Knowledge Graph
Definition
A Knowledge Graph is a structured representation of entities (people, concepts, products, documents) and the relationships between them. Knowledge graphs power semantic search, context retrieval, and explainable reasoning in enterprise AI systems.
Why it matters
The business case for Knowledge Graph.
LLMs without knowledge graphs hallucinate facts. Knowledge graphs ground AI in verifiable enterprise truth, which is why they are foundational to deterministic, auditable AI.
How SynthesisArc applies it
From concept to production.
PRISM's cognitive architecture integrates knowledge graph retrieval alongside vector search, so facts are grounded in structured enterprise data, not just learned patterns.
Related terms in AI Architecture
Cognitive Architecture
A Cognitive Architecture is the structural design of an AI reasoning system, including how it perceives input, accesses memory, plans actions, and learns from feedback. Cognitive architectures are what differentiate sophisticated AI from simple model wrappers.
PRISM
PRISM is SynthesisArc's seven-layer cognitive architecture for enterprise AI. The layers, perception, context, memory, reasoning, planning, action, and learning, combine deterministic and generative AI to deliver consistent, auditable outcomes.
LLM (Large Language Model)
A Large Language Model (LLM) is a foundation model trained on massive text datasets to predict and generate language. GPT, Claude, Gemini, and Llama are all LLMs.
Agentic AI
Agentic AI refers to AI systems that autonomously execute multi-step tasks toward a defined goal, using reasoning, tool use, memory, and self-correction. Agentic AI moves beyond chatbots that respond to systems that act.
Multi-Agent System
A Multi-Agent System is a coordinated set of AI agents working together on a shared goal, sharing context, handing off tasks, and avoiding conflicts. Multi-agent systems are required for any workflow that crosses departmental or functional boundaries.