Enterprise RAG Platform

Overview

Enterprise knowledge is often distributed across documents, internal systems and multiple data sources.

The Enterprise RAG Platform demonstrates an architecture for connecting generative AI with trusted organizational information so users can ask natural-language questions and receive responses grounded in enterprise data.

The goal is not simply to connect an LLM to documents. The solution is designed around retrieval quality, security, scalability, evaluation and user trust.

The Challenge

Organizations adopting generative AI often want employees or customers to interact with internal knowledge using conversational interfaces.

However, enterprise environments introduce challenges that simple demonstrations do not address.

These include:

• Large and continuously changing document collections
• Multiple enterprise data sources
• Access permissions
• Search quality
• Hallucination risk
• Source traceability
• Data security
• Performance
• AI evaluation
• Operational monitoring

A production-ready RAG architecture needs to address the complete information retrieval pipeline rather than focusing only on the language model.

Solution Architecture

The architecture separates the solution into several logical layers.

Data Ingestion

Enterprise information is collected from approved organizational data sources.

The ingestion pipeline prepares this information for retrieval by performing tasks such as:

• Document extraction
• Text processing
• Chunking
• Metadata enrichment
• Embedding generation
• Search indexing

Metadata can be used to preserve information such as document source, business area, timestamps and access-control information.

Retrieval Layer

When a user submits a question, the retrieval layer searches the indexed enterprise knowledge.

Retrieval may combine techniques such as:

• Semantic search
• Vector search
• Keyword search
• Metadata filtering
• Hybrid search
• Ranking

The objective is to retrieve the most relevant information before asking the language model to generate an answer.

Generative AI Layer

The retrieved information is provided to a foundation model together with the user's question and system instructions.

The model generates a response based on the retrieved context rather than relying only on its pretrained knowledge.

The application can also expose citations so users can inspect the original information behind the response.

Application Layer

Users interact with the platform through a conversational application.

The application layer is responsible for areas such as:

• Authentication
• Conversation management
• User experience
• Citations
• Feedback
• API integration

Evaluation and Observability

Production AI systems need continuous monitoring and evaluation.

Useful signals include:

• Retrieval relevance
• Answer groundedness
• Response quality
• Latency
• Token usage
• Cost
• User feedback
• Failed queries

Evaluation data can be used to continuously improve prompts, search configuration, chunking and the overall architecture.

Security Considerations