AGENT-BASED GRAPH REASONING
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
Financial Report
Email Log
Compliance Policy
Risk Assessment
Market Volatility
Q3 Revenue
Insider Risk
Reasoning Output
Synthesizing...
Based on the Financial Report and Email Log, risk is elevated. [Warning: Connection to Compliance Policy missing]
System Metrics
Depth
1 Hop
Confidence
Low
Privacy
Standard
Standard RAG
G-RAG Agent
Federated
REASONING...
IDLE
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.
System Definition
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
Inside the Engine
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."
Zero-Knowledge Proofs
Cryptographically prove that an answer is correct and derived from a specific document, without revealing the document itself.
Federated Learning
Models improve by learning from data across silos (e.g., different hospitals) without that data ever moving or being decrypted.
Optimized for Real-Time Decisions
12ms
Average Graph Update Time
Deployed for High-Stakes Decisions
How G-RAG Fits into AHOY
Data Sources (AVML, DBs)
G-RAG Engine
AHOY Co-Pilot / API