The only AI approach that combines accuracy, customization, and cost-efficiency at enterprise scale
RAG combines retrieval from your data with generative AI. Think of it as giving an AI assistant a library card to your organization's knowledge before answering questions.
Accuracy on your domain-specific data without expensive model fine-tuning. Your AI becomes an expert on your business.
Why base LLMs aren't enough for enterprise use
Base LLMs are trained on public internet data, not your proprietary information, knowledge cutoffs make them outdated.
Models confidently generate incorrect information, which is unacceptable for enterprise decisions and compliance.
Sending sensitive business data to external APIs creates security and compliance risks.
Can't verify where information comes from, making it impossible to audit or trust critical answers.
The advantages that make RAG essential for enterprise
Always up-to-date without retraining. New documents are immediately available for queries.
Answers backed by retrieved documents with citable sources you can verify.
Works on your proprietary data, terminology, and formats without model changes.
60% cheaper than continuous fine-tuning with faster implementation and maintenance.
Keep sensitive data in your infrastructure with full access controls.
See exactly which documents informed each answer for compliance and trust.
Compare RAG with other AI approaches
When to choose each approach for your LLM project. Understand the trade-offs in cost, accuracy, and maintenance.
Explore Comparison →Why context windows aren't enough at enterprise scale. When simple prompting breaks down.
Explore Comparison →Honest assessment: cases where simpler solutions work better. We turn down projects that don't need RAG.
Explore Limitations →Assess your organization's readiness for RAG implementation and create your roadmap.
Explore Maturity →Real-world applications by sector
| Industry | Problem | RAG Solution |
|---|---|---|
| Finance | Regulatory compliance | Regulatory document Q&A with citations |
| Healthcare | Clinical research | Clinical trial matching across studies |
| Legal | Case research | Contract analysis with precedent tracking |
| Aerospace | Design knowledge | Technical documentation retrieval |
| Manufacturing | Quality control | Historical defect analysis and prevention |
Quick assessment to determine if RAG fits your needs
Why enterprises choose RAG over other approaches
Let's assess if RAG is the right solution for your specific challenges