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TACITUS isn't another chatbot or document summarizer. It's the foundational infrastructure that transforms scattered conflict traces into structured, machine-queryable knowledge graphs -- with temporality, causality, and provenance preserved.
LLMs flatten time into vectors. "Promise made" and "promise broken" become statistical neighbors, not a causal sequence spanning months.
Transformers learn correlations, not causation. "Before" doesn't mean "caused." This distinction is fatal in any dispute context.
Generative models cannot trace outputs to inputs. In legal, HR, and diplomatic contexts, unverifiable claims are worthless.
We use LLMs for what they're good at -- extraction and classification. Then we apply ontology-governed, deterministic algorithms to reason over the structured knowledge graph.
Raw sources: emails, transcripts, docs, notes.
LLMs identify actors, claims, events, evidence.
ACO: type binding, relation mapping, temporal ordering.
Graph-native storage. API-queryable. Deterministic.
What makes this different from RAG, vector search, or document summarization:
Typed Knowledge Graph
Not embeddings. Explicit typed nodes (Actor, Claim, Event, Evidence) with explicit typed relations (CAUSED, OPPOSES, SUPPORTS).
DAG-Preserved Temporality
Events are Directed Acyclic Graph-ordered. The system knows that Event A preceded and caused Event B.
Full Provenance
Every fact links to a source document, page, and text span. Hash-verified. Zero hallucination on keyed facts.
Agentic Conflict Ontology
Encodes professional mediator reasoning patterns: interest vs. position, escalation triggers, de-escalation paths.
All tools are in active development
Map your conflict. Structure actors, claims, and events into actionable timelines.
Free demo — in active development. 5 early users.
ExploreHybrid mediator for scenario exploration and graph-grounded AI reasoning.
Targeted launch: Q2 2026.
Contact UsStress-test strategies and narratives against structured conflict data before deployment.
Free demo — in active development.
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