Narragist is a narrative intelligence engine that applies complex systems analysis to public discourse — extracting implicit assumptions, modeling convergence dynamics, and mapping the attractor landscape beneath the surface. Not fact-checking. Not bias-labeling. Something deeper.
Two sources can agree on every fact and still tell completely different stories through selection, emphasis, and framing. The interesting question isn't what someone said — it's what you'd have to already believe for their framing to feel coherent.
Existing tools tell you that Source A leans left and Source B leans right. That's table stakes. Narragist maps the deeper architecture: the causal assumptions, moral foundations, and worldview substrates that make each framing feel natural to its audience — and tracks how those structures evolve, converge, and reorganize over time with the rigor of a dynamical system.
Most media analysis tools operate at the surface. Narragist is built to descend through five layers of narrative structure, from observable claims down to the worldview substrates that generate them.
Layers 4–5 are where the real analytical value lives — and where Narragist focuses.
Extracting what must be true for a statement to make sense. Linguistically grounded, high-confidence — the bedrock of the extraction pipeline.
Identifying the unstated bridge principles connecting evidence to conclusions. "Crime rose after the policy, so the policy failed" relies on hidden causal warrants.
"Tax relief" vs. "tax cut" vs. "revenue reduction" — frame choice reveals conceptual structure. Systematic detection of framing patterns across sources.
Analyzing which moral foundations a narrative appeals to — care, fairness, loyalty, authority, sanctity, liberty — and how the weighting differs across clusters.
When a story breaks, there's a brief period of high-entropy variation — many possible framings competing for adoption. Then social and tribal dynamics pull interpretations toward attractor basins shaped by deeper ideological commitments. This isn't metaphor. It's measurable.
Narragist draws on the formal tools of statistical physics, opinion dynamics, and critical transitions theory to model how narratives propagate, converge, and shift. The same mathematics that describes phase transitions in physical systems describes the moment a scattered discourse crystallizes into competing tribal frames.
How quickly does within-group framing variance collapse after a story breaks? Faster convergence signals stronger attractor pull from existing worldview commitments.
Detecting critical thresholds where narrative landscapes reorganize — when gradual frame drift gives way to sudden, discontinuous shifts in dominant interpretation.
Rising autocorrelation and variance in narrative embedding space as leading indicators — the same signatures that precede transitions in ecological and financial systems.
Tracing the origin points where new frames are seeded and tracking their propagation dynamics through discourse networks.
Narrative convergence signals and frame-shift detection as leading indicators for market movements. Structured quantitative signals from discourse dynamics, not sentiment scores.
Anticipate how events will be framed across media ecosystems before narratives crystallize. Understand the landscape, don't guess at it.
Longitudinal datasets tracking narrative dynamics over time. Testable predictions grounded in extracted worldview models, not post-hoc interpretation.
Narragist is in active development. Sign up to get notified as we approach launch, or reach out directly to discuss the project.
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Or reach out directly at ross@narragist.com