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Simulate A/B tests instantly to predict hook fatigue and conversion rates before you allocate a single dollar of live ad budget.

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Abstract & Key Takeaways

  • We are introducing an in-silico foundation model that acts as a digital twin for active consumer decision-making.
  • Unlike neuroscience models (e.g. TRIBE v2) that predict passive fMRI responses to stimuli, our engine predicts deterministic conversion actions using over $1B in ad spend telemetry.
  • This computational approach enables growth teams to simulate hook fatigue, pacing velocity, and CPA trajectories before allocating live capital.

Methodology & Architecture

A comparative analysis of predictive foundation models.

The Neuroscience Baseline

Mapping Perception

Meta´s TRIBE v2 leverages high-resolution fMRI data from over 700 subjects to predict how the brain processes multimodal sights and sounds in zero-shot environments. It models passive perception to external stimuli.

Ground Truth Variable
Blood oxygenation (BOLD signals) across 70,000 cortical regions.
The Behavioral Application

Mapping Conversion

eonik adopts the same predictive framework but replaces fMRI scans with deterministic clickstream data. It models the downstream, binary result of complex cognitive processing to map active response.

Ground Truth Variable
Deterministic conversion events and ROAS velocity.

Experimental Pipeline

01

Stimulus Ingestion

The engine parses visual, linguistic, and auditory stimuli into measurable tensors.

02

Behavioral Simulation

Run variants through the foundation model to predict fatigue velocity prior to capital allocation.

03

Empirical Deployment

Launch statistically verified variants to live ad accounts, scaling capital with mathematical confidence.

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