information. In real enterprise systems, though, data is not made of documents alone.

Enterprise knowledge is spread across systems

An organization's knowledge may be spread across very different systems:

• ERP
• CRM
• Data warehouse
• Data lake / lakehouse
• Operational databases
• Document management systems
• Email and knowledge bases
• IoT and manufacturing systems
• APIs
• Analytical datasets

Loading only PDFs, Word files and similar documents into a vector database is therefore not enough for most enterprise scenarios.

When a user asks “which product group saw a drop in sales last quarter, and what were the main reasons?”, part of the answer may sit in documents, part in the data warehouse and part in operational systems.

The job of Enterprise RAG is to make different enterprise knowledge sources usable by AI.

The Enterprise RAG architecture

At enterprise scale the architecture is wider:

In this architecture retrieval is only one part of the system. What matters more is deciding which information the AI may use, on whose behalf, and under which rules.

Authorization cannot be separated from retrieval

An AI system being technically able to reach a piece of data does not make it right for every user to see it. A finance manager, a salesperson and an HR employee using the same enterprise AI assistant may hold quite different data permissions.

When a user asks “show me everything you know about this employee”, the LLM's answer has to be shaped not only by what the question means but by what that user is permitted to see.

In an Enterprise RAG architecture, authorization therefore cannot be considered separately from retrieval.

In the Kauzas approach, data access for users and AI applications alike can be handled alongside enterprise governance policy.

Carrying permissions through to the data source →

RAG and AI agents

AI agents can use enterprise RAG as one of the tools they reach enterprise knowledge with. But agents go beyond retrieving information: they can also act, through APIs and enterprise systems.

So in an agent architecture, alongside data access, agent identity, tool authorization, action policy and audit all have to be addressed.

More on AI agent governance →

Metadata and the data catalog

For an AI system to use enterprise data correctly, finding the data is not enough; it has to know what the data means.

Fields such as `revenue`, `net_revenue` and `recognized_revenue` can look technically similar while carrying quite different business meanings inside an organization.

The data catalog and metadata layer helps define:

• The definition of the data
• Its source
• Its owner
• Its classification
• Its relationships
• Its usage policy

In an Enterprise RAG architecture, metadata is therefore not only a data engineering problem; it is part of how AI interprets enterprise data correctly.

Data lineage

Which data did an AI system produce an answer from? Where did that data come from, what processing did it pass through, which dataset was it derived from? These questions matter more and more in enterprise AI systems.

Data lineage helps trace the path data takes from the source system to the AI application.

Especially in AI systems used for critical decisions, it matters that the answer can be understood not only for what it says but for the data it rests on.

Structured and unstructured data

RAG is usually associated with unstructured data: PDFs, Word files, HTML, email, technical documents. Enterprise knowledge is not limited to those. Structured data holds much of what matters — sales, finance, stock, production, customer and operations.

Advanced Enterprise RAG architectures can therefore use unstructured retrieval and structured data query together where needed.

In the same conversation a user might ask “what is the warranty period in this product's technical document?” and then “how many of this product did we sell in the last 12 months?” The first needs document retrieval; the second needs the enterprise data platform to be queried.

The real power of enterprise AI appears when these two worlds are brought together.

RAG and the data lakehouse

One important difference in the Kauzas approach to Enterprise RAG is that it does not treat the RAG system as separate from the enterprise data platform. The data lakehouse on the Kauzas Data Platform can serve as one of the shared data layers for both analytics and AI.

The aim is to use the organization's existing data infrastructure rather than create separate islands of data for AI projects.

What is a data lakehouse? The architecture in full →

On-premise Enterprise RAG

Enterprise RAG systems do not have to run on public AI services. Document processing, embedding, vector search, retrieval and suitable LLM inference components can all run on the organization's own infrastructure.

With its Kubernetes-based architecture, Kauzas supports architectures that run RAG and other AI workloads on-premise, in a private cloud or on suitable cloud infrastructure.

More on the on-premise AI platform →

Enterprise RAG compared with simple RAG

Both use the same method but do not do the same job. The difference is in the layers around retrieval.

Enterprise RAG with Kauzas

The Kauzas AI Platform is designed on the view that enterprise RAG applications are not made of the LLM and vector search layers alone. Using the data platform and the AI platform together, these layers can be positioned within one enterprise architecture:

• Enterprise data sources
• Data lakehouse
• Metadata and catalog
• Governance
• User and agent authorization
• LLM
• RAG
• AI applications

RAG can therefore stop being a standalone chatbot project and become part of the organization's Data & AI infrastructure.

Explore Kauzas AI Platform →

Frequently asked questions

What is Enterprise RAG?

Enterprise RAG is the application of a retrieval-augmented generation architecture together with enterprise data sources, authorization, metadata, governance, lineage and security policy.

Does RAG only work with documents?

No. RAG is commonly used with documents, but enterprise AI systems can use structured and unstructured sources together.

Is a vector database required for RAG?

Vector search is one of the retrieval methods commonly used in RAG systems, but not every information retrieval problem has to be solved with vector search alone. Structured query, search and other retrieval methods can be used alongside it.

Can RAG run on-premise?

Yes. Embedding, retrieval, the vector database and suitable LLM components can all run on the organization's own infrastructure.

How does user authorization work in Enterprise RAG?

Retrieval can be carried out taking the user's identity and data permissions into account, which prevents data the user is not permitted to see from being sent to the LLM as context.

What is the difference between an AI agent and RAG?

RAG focuses on supplying relevant information to an AI model. An AI agent can, beyond reaching information, use tools, interact with systems and carry out defined tasks. An agent may use RAG as one of its information retrieval mechanisms.