Graph RAG Engine

Zero-Hallucination AI Knowledge Retrieval Engine

Standard vector search RAG hallucinates missing context. Memora’s Graph RAG uses graph topology constraints to enforce grounded accuracy.

The Problem: Standard Vector RAG Hallucinates Enterprise Data

Vector embeddings rely purely on statistical keyword similarity, frequently returning irrelevant text chunks and hallucinating non-existent corporate policies or technical facts.

The Memora Solution

Memora combines dense vector similarity with structural graph traversal (Graph RAG), constraining LLM outputs strictly to verified topological paths in your knowledge graph.

Vector RAG vs Memora Graph RAG Pipeline

Traceable evidence paths linking daily SaaS signals into grounded AI answers.

Siloed SaaS Data Signals
Slack Threads & DMs
GitHub PRs & Commits
Jira Tickets & Issues
Google Meet Transcripts
Notion & Confluence Docs
Memora Graph RAG

Extracts entities, links PRs to discussions, and builds a living topological graph of decisions.

Grounded Answer Output
Zero-Hallucination RAG

Direct answers verified against exact GitHub commits and Slack message IDs.

Traceable Evidence Chain

Key Enterprise Capabilities

01.

100% zero-hallucination guarantee via topological graph constraints.

02.

Multi-hop relational reasoning across complex engineering dependencies.

03.

Reciprocal Rank Fusion (RRF) combining vector embeddings and graph path distance.

04.

Verifiable citations for all generated answers.

Frequently Asked Questions

Why is Graph RAG better than traditional RAG?

Graph RAG preserves multi-hop relationships between tickets, code, and discussions that vector similarity loses.

Does Memora work with private LLM deployments?

Yes. Memora supports self-hosted Llama 3, DeepSeek R1, and enterprise OpenAI endpoints.

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