Question 1

What is RAG?

Accepted Answer

RAG (Retrieval-Augmented Generation) combines search with AI generation. When you ask a question, the system first retrieves relevant documents from your knowledge base, then the AI generates an answer based on that retrieved context. This grounds AI responses in your actual data, reduces hallucinations, and provides citations. It's how you build AI assistants that actually know your information.

Question 2

How is this different from regular search?

Accepted Answer

Traditional search returns documents; RAG answers questions. Search for 'vacation policy' returns the policy document. RAG for 'how many vacation days do new employees get?' reads the policy and answers '15 days in the first year, prorated based on start date.' RAG understands intent, synthesizes across documents, and provides direct answers with sources.

Question 3

What can RAG systems search?

Accepted Answer

RAG works with virtually any text content: documents (PDF, Word, PowerPoint), wikis and knowledge bases, emails and conversations, database records, web content, help desk tickets, and more. We process your content into searchable embeddings and make it accessible to AI. Both structured and unstructured data can be included.

Question 4

How much does RAG implementation cost?

Accepted Answer

RAG costs depend on scale and complexity. Basic implementations with a focused document set typically range $30,000-$60,000. Enterprise systems with multiple sources, complex security, and high scale range $60,000-$150,000. Very large implementations can exceed $150,000. Ongoing costs include hosting and periodic reindexing as content changes.

Question 5

How accurate are RAG answers?

Accepted Answer

RAG significantly improves accuracy by grounding responses in your actual documents. Accuracy depends on content quality, retrieval effectiveness, and proper implementation. We implement evaluation benchmarks and continuous monitoring. Citing sources allows users to verify answers. For high-stakes use cases, we can add human review for quality assurance.

Question 6

Is my data kept private?

Accepted Answer

Absolutely. We offer fully private RAG deployments. Your documents stay on your infrastructure or in isolated cloud environments. The AI model can run locally or in your private cloud. No data is sent to third-party AI services. This is essential for sensitive content and regulated industries. Privacy-first architecture is our default approach.

Question 7

How do you handle document updates?

Accepted Answer

RAG systems need to reflect current information. We implement pipelines that detect document changes and update the search index. This can be real-time for frequently changing content or scheduled for more static documents. Version tracking ensures users see answers based on current documents, with options to search historical content if needed.

Question 8

How long does implementation take?

Accepted Answer

Basic RAG systems can be deployed in 6-10 weeks. Enterprise implementations with multiple sources and complex requirements typically take 3-5 months. We deliver incrementally—you see working search capabilities early while we expand coverage and refine accuracy. Time depends significantly on document volume and preparation needs.

RAG & Enterprise Search

What You Get RAG & Enterprise Search

Knowledge Assistants

Q&A Interface

Semantic Search

Meaning-Based

Citations

Source Links

Multi-Source

All Your Content

Private Deployment

Your Infrastructure

Access Control

Permission-Aware

Auto-Update

Current Content

Integration

Chat, Web, Apps

Analytics

Usage Insights

Answers From Your KnowledgeAI-powered search that understands your content.

Direct Answers

Not just documents

Grounded

No hallucinations

Private

Your data stays yours

DOCS

DOCUMENTS

WIKI

KNOWLEDGE BASES

DATA

DATABASES

TICKETS

SUPPORT HISTORY

What You Get with RAG & Enterprise Search

Semantic Search

Meaning-based

Citations

Verifiable answers

Access Control

Permission-aware

Frequently Asked Questions Common Questions About RAG & Enterprise Search

Understanding RAG

Implementation

MainSail Data builds knowledge assistants that find answers.