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The Core Engine transforms scattered conflict traces into a unified graph structure that preserves what matters: when things happened, why they escalated, and who can be held accountable.
The Core Engine is under active development. Architecture and capabilities are being refined continuously.
Standard AI approaches collapse conflict into statistical patterns, losing the temporal causality that explains escalation, the actor networks that shape outcomes, and the provenance chains that enable accountability. The Core Engine preserves this structure, enabling AI to reason about conflict like trained professionals.
Events exist in sequence. Claims evolve over time. Positions shift. The graph preserves when everything happened, not as metadata, but as first-class structure.
Understanding that "Event A caused B, which escalated to C" requires explicit causal links—not statistical correlation. The graph encodes why things happened.
Every fact traces back to source documents with timestamps. No hallucinations, no fabrications. Just verifiable claims with full audit trails.
The Agentic Conflict Ontology (ACO) isn't just a schema—it's the encoded reasoning patterns of trained conflict professionals. It defines how actors relate, how claims connect to evidence, and how events chain causally.
Actor Classification
Principals, agents, veto players, bridge actors. Each with defined relationships and influence patterns.
Claim-Evidence Binding
Positions vs. interests vs. red lines, all linked to supporting documentation with confidence scores.
Causal Relation Types
Triggered-by, escalated-from, blocked-by, enabled-by—explicit causality for reasoning.
Every LLM response about a conflict case is grounded in this graph. No hallucinations. No fabricated facts. Just structured, traceable, causally-connected intelligence.
Actors, claims, events, evidence. Each with timestamps, source attribution, and confidence scores.
Causal links, temporal sequences, contradictions, alliances, oppositions—relationships that matter.
Query the temporal layer for timelines, actor layer for networks, evidence layer for compliance. All from one graph.
Conflict traces are everywhere: emails, documents, meeting notes, chat logs. The Core Engine ingests this scattered information and reifies it into persistent graph objects that AI can reason over.
Escalation Signals
Detect early warning patterns before they cascade into full conflict.
Claim Evolution
Track how positions and counter-positions shift over time.
Actor Dynamics
Map shifting alliances, emerging veto players, potential bridge actors.
The ACO encodes how trained professionals actually think about conflict—not statistical patterns, but structured cognitive frameworks.
Track claims, map interests vs. positions, identify common ground. Maintain neutrality through structural clarity, not opinion.
Model actor networks, trace escalation pathways, identify structural constraints. Surface early warning signals through pattern recognition.
Maintain long-term temporal awareness, track shifting coalitions. Preserve provenance for auditability across multi-stakeholder complexity.
The Core Engine is under active development and available as a pilot demo through our API for developers and through the Resolution Suite for end users. Let's discuss your use case.