Should I Buy or Build My RAG Infrastructure?

This is Part 1 of our three-part series exploring the build vs. buy decision for Retrieval-Augmented Generation (RAG) solutions. In this piece, we'll examine why RAG matters for enterprise search and what to consider when evaluating implementation options.

The Enterprise Data Challenge

In today's fast-paced work environment, employees lose ± 1 hour every day searching for information, resulting in lost productivity and missed opportunities. This isn't just an inconvenience; it's a significant business challenge that affects your bottom line. Modern organizations struggle with data fragmented across multiple systems:

• Emails and communications
• Project management tools
• Internal documentation
• Customer relationship management systems

This fragmentation creates three critical problems:

1. Wasted Time: Valuable hours lost in manual searches
2. Communication Bottlenecks: Teams working in silos
3. Missed Insights: Decision-making hindered by incomplete information

Why RAG Matters

Traditional generative AI models like ChatGPT or Gemini offer compelling opportunities for streamlining processes and improving productivity. However, using these models alone isn't enough to create a competitive advantage, anyone can use them for basic tasks like writing emails or summarizing documents.

The real differentiator lies in applying AI to your organization's proprietary data and unique business processes. This intellectual property, spanning customer histories, product designs, research findings, and countless other assets... contains the domain-specific expertise that gives your company its edge. When combined effectively with AI, this data becomes your secret weapon, but only if you can properly manage the inputs, outputs, and associated costs.

Understanding RAG: The "Open-Book Test" for AI

Retrieval-Augmented Generation (RAG) represents a breakthrough in how we interact with enterprise data. Think of it as giving AI an "open-book test", instead of relying solely on its general knowledge, it actively consults your organization's specific information to provide accurate, contextual answers.

Traditional AI models, while powerful, face several limitations when dealing with enterprise data:

• They lack access to your private, domain-specific information
• They can produce "hallucinations" or inaccurate responses
• They may mishandle sensitive data or intellectual property
• They pose risks when autonomous agents act without human oversight

RAG addresses these challenges by:

1. Retrieving relevant content from your data sources
2. Using this information to augment AI prompts
3. Generating responses grounded in your actual business context
4. Minimizing the risk of hallucinations and inaccuracies

The Build vs. Buy Decision

As organizations look to implement RAG, they face a critical choice: build a custom solution or invest in a commercial platform. This decision requires careful consideration of several factors:

Expertise Required

• Custom Build: Requires deep expertise in data management, ML engineering, and DevOps
• Commercial Solution: Reduces need for specialized skills through standardization

Infrastructure Needs

• Custom Build: Demands robust infrastructure for hosting and maintaining RAG workflows
• Commercial Solution: Offers managed services that handle infrastructure complexity

Governance & Security

• Custom Build: Requires implementing comprehensive security and governance frameworks
• Commercial Solution: Provides built-in security features and compliance controls

The Consultant Conundrum

When considering a custom build, many organizations look to consultants for implementation. However, this approach comes with significant risks:

• RAG technology is evolving rapidly, with new developments emerging monthly
• Consultants may build on soon-to-be-outdated architectures
• Once the consultants leave, your team inherits complex infrastructure that requires continuous updates
• Maintaining and updating RAG systems demands deep expertise that goes beyond typical IT maintenance
• The cost of keeping up with evolving best practices often exceeds initial implementation costs

This challenge is particularly acute because RAG isn't a "build once and forget" solution... It requires constant adaptation to new language models, embedding techniques, and retrieval methods. A commercial solution, maintained by a dedicated team focused solely on RAG technology, often provides more sustainable long-term value.

Looking Ahead

In Part 2 of this series, we'll dive deeper into the specific tradeoffs between building your own RAG workflow and adopting a commercial solution. We'll explore real-world examples and provide a detailed framework for making this critical decision. Finally, in Part 3, we'll show you how to implement intelligent search capabilities seamlessly into your daily operations.

The future of enterprise search lies in making your organization's collective knowledge instantly accessible. Whether you choose to build or buy, the key is selecting an approach that aligns with your resources, expertise, and business objectives.

This article is Part 1 of a three-part series on modernizing enterprise search and knowledge management.