Enterprise RAG
The enterprise RAG that knows your documents
Hybrid Retrieval-Augmented Generation, pgvector vector DB, Knowledge Graph. Sourced answers, zero hallucination, total sovereignty.
What is enterprise RAG?
RAG (Retrieval-Augmented Generation) is a technique that allows a large language model (LLM) to answer based on your real documents rather than its training memory. Instead of generating an answer from what it has "learned", the system first retrieves relevant passages from your data, then lets the model formulate an answer grounded exclusively in those sources.
In business, this is essential: a standalone LLM hallucinates, ignores your internal procedures and doesn't cite its sources. RAG turns generative AI into a reliable, traceable and compliant tool.
The Wardian RAG architecture
Haystack
Hybrid RAG orchestration (lexical + semantic). Indexing, querying, reranking.
pgvector
Native PostgreSQL vector DB. Encrypted embedding storage, ultra-fast queries.
Knowledge Graph
Cognee/Kuzu — links entities (people, projects, contracts) for context.
Mem0
Persistent memory: preferences, ongoing projects, past decisions.
Connected sources
- Documents — PDF, Word, Excel, PowerPoint, Markdown
- Email & messaging — Gmail, Slack, Outlook
- Business tools — Jira, Linear, Notion, Drive
- Custom connectors — via MCP protocol, in Enterprise plan