How to Get Enterprise RAG Right

What is enterprise RAG?
‍

Enterprise RAG (Retrieval-Augmented Generation) builds on standard RAG technology to address the complexity and scale of large organizations.

In a typical RAG workflow, additional context is added to the input of LLMs to help ground their output from preselected and specially processed sources. Enterprise RAG solutions are designed to integrate seamlessly with internal systems and extract information from diverse, unstructured data stored across an enterprise.

‍

‍

An enterprise RAG architecture enables businesses to power generative AI applications at scale, maintaining stronger control over what data goes in and what responses come out. The result? Smarter decision-making backed by enterprise-grade performance and robust security measures.

Benefits of enterprise RAG
‍

An enterprise-ready RAG system offers several powerful advantages for large organizations. Benefits include:

• Results your team can trust and verify: Processes diverse, unstructured data using high-precision ingestion and retrieval techniques. Fine-grained attribution ensures that responses generated by LLMs are accurate and easily verifiable, giving teams the confidence they need to make data-driven decisions.
• Scalable without compromise: Handles millions of pages of multi-modal content from various sources — without compromising on performance, accuracy, or speed. Whether you’re processing large volumes of text, images, video, or a mix of data types, the system scales to meet the needs of a growing enterprise.
• Security that’s airtight – and CSO-approved: Delivers advanced protection for sensitive data, ensuring compliance with stringent regulatory standards. Robust security features such as document-level access controls, SSO integrations, and flexible deployment options (e.g. on-premises, air-gapped, private cloud) provide airtight security, making it easy for your Chief Security Office to sign off.
• Greater value, delivered faster: Streamlines the path from prototype to production, with deployment often completed in just weeks. Organizations can gain valuable insights and results quickly, enabling immediate impact across multiple departments and use cases.

Enterprise RAG challenges: Why your RAG POCs don’t scale‍

While it may seem tempting to roll out a RAG system with just a few lines of code and open-source libraries, enterprises often find themselves running into a wall when trying to scale.

The ease of prototyping can create a false sense of security, making it seem like RAG is a plug-and-play solution. But when you move from proof of concept to enterprise-scale deployment, the hurdles become far more complex. Here's why:

• Data everywhere, but not easily accessible: Enterprise data is scattered across a labyrinth of systems—data lakes, on-premises legacy systems, and cloud-based platforms (just to name a few). For RAG to function properly, it needs seamless access to all these disparate sources. But pulling data from each of these systems isn’t just about calling a few APIs; it’s about designing a robust architecture that can connect and harmonize these data streams for consistent, accurate retrieval.
• Complex data types: Enterprises manage a wide array of data types, from structured data (like spreadsheets and customer records) to unstructured data (like emails, PDFs, and videos). A basic RAG system can’t easily process these complex data types, making enterprise-grade solutions necessary for accurate and comprehensive data retrieval.
• The performance vs. latency vs. cost balancing act: When building a RAG application, every decision made during data ingestion, retrieval, and content generation stages directly impacts system performance, response time, and costs. Balancing these elements becomes increasingly difficult as the system scales to handle millions of queries, complex user interactions, and growing datasets—all while maintaining cost-efficiency. Without careful optimization, a homegrown RAG solution can quickly spiral into performance bottlenecks and exorbitant compute costs.
• Data governance and compliance: Enterprises, particularly those in regulated industries like finance and healthcare, must adhere to strict data governance and compliance protocols. Handling sensitive data, whether it’s customer information or internal documentation, requires a RAG architecture that incorporates advanced security measures. Without these protections, an enterprise risks legal liabilities, security breaches, and reputational damage.

By recognizing these challenges early, your organization can avoid the trap of reactive troubleshooting and instead build a RAG architecture that’s not only effective but also scalable, secure, and future-proof.

5 principles for building enterprise-ready RAG
‍‍

How do you build a RAG solution that can handle enterprise-level complexity and performance standards?

We’ve identified five core principles to guide any enterprise RAG implementation.

1. Understanding your content wherever it is, as it is

Whether your data lives in Google Drive, SharePoint, or a combination of systems, your enterprise RAG solution must accurately ingest it.

What to focus on:

• High-performance ingestion ensures compatibility with a variety of platforms and file types.

• Handle content diversity, from annotated PDFs to audio files, seamlessly.

• Enable real-time content updates so users access the most current information.

• Ensure the system can scale to handle high volumes of data without sacrificing performance.

‍

2. Query comprehension that goes deeper

Enterprise RAG isn’t just a search tool—it’s designed to understand the precise intent behind a query.

What to focus on:

• Adapt to industry-specific jargon and acronyms.
• Break down complex, multi-part questions into smaller pieces for better accuracy.
• Maintain context across sessions, so users don’t have to re-explain.
• Detect intent and intuitively adapt based on the type of response a user is looking for (e.g. information retrieval vs. conversational reply).
  ‍
  ‍

3. Matching answers with precision

It’s critical that users of an enterprise RAG application receive verified, relevant answers they can trust.

What to focus on:

• Minimize hallucinations with advanced ingestion and retrieval practices.
• Implement out-of-domain-detection for unanswerable queries so the system doesn’t try to make something up.
• Ensure deterministic controls for highly regulated content, like questions about compliance policies.
• Offer tailored responses depending on the user’s role and the query’s sensitivity.
• Maintain access control levels (ACL) so only authorized users can access sensitive information.
  ‍
  ‍

4. Delivering an exceptional answer experience

Enterprise users want more than just information—they want answers delivered with clarity and speed.

What to focus on:

• Balance latency with performance to keep response times under three seconds.
• Use feedback mechanisms to refine future responses.
• Provide source attribution for transparency and compliance.
• Ensure the same query receives consistent answers for reliability.
• Connect with various front-end user interfaces, from mobile apps to chatbots, for seamless accessibility.

‍

5. Prioritizing security, compliance, and flexibility

Enterprise stakeholders demand solutions that safeguard sensitive data and adapt to existing infrastructure.

What to focus on:

• Incorporate encryption, secure data storage, and access controls to prevent unauthorized access to sensitive information.
• Consider secure deployments (on-premises or air-gapped) for critical industries.
• Maintain compliance with regional and industry standards for data.