Enterprise knowledge hardly ever lives in one well-organized place. It is distributed across business documents, source code, operational logs, technical manuals, diagrams, and other materials. In enterprise environments, finding the right information often requires searching several systems or relying on experienced co-workers who know where the knowledge is stored.

At the same time, connecting raw corporate knowledge directly to public AI tools can introduce security, compliance, and operational risks. RAG for enterprise AI requires a different approach: the architecture must be designed around controlled data access, corporate identity, and clear security boundaries. Instead of sending sensitive knowledge to generic consumer chatbots, enterprises need solutions that can operate within their existing environment. That is why organizations need enterprise RAG solution providers that can integrate AI with their internal data while preserving governance and access control.

One of Brightgrove's project, was an enterprise RAG solution that makes internal knowledge easier to access through a single conversational interface. Employees can ask natural-language questions across company documents, source code, logs, and ERP data. English and French support allows different teams to work with the same enterprise knowledge base in their preferred language. The solution also helps train and onboard newcomers, so they can quickly find relevant information and understand existing systems and business processes.  

The solution is not limited to plain text. Its document-processing pipeline can extract text, images, diagrams, schemas, screenshots, tables, and other visual elements from documents. This makes the application especially useful for complex enterprise documentation where important information is often contained in both textual and visual form.

Enterprise RAG Architecture: From User Question to Governed Answer

The solution uses a Retrieval-Augmented Generation architecture, where enterprise information is retrieved first and then provided to the language model as context for the final answer.

The application is deployed in the client’s Microsoft Azure tenancy. Users interact with a web application built with ASP.NET Core. Azure OpenAI provides natural-language processing and answer generation, while Azure AI Search acts as the retrieval layer over enterprise content stored in Azure Blob Storage and other places.

The process follows several stages. When a user submits a question, the application creates an improved, search-optimized version of the question. Azure AI Search then retrieves the most relevant information from the enterprise knowledge base. Finally, the retrieved data, original question, and conversation history are passed to Azure OpenAI, which generates the final context-aware response.

In simplified form:

User question → query enhancement → Azure AI Search → retrieved enterprise context → Azure OpenAI → final answer

The search layer uses a hybrid vector search, which allows the system to retrieve information based on semantic similarity and keyword analysis. 

Enterprise information is continuously ingested through specialized ingestion pipelines. The system collects documents and ERP files from file shares, source code from GitHub, and logs. Scheduled indexing jobs keep the searchable knowledge base up to date. 

Enterprise RAG Security, SSO, and User Permissions

Security is an important part of the solution architecture. The application uses Microsoft Entra ID for Single Sign-On, allowing employees to authenticate with their existing corporate accounts instead of maintaining separate application credentials. 

User permissions are managed through role-based access control. Administrative functionality is protected by an Admin role that can be assigned to individual users or Entra ID security groups. In addition, access to individual knowledge domains is configured when Azure AI Search indexes are registered in the application. It helps to ensure that users can work only with the information made available to them.

The solution is deployed within the client’s Microsoft Azure tenancy, keeping corporate data and AI services inside client’s controlled Azure environment rather than transferring the knowledge base to an external public AI application.

Golden Dataset and Answer Quality Validation

To evaluate answer quality, the solution uses a golden dataset: a collection of representative user questions together with expected answers and relevant source documents. This dataset reflects real business scenarios and provides a repeatable benchmark for testing the system.

The application can be evaluated against this dataset after changes to prompts, retrieval logic, indexing, or AI models. Each generated answer is checked for relevance, factual correctness, completeness, and grounding in the retrieved enterprise data. This helps identify regressions and ensures that improvements do not reduce answer quality.

Such a validation process is very important in enterprise RAG, where response quality depends not only on the language model, but also on retrieval accuracy, document quality, and the context supplied to the model.

Usage Monitoring, Token Consumption, and Cost Control

The solution includes a dedicated Token Usage Analytics area that gives administrators visibility into how the enterprise RAG solution is being used. It tracks users activity, response performance, and token consumption for individual interactions, helping teams understand both system load and AI usage patterns. 

Token usage is broken down by processing stage, embedding generation, answer generation, etc. It allows to see where tokens are being consumed and which requests are the most expensive to process.

The collected data helps to control costs, performance analysis. Administrators can identify heavy users or unusually expensive requests and export usage information for reporting or billing purposes. Based on token consumption, the organization can also estimate AI-related spend and monitor how usage changes over time.

Conclusion

Secure enterprise AI requires more than a generic chatbot or a quick DIY implementation. Off-the-shelf tools often struggle with complex legacy data, security requirements, and enterprise-specific retrieval needs, while internal projects can stall on access control, data integration, and search quality.

That is why successful implementation often requires a custom RAG development approach tailored to the organization’s data and security constraints. The solution demonstrates how such an approach could combine governed enterprise data, secure access, reliable retrieval, and generative AI in a single architecture.