NeuroSymbolic RAG

The next generation of RAG: Combining the precision of Symbolic temporal logic with the reasoning power of Neural language models.

Standard RAG (Retrieval-Augmented Generation) is often brittle when applied to complex, evolving datasets. It relies on vector similarity, which lacks the causal and temporal awareness required for mission-critical intelligence. Pometry's NeuroSymbolic RAG natively integrates the temporal graph (Symbolic) to provide facts, causality, and context directly to the LLM (Neural).

Why NeuroSymbolic?

FeatureStandard Vector RAGPometry NeuroSymbolic RAG
LogicFuzzy similarityHard temporal causality
ContextNearby chunksFull network state across time
AccuracyProne to hallucinationsFact-grounded in graph structure
"When" QuestionsNear impossibleNative capability

The Architecture

NeuroSymbolic RAG treats the temporal graph as a High-Fidelity World Model that the LLM can query and reason about.

1. Symbolic Retrieval (Temporal Graph)

Instead of searching for "similar text," the system evaluates symbolic rules over time. Example: "Find all accounts created 24h before transaction X that have a shared device ID."

2. Neural Reasoning (LLM)

The LLM receives the Temporal Path as a structured fact. It does not need to guess if the connection exists; it performs higher-order reasoning on the meaning of that connection.


Intelligence Workflow

State the Intelligence Goal

Identify the "Why" behind a pattern. Query: "Why was this transaction flagged as high risk?"

Symbolic Temporal Trace

The system traverses the graph to find the sequence of events leading to the risk.

NeuroSymbolic Synthesis

The structured trace is passed to the LLM agent to generate a human-readable investigative narrative.

Real-World Advantage

While legacy systems require complex manual "wiring" of LLMs to their data silos, Pometry's architecture isnatively integrated. The graph is the memory of the agent.

Use Case: Cyber Threat Hunting

  • Standard RAG: "Find logs similar to 'Unauthorized Login'." (Returns 10,000 hits).
  • NeuroSymbolic RAG: "Analyze the lateral movement sequence starting from the initial breach at 10:04 AM. What systems were touched before the firewall was updated at 10:15 AM?"

Next Steps