What is RAG?
Why can't our AI use our private data?
How do we make search actually understand what people are looking for?
Why are we spending so much time finding information across different systems?
RAG (Retrieval Augmented Generation) solves these problems by connecting AI to your private data, ensuring accurate and contextual responses based on your actual content.
The Problem with Traditional AI
Common Challenges
- AI can't access your private knowledge
- Search only matches keywords
- Information silos across platforms
- Content becomes outdated quickly
- Hours wasted finding information
Our Solution
- AI powered by your private data
- Semantic search understands meaning
- Unified knowledge access
- Always up-to-date information
- Instant relevant answers
How it Works
RAG operates in three main phases:
Indexing
Documents are processed into small chunks and converted into vector embeddings - think of it as creating a smart index that understands meaning, not just keywords. These are stored in your vector database for quick retrieval.
Retrieval
When a question comes in, RAG finds the most relevant information from your data using semantic search - it understands context and meaning, not just matching words.
Generation
The AI combines the retrieved information with its capabilities to generate accurate, contextual responses - always grounded in your actual data.
Use Cases
Common Challenges:
• Support agents spend 30+ mins per ticket searching docs
• Inconsistent answers across team members
• Outdated responses from generic chatbots
RAG Solution:
• Instant access to relevant documentation
• Consistent, accurate responses across channels
• Always up-to-date with latest product changes
Example:
Customer asks about a new feature released yesterday. RAG instantly retrieves the latest docs and provides accurate details, while a traditional chatbot would give outdated or generic responses.
Common Challenges:
• Knowledge scattered across 10+ platforms
• Hours wasted searching for information
• Critical details missed in manual searches
RAG Solution:
• Unified search across all platforms
• Semantic understanding finds relevant content
• Automatic discovery of related information
Example:
Engineer searches for "authentication error handling". RAG finds relevant docs across API specs, GitHub issues, and Slack threads, connecting information that would be missed in siloed searches.
Common Challenges:
• Hours spent fact-checking AI content
• Generated content misses key details
• Brand voice inconsistency
RAG Solution:
• Content grounded in your actual data
• Automatic fact-checking against docs
• Consistent brand voice and terminology
Example:
Marketing needs product comparison content. RAG generates accurate comparisons by pulling from product specs, pricing, and customer feedback - no manual research needed.
Common Challenges:
• Docs become outdated within weeks
• Developers waste time searching repos
• Implementation details hard to find
RAG Solution:
• Always synced with latest codebase
• Semantic code search
• Contextual implementation help
Example:
Developer asks "How do I implement OAuth?". RAG provides relevant code examples, configuration steps, and security best practices from your actual codebase and documentation.
Benefits
While RAG is powerful, implementing it properly requires significant infrastructure and expertise. Here's how RAGaaS delivers these benefits in a production-ready way:
Our Platform Provides:
• Real-time data synchronization
• Source attribution for every response
• Hybrid search with reranking
• Configurable confidence thresholds
How We Deliver:
Our battle-tested infrastructure ensures your RAG implementation stays accurate and reliable, with immediate updates when your content changes.
Your Infrastructure:
• Your S3-compatible storage
• Your vector database
• Your API keys
• Zero data retention by us
How We Deliver:
Our platform processes your data securely while keeping it in your infrastructure - perfect for regulated industries with strict privacy requirements.
Our Infrastructure:
• Process millions of documents
• Handle 500+ requests per minute
• Automatic load balancing
• Global infrastructure
How We Deliver:
Our platform handles the heavy lifting, delivering sub-200ms response times even at scale, with no maintenance burden on your team.
Platform Benefits:
• No infrastructure to build
• Optimized embedding costs
• Production-ready in minutes
• Pay only for what you use
How We Deliver:
Skip months of development and start seeing results immediately. Our platform turns RAG from a complex project into a simple API call.