Enterprise RAG Applications: Which Architecture Do You Need?
When you plan an enterprise RAG application, the technical challenge goes beyond building a chatbot: how do internal information and current business data reach the language model securely? An assistant for manuals needs a different architecture from an application that retrieves delivery status or revenue figures. Three questions should guide your decision: who can access which information, how fresh must it be, and which systems need connecting?
In short: Document-based RAG usually benefits from a search index, while structured business data is often better accessed through controlled SQL queries or APIs. If your application needs both, a hybrid architecture combines these access paths. Permissions, update processes and operational requirements determine which approach is viable.
What building blocks does an enterprise RAG application need?
A RAG application connects a language model to external knowledge sources and supplies relevant information as context at runtime. RAG stands for Retrieval-Augmented Generation; the process does not automatically incorporate your business data into the model’s weights.
A typical architecture includes:
- Source connections: Document repositories, databases, business applications and APIs.
- Preparation: Extracting content, splitting documents into sections and preserving metadata.
- Retrieval: Selecting relevant information through full-text search, vector search or controlled data queries.
- Orchestration: Checking identity, choosing the appropriate access path and assembling context.
- Generation: Having the language model formulate an answer using the supplied context.
- Operations: Monitoring access, errors, execution times and updates.
Not every application needs a separate vector database. What matters is whether your search and data infrastructure can reliably support the required access patterns.
When is document-based RAG the right choice?
Document-based RAG works well for knowledge stored in manuals, policies, contracts or technical documentation. A search index makes that content accessible, but it must also reflect permissions and document updates.
How access works
A processing pipeline ingests documents, extracts text and creates smaller sections known as chunks. Each chunk receives metadata such as its source, version and access permissions. Embeddings support semantic search.
At query time, applications often combine full-text and vector search. Full-text search helps with part numbers and exact terms; vector search also identifies passages with similar meaning.
Permissions and freshness belong in the pipeline
Search must exclude unauthorized content before it reaches the model’s context. This requires the user’s identity and a reliable mapping between source permissions and index filters. A prompt instruction is not a substitute for access control.
Changes, deletions and revoked permissions must also reach the index. Delayed synchronization can leave outdated content or newly restricted documents accessible. Complex group memberships and document-specific exceptions are particularly challenging.
Integration effort therefore extends well beyond importing PDFs. Connectors, document quality and permission synchronization are often the more demanding tasks.
How do you connect structured data to RAG securely?
For current metrics and business transactions, structured data is usually better accessed through controlled queries or APIs than through embedded copies of tables. This keeps filtering, aggregation and business definitions closer to the authoritative systems.
A question such as “Which open orders does this customer have?” requires a precise data query rather than similarity search. Two common approaches are available:
Predefined tools and APIs
The application calls specific functions, such as retrieving delivery status. Parameters, permitted operations and response formats are predefined. This limits flexibility but makes security controls and integration with existing business processes easier to manage.
Controlled Text-to-SQL
A language model translates a question into SQL. Queries should run against approved views or a semantic model, rather than arbitrary tables with broad access rights.
Important safeguards include:
- read-only access with the minimum necessary permissions,
- validation against permitted data objects and query patterns,
- limits on execution time, result size and resource consumption,
- enforcement of row-level and column-level permissions in the execution path.
Power BI permissions do not automatically apply to a separate SQL connection. If you bypass a semantic model, you must independently enforce business rules and access controls along the alternative path. Even a live query is only as current as the source it reads.
When do you need a hybrid RAG architecture?
A hybrid RAG architecture makes sense when a request needs both explanatory document content and current business data. It combines distinct access paths instead of copying everything into the same search index.
For example, a service application might combine a machine’s maintenance manual with current spare-parts availability. The orchestration layer identifies the required sources, calls document search and an inventory API, and brings their results together.
Shared identifiers, such as machine IDs or part numbers, are essential. Without them, documents and records can easily be matched incorrectly. Partial failures also need explicit handling: if the inventory system is unavailable, the application must not imply that it has retrieved current stock levels.
| Criterion | Document index | SQL or API | Hybrid architecture |
|---|---|---|---|
| Suitable content | Knowledge documents | Metrics and transactions | Cross-source questions |
| Access rights | Identity and index filters | Rules in the data access path | Secure both paths |
| Freshness | Depends on synchronization | Depends on the source system | Account for different update times |
| Integration effort | Parsing and index maintenance | Data models and interfaces | Additional routing and linking |
On-premises LLMs in the enterprise: what does this mean for your RAG architecture?
Running an LLM locally moves model inference into your own infrastructure, but it does not automatically make the entire RAG application local. Fully internal processing also requires embeddings, search, document preparation and operational data to remain within the intended system boundaries.
Check the complete data flow
In particular, establish:
- Where are documents processed and embeddings generated?
- Where are search indexes, caches and conversation histories stored?
- What information appears in logs and telemetry?
- Which components require outbound connections?
On-premises operation also requires capacity planning, model deployment, security updates and monitoring. Whether you need GPUs, and how much capacity they must provide, depends on the model, context length and concurrent requests.
A cloud or hybrid setup can reduce operational effort, but it must satisfy your privacy, contractual and network requirements. The location of the LLM does not replace an authorization model.
How can you make effective use of existing data platforms?
The right RAG architecture builds on your existing data platform rather than recreating its governance in parallel. Databricks, Microsoft Fabric and Azure provide different building blocks; the interfaces and permission paths you actually use are what matter.
In a Databricks environment, you can build on curated data and governance with Unity Catalog. In a Microsoft environment, options include Microsoft Fabric for data provision, Power BI for semantic models and Azure AI Search for document retrieval.
For each connection, check which identity executes the query and which rules are enforced. A central platform alone does not guarantee consistent user permissions all the way through to the assistant.
How we approach implementation
At Ailio in Bielefeld and Hamburg, we start with data access paths and operational requirements. These provide the basis for a practical target architecture.
- Define the use case: Which tasks should the application support, and which sources does it need?
- Map sources and permissions: Document data owners, identities, groups, exceptional permissions and deletion processes.
- Set freshness requirements: Define acceptable delays for each source and how outages should be handled.
- Choose the architecture: Assess document indexing, controlled data queries or a hybrid approach against existing platforms.
- Build an end-to-end technical slice: Implement a complete access path, including authentication, authorization, updates and error handling.
- Prepare for operations: Establish ownership, monitoring, recovery procedures and resource limits before expanding.
This technical slice reveals early whether connectors, permissions and data quality work together. The focus is the architecture’s technical viability, not the isolated assessment of individual chatbot answers.
Your next step
Choose your architecture around data access and operational realities, not your preferred language model. If you want to connect documents and business data securely, explore our AI & BI Use Cases. Together, we can identify the RAG architecture that fits your systems and requirements.