Train with what marketers actually have.
Purpose-built one-shot learning reduces the training data required to build a viable agent, so the stack works on sparse panels, small samples, and new categories without waiting for years of history.
NEURAL MODELS
OF INDIVIDUAL CAUSALITY.
Divera agents are neural models of individual consumer causality—a fusion of neuroscience and artificial neural networks built for the one environment where data is scarce, biased, and high-stakes: strategy.
01 // FROM_MIND_TO_MODEL
INPUT_STREAM_01
Biological signal
Neuroscience-based measurements capture the subconscious reactions surveys structurally miss—the part of the decision that people cannot actually self-report.
INPUT_STREAM_02
Response mapping
Reactions are converted into structured reads on strategy-relevant attributes—Belief Vector Direction (which way the opinion leans) and Belief Vector Magnitude (how strong the conviction is).
INPUT_STREAM_03
Neural synthesis
The output is a per-person decision engine: a neural model that can reproduce why that individual chooses what they choose, not just the aggregate trend.
02 // THE_SCIENTIFIC_MOAT
Why general-purpose AI doesn't work for strategy.
Most modern AI is trained on massive, clean datasets. Marketing rarely has that. Divera's architecture is engineered around a different regime: small, structured, individual-level data, where each sample has to do more work.
That requirement forced three design choices that still separate the stack from general-purpose LLMs and survey analytics.
The result is a model you can actually defend in a boardroom: the what is paired with a why, because the agent's reasoning is exposed rather than hidden behind a black box.
This is also what keeps the model usable when a new category appears with almost no prior data—a recurring condition in real enterprise work.
No black boxes in strategy.
Explainable AI (XAI) exposes the rationale behind each agent's decision—what weighed most, in what direction—so strategy leads can defend recommendations, not just quote a score.
Measure what surveys miss.
Agents are trained on neuroscience-based data—subconscious response captured at signal level—so models reflect real reactions, not the story respondents tell about themselves.
03 // TRAINING, IN PLAIN VIEW
04 // THE_HUMAN_ELEMENT
Not a Persona. A Person.
Conventional segmentation forces complex individuals into a handful of categories. We model them one at a time instead—outliers stay outliers, contradictions stay contradictions, and the agent can answer for both.
05 // SOLVING_FOR_COMPLEXITY
Memory & compounding impact
Repeated brand exposure builds and decays over time. Agents carry the trace from earlier scenarios into later ones, so the model can show the spacing that creates lasting impact without producing fatigue.
Secondary consequences
Targeting one segment moves adjacent segments—sometimes positively, sometimes against the play. The simulation surfaces those second-order effects before media commits, instead of after.
06 // THE_DECISION_ENGINE
{
"engine_type": "deep_behavioral_nn",
"layers": [
{ "id": "biological_input", "nodes": 512 },
{ "id": "latent_belief_space", "nodes": 1024 },
{ "id": "decision_output", "nodes": 1 }
],
"activation": "swish",
"confidence_threshold": 0.9842,
"trace_id": "0x4A2F"
}
"engine_type": "deep_behavioral_nn",
"layers": [
{ "id": "biological_input", "nodes": 512 },
{ "id": "latent_belief_space", "nodes": 1024 },
{ "id": "decision_output", "nodes": 1 }
],
"activation": "swish",
"confidence_threshold": 0.9842,
"trace_id": "0x4A2F"
}
07 // ARCHITECTURE_PILLARS
Non-linear processing
Unlike traditional branching logic, neural nets allow for complex, non-obvious interactions between memory, pricing, and brand perception.
Adaptive learning
As market conditions shift in the simulation, agents update their decisions from what they have already learned about each person—boundaries move with new runs, not a fixed template.
Calibrated stochasticity
Models carry calibrated randomness so agents reproduce the occasional unpredictable choice real people make. Without it, simulations look too clean to be useful.
08 // LAYER_STACK
Input layer
Captures raw biological and behavioral data. Encodes subconscious reactions as structured signals the rest of the model can combine and track over time.
Hidden layer
The belief space—where prior experience and current perception are weighted into a per-person decision pattern.
Output layer
The decision: a single probability of action—purchase, loyalty, or attrition.