What Is a Context Graph?
Key Takeaway: A context graph is a structured representation of relationships between business entities, signals, and decisions. It allows AI systems to interpret data as part of a broader relational network. But a context graph alone is not enough to drive decisions.
In complex organizations, revenue connects to marketing and product. Product usage influences churn. Operational constraints affect strategy. Without relational structure, AI cannot reason accurately.
What Is Contextual Reasoning →
Context graphs provide the connective layer between data and meaning. They answer the question: how do the pieces of this business fit together?
Nodes are business entities: customers, deals, campaigns, features, teams. Each node represents something the system needs to track.
Edges are relationships between nodes. A campaign influences pipeline. A feature adoption rate impacts retention. An edge defines the connection and its direction.
Attributes are properties attached to nodes and edges. A deal node has a value, a stage, an owner. An edge might have a strength or confidence score.
Temporal context captures time-based relationships. A campaign launched today affects pipeline next month, not today. Without temporal context, the graph treats all signals as simultaneous.
It is a relationship model, a semantic mapping structure, and a foundation for contextual reasoning. It is not a visualization tool, a BI dashboard, a predictive model, or a standalone database.
A context graph can tell you that Campaign A influences Pipeline B, which feeds Revenue Target C. That's valuable structural knowledge. But it doesn't tell you whether Campaign A is actually performing right now, whether Pipeline B is trending up or down this week, whether Revenue Target C is at risk based on current signals, or what you should do about any of it.
Context graphs provide the map. Decision systems provide the navigation.
The missing layers are: continuous evaluation (a reasoning engine that monitors signals in real time), action recommendation (structure without action is a reference document, not a decision tool), hypothesis monitoring (relationships need to be tested against assumptions that evolve over time), and decision linkage (signals need to be tied to specific decisions, not just entities).
Decision Monitoring vs Dashboards →
DecisionX uses context graphs as one layer within its 9-layer ontology. The context graph maps relationships between signals, hypotheses, and decisions. But DecisionX adds reasoning, monitoring, and action recommendation on top of the graph structure. When a signal changes, the graph tells Green which related entities are affected. Green then evaluates the impact, checks whether assumptions still hold, and recommends the next move with a visible reasoning chain.
DecisionX puts Decision AI into practice by continuously monitoring signals, structuring context, reasoning across hypotheses, and surfacing the next best action within a single system.
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