DEEPSEEK AGENT ENGINE

Agentic Consumer Insights Lab & Persona Simulator

Consensus Score
42/100
Fragmented polarization
Churn Hazard Rate
38%
Critical threshold in SMB/Dev
Primary Friction Point
Mandatory AI Addon
Lock-in & unbundled demands
Active Synthetic Ensemble
5 Agents
Latency: 14ms (Local Model)
Counterfactual Interventions Live Tuning
$49
Strategic AI Pivot

Decouple AI copilot into consumption-based credit add-on with zero-data-retention certification.

Multi-Agent Opinion Cluster & Polarities Active Convergence
Supportive
Neutral/Skeptical
Churn Risk
Interactive Graph: Drag nodes to test latent tension. Click node to inspect agent memory buffer.
Agent Deliberation & Inspector DevOps Lead Dave
DevOps Lead Dave High Churn Risk
Archetype: Power User | Price Sens: 0.35 | Skepticism: 0.82
"Rejects bundled AI as vendor lock-in; demands unbundled API tier"
Synthetic Focus Group Feed

Understanding the Synthetic Deliberation Model

How does this persona simulator calculate consensus and churn hazard from pricing, bundling, and privacy policies?

This interactive lab evaluates five synthetic buyer archetypes using a deterministic multi-attribute utility formula rather than a live large language model. For each persona, the script calculates a net sentiment score between -1 and +1 by subtracting price penalties and skepticism from a base constant (0.30) and adding discrete policy adjustments. Consensus represents the average sentiment rescaled to a 0–100 index, while churn hazard measures the proportion of personas experiencing negative sentiment weighted by individual sensitivity.

The application runs entirely client-side on fixed deterministic heuristics rather than an active neural network or real market telemetry. The labels 'DEEPSEEK AGENT ENGINE' and 'Latency: 14ms (Local Model)' are decorative fixtures. In addition, the churn rate includes a hardcoded override of 38% specifically for the default initial setting, and the synthetic network links are visual force-directed graph artifacts that do not simulate peer-to-peer social influence.

Try a worked example

Keep the baseline price at $49 and change AI Feature Bundling Policy from 'Mandatory AI Addon ($15/mo)' to 'Consumption-Based Credits (Opt-in)'. In response, each persona receives an additive utility boost (+0.35) instead of penalties, lifting Dave's sentiment out of negative territory, shifting Consensus Score from 37/100 to 69/100, dropping Churn Hazard Rate from 38% to 0%, and updating Primary Friction Point to 'Telemetry Opt-in Uncertainty'.

Individual Persona Utility Equation

The script calculates persona sentiment as rawSent = 0.3 - pricePenalty + bundlingMod + privacyMod - (skepticism * 0.3), clamped between -1 and +1. The price penalty scales with (basePrice - 39) * 0.02 * price_sensitivity, meaning higher base prices penalize price-sensitive personas like Freelance Creator Chloe (sensitivity 0.90) far more severely than Enterprise Director Elena (sensitivity 0.20).

Bundling and privacy options apply categorical adjustments. For example, 'mandatory_15' applies penalties of -0.4 for Power Users and -0.5 for Budget Sensitive personas, whereas 'optional_credits' adds +0.35 across all archetypes. Strict zero retention adds +0.7 for governance archetypes but only +0.2 for others, while model training defaults heavily penalize governance personas with -0.8.

Consensus Scoring and Churn Calibration

The Consensus Score averages the sentiment scores across all five agents and rescales the resulting mean from the range [-1, 1] onto a [0, 100] scale: Math.round(((avgSent + 1) / 2) * 100).

Whenever an agent's sentiment drops below -0.3, the simulation accumulates an agent churn penalty equal to (price_sensitivity * 0.6 + skepticism * 0.4). While non-default states divide this accumulated penalty across all five agents, the default SaaS tier state with mandatory bundling, $49 price, and standard privacy explicitly overrides the churn hazard rate calculation to a fixed 0.38 (38%).

Synthetic persona weights and a fixed recommendation discrepancy

Read the explanation

The page evaluates five assigned personas with baseline point three minus price penalty, plus bundling and privacy modifiers, minus skepticism times point three. At forty nine dollars, mandatory bundling and standard privacy, sentiments are minus point four one six, minus point five one five, minus point one six five, minus point zero five five and minus point one six. Their mean minus point two six two two maps to thirty seven consensus points. This is synthetic arithmetic, not a survey or language model deliberation. Only sentiments below minus point three add a weighted churn contribution. The first two default personas contribute point five three eight and point seven two. Dividing their sum by five gives twenty five point one six percent, but the exact default combination overrides that value to thirty eight percent. Changing assumptions exits this fixed branch. Neither number estimates observed customer churn, and the weights are supplied constants rather than fitted consumer behavior. At the same price, optional credits and zero retention yield sentiments point five three four, point five three five, one after clipping, point six nine five and point five nine. Their mean maps to eighty four consensus points. A separate recommendation branch nevertheless inserts a fixed ninety two percent synthetic approval sentence. The marketing narrative field does not enter this utility equation. Exported persona reactions and graphs are locally generated from these rules, not interviews, backend agent runs, certified privacy terms or validated rollout recommendations.

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