RAG with Enterprise Guardrails: Building Safe AI Applications

Retrieval-Augmented Generation (RAG) has become the go-to approach for building AI applications that can access and use organizational knowledge. But implementing RAG in enterprise environments requires more than just connecting a vector database to an LLM.

The Challenge

Enterprise RAG systems need to:

• Enforce policies - Ensure AI outputs comply with organizational policies
• Maintain accuracy - Prevent hallucinations and ensure factual correctness
• Protect sensitive data - Filter out sensitive information from responses
• Provide auditability - Track what data was used and how decisions were made

Enterprise Guardrails

Our FlexiRAG framework includes guardrails for:

Content Filtering

Automated filtering of sensitive or inappropriate content before it reaches the LLM or after generation.

Fact-Checking

Verification of AI-generated content against source documents to prevent hallucinations.

Source Attribution

Clear attribution of sources used in RAG responses for transparency and auditability.

Policy Enforcement

Automated enforcement of organizational policies, compliance requirements, and data governance rules.

Implementation Patterns

We've seen successful RAG implementations follow these patterns:

1. Governance-First - Establish data governance before building RAG systems
2. Layered Guardrails - Multiple layers of guardrails (input, processing, output)
3. Human-in-the-Loop - Critical decisions require human approval
4. Observability - Comprehensive monitoring and logging

Getting Started

Ready to build RAG with guardrails? Explore our AI & Automation capabilities or learn more about FlexiRAG.

For organizations needing on-premises or SLM options, we can help you implement RAG while maintaining data sovereignty.