RAG Knowledge System

Answers grounded in your data. Retrieval-augmented generation that connects AI agents to your documents, databases, and knowledge bases for accurate, context-aware responses.

Core Capabilities

Connect 50+ data source types
Semantic search and relevance ranking
Automatic data synchronization
Vector embeddings and similarity search
Custom metadata and filtering
Citation and source attribution
Chunking and context management
Real-time indexing and updates

Key Features

Multi-Source Integration

Connect documents, databases, APIs, and knowledge bases. Real-time sync keeps data current.

Semantic Search

Understands meaning and context, not just keywords. Finds relevant information accurately.

Source Attribution

Agents cite where information came from. Enables verification and builds trust.

Getting Started with RAG

1

Connect Your Knowledge Base

Connect documents, databases, and systems containing your proprietary information.

2

Configure Indexing

Set up how documents are chunked, indexed, and searched. Define metadata and filters.

3

Test Search Quality

Query your knowledge base and refine search parameters to improve relevance.

4

Deploy with Agents

Use RAG with your agents to provide grounded, accurate responses.

Frequently Asked Questions

RAG Integrations

Data Sources

  • • Salesforce (CRM data)
  • • SharePoint & OneDrive (documents)
  • • Notion & Confluence (wikis)
  • • Slack (conversations)
  • • Snowflake & BigQuery (warehouses)
  • • Email archives (Gmail, Outlook)

Infrastructure

  • • Vector databases (Pinecone, Weaviate)
  • • OpenSearch & Elasticsearch
  • • AWS & Google Cloud storage
  • • Custom embeddings
  • • LLM providers (OpenAI, Claude)
  • • On-premise deployment

Ready to Deploy RAG?

Give your agents grounded knowledge from your data.