AHOY GRAG
Real-Time Agent-Based Graph Reasoning for Sovereign and Sensitive Data Environments.
Decentralized reasoning, cryptographic verification, and real-time decision intelligence without exposing raw data.
SANDBOX DEMO
v1.4.2-beta
Representative simulation. Production systems operate across distributed nodes.
Why Traditional RAG Breaks at Scale
Flat Retrieval
Vector similarity ignores complex relationships. It finds keywords, not concepts or causality.
Centralized Risk
Ingesting all enterprise data into one vector store creates a massive honeypot for attackers.
Poor Adaptability
Static indexes become stale instantly. Re-indexing terabytes of data is too slow for real-time ops.
No Guarantees
LLMs hallucinate. Standard RAG offers no cryptographic proof that an answer was derived from the retrieved text.
What AHOY GRAG Is
AHOY-G-RAG is a real-time, agent-based GraphRAG system that performs distributed reasoning over dynamically evolving graphs.
Live Knowledge Graphs
Agent Sub-graph Mining
Decentralized Reasoning
Verified Synthesis
Agent-Based Graph Reasoning Architecture
Moving from static document retrieval to dynamic, multi-agent reasoning.
Ingestion
Unstructured Data
Dynamic Graph
Nodes & Edges
Agent Engine
Multi-Hop Reasoning
DATA LAYER
GRAPH LAYER
REASONING LAYER
Agent-Based Graph Reasoning
Agents operate autonomously on sub-graphs. They don't just "fetch" text; they understand relationships. An agent assigned to "Risk" will traverse the graph looking for connections between "Financial Reports" and "Email Logs" that a keyword search would miss.
Sub-graph specialization
Continuous local learning
Parallel inference & collaboration
Security Is Embedded, Not Added
"All reasoning can be verified without revealing underlying data."
Optimized for Real-Time Decisions
Deployed for High-Stakes Decisions
How G-RAG Fits into AHOY
Data Sources (AVML, DBs)
G-RAG Engine
AHOY Co-Pilot / API