Connecting enterprise data to deliver trusted answers with Databricks Genie and Lakebase

Imagine a finance leader asking, “How does FY26 revenue compare with FY25?” It sounds like one question, but the answer may be spread across SQL databases, Excel forecasts, CSV exports, and PDF commentary.

An analyst can pull those pieces together manually. The harder challenge is doing it quickly, consistently, and with enough context for the answer to be trusted. Traditional search may locate documents, while a basic chatbot may produce a fluent response, but neither necessarily completes the retrieval, analysis, comparison, and summarization the question requires.

The Lakebase Agentic RAG architecture on Azure connects those steps. It combines data preparation, semantic retrieval, agentic reasoning, language generation, application delivery, and automated deployment in one governed flow.

Why the answer is difficult to assemble

Enterprise knowledge rarely lives in one format. Different sources contribute different parts of the story:

• PDF documents hold policies, manuals, contracts, and standard operating procedures.

• SQL databases contain transactional and business data.

• Excel files capture reports, forecasts, and planning sheets.

• CSV files provide exports and analytical datasets.

The architecture does not ask users to navigate these sources individually. Instead, it turns them into a unified enterprise knowledge base that users can search and analyze through natural language.

The architecture, layer by layer

Each layer has a distinct role in moving from raw information to a grounded answer. The flow prepares enterprise data, retrieves the most relevant context, coordinates the interaction, reasons across the evidence, and presents the result in clear language.

Prepare the knowledge in Azure Databricks

Azure Databricks notebooks form the processing engine. They ingest information from source systems and convert raw content into knowledge that downstream AI components can use.

The preparation workflow completes five key tasks:

1. Extract data from each source system

2. Clean and normalize the collected data

3. Divide large documents into smaller, usable chunks

4. Generate vector embeddings for semantic retrieval

5. Build indexes that make the prepared knowledge searchable

This stage creates the foundation for accurate retrieval. If the content is incomplete, inconsistent, or poorly organized, every later step inherits that weakness.

Retrieve relevant context with Lakebase Search

Once the content is prepared, Lakebase Search provides the retrieval layer. It stores document chunks, metadata, vector embeddings, and search indexes, then uses them to find information connected to the user’s intent.

Its core capabilities include semantic search, vector similarity search, metadata filtering, and fast retrieval. Together, these capabilities help the system find relevant meaning rather than depend only on exact keyword matches.

Orchestrate the experience with Databricks Apps

Databricks Apps provides the application layer between users and the AI system. It receives a question, retrieves context from Lakebase Search, builds prompts for the language model, and enforces authentication and authorization.

For the user, this creates one conversational entry point. For the team operating the solution, it provides a controlled place to coordinate interactions among users, enterprise data, and AI models.

Reason across the evidence with a Genie Agent

The Genie Agent acts as the intelligence layer. It understands user intent, reasons across multiple steps, uses search tools and data sources, and maintains conversational context.

That creates an experience that goes beyond a simple exchange of questions and answers. A revenue comparison, for example, may require the agent to retrieve several sources, analyze the numbers, compare periods, and enrich the context before generating a response.

Generate a clear response with OpenAI

OpenAI models provide natural language understanding and response generation. The model uses the retrieved and enriched context to interpret the question, analyze relevant data, summarize the findings, and express them in a language that is easy to understand.

A clear response is only useful if it is based on the right evidence. Retrieval supplies that evidence, while generation turns it into a readable answer. Here, Lakebase Search supplies the relevant evidence, the Genie Agent coordinates the reasoning, and OpenAI converts the resulting context into an answer. Clear responsibilities make the architecture easier to understand, test, and improve.

How the revenue question moves through the system

With the layers connected, the question about FY25 and FY26 follows a clear path:

1. Databricks notebooks ingest PDFs, SQL data, Excel files, and CSVs, then clean the content, divide documents into chunks, and generate embeddings.

2. The prepared content is indexed in Lakebase Search.

3. The finance leader submits the question through a Databricks App.

4. Lakebase Search retrieves the most relevant content.

5. The Genie Agent analyzes the evidence and enriches the context.

6. OpenAI generates a grounded, readable response.

7. The Databricks App presents the answer and supports additional questions.

What feels like one seamless conversation is therefore a coordinated sequence. Each layer contributes a specific capability, and the quality of the final answer depends on how well those layers work together.

From working prototype to production platform

A useful answer is only part of the production story. The architecture also needs consistent delivery and maintenance, with Azure DevOps CI/CD providing four essential capabilities:

• Source control keeps application and pipeline changes manageable.

• Automated testing helps teams validate changes before release.

• Build pipelines package changes through a repeatable process.

• Production deployment workflows promote releases consistently.

Combined with authentication, authorization, metadata filtering, and governance, these practices reduce manual effort while supporting faster and more consistent releases.

What the connected architecture delivers

When the components operate as one connected flow, the architecture delivers six practical benefits:

• Organizations gain unified access to structured and unstructured enterprise knowledge.

• Responses become more accurate because they are grounded in retrieved context.

• Anchoring generation in relevant evidence helps reduce hallucination risk.

• Access controls and governance support a scalable and secure foundation.

• Reducing manual discovery and synthesis helps teams make decisions faster.

• CI/CD enables more consistent and automated delivery.

To put this architecture into practice, start with a focused pilot around one business question, such as the revenue comparison introduced earlier. Use it to assess retrieval quality, response usefulness, permissions, and operational readiness before expanding to additional use cases.

Conclusion

The Lakebase Agentic RAG architecture on Azure creates a practical path from fragmented enterprise information to contextual answers. Azure Databricks prepares the knowledge, Lakebase Search retrieves it, a Genie Agent reasons across it, OpenAI generates the response, and a Databricks App delivers it to the user. The key lesson is that no single component creates trust on its own. Trust comes from the complete flow.

For the finance leader asking how FY26 revenue compares with FY25, this means getting an answer grounded in the relevant figures and commentary without manually bringing separate sources together. Access controls govern which information users can access, while automated testing and deployment help the developers maintaining the application to validate and release changes consistently.