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 simplistic categories. We reject this. We celebrate the inherent diversity and fluidity of human behavior.
05 // SOLVING_FOR_COMPLEXITY
Memory & compounding impact
Simulate how repeated brand exposure builds over time. Determine the exact spacing required to create lasting impact without causing fatigue.
Secondary consequences
Identify how targeting one segment unexpectedly influences another. Predict competitive reactions before they trigger a chain reaction in the market.
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": "proprietary_divera_sync",
"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": "proprietary_divera_sync",
"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.
Authentic noise
We inject controlled stochasticity into the network to mirror the human factor—the occasional unpredictable choice that makes simulations real.
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 brand perceptions and past experiences collide to form a unique mental model.
Output layer
The decision: a single probability of action—purchase, loyalty, or attrition.