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Orwel reads how a visitor behaves in a session (clicks, scroll, hovers, form inputs, pace) and turns it into intent signals: behavior patterns such as comparing options, ready to act or lost. They are statistical patterns of on-page behavior, not a psychological profile of the person, and they exist to trigger a useful action: show proof to someone who is verifying claims, offer help to someone who is lost, shorten the path for someone ready to act.
Signals are computed from on-site behavior only. They never use sensitive categories, and they are exposed to you through the app, the API and MCP; nothing is shown to the visitor unless you build it.

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Signal to action

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The 12 signals

Every session is scored against 12 behavior patterns, grouped below. A visitor usually shows a mix, with one or two dominant signals. The API names them personalities for backwards compatibility.

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Action Architects

Users who are goal-oriented and structured.

Decisive

Quick to act, focused on results.

Impulsive

Acts on immediate urges, high energy.

Methodical

Systematic, detail-oriented.

Focused

Highly attentive to specific tasks.

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Information Explorers

Users who seek knowledge and understanding.

Curious

Seeks new information, explores widely.

Skeptical

Cautious, verifies claims.

Lost

Disoriented or unsure.

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Social Connectors

Users driven by interaction and community.

Social

Seeks connection with others.

Reactive

Responds to external stimuli.

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Emotional States

Transient states reflecting current mood.

Anxious

Worried or hesitant.

Playful

Fun-seeking, non-linear.

Engaged

Deeply involved and committed.

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Calculation Logic

The engine uses a Bayesian Inference model combined with Rate-Based Feature Extraction.
  1. Event Collection: The SDK captures granular events (clicks, scrolls, hovers, form inputs).
  2. Feature Engineering: Events are normalized into rates (e.g., clicks_per_minute, interaction_ratio) to account for session duration.
  3. Probabilistic Scoring: Each trait has a Gaussian probability model. The engine calculates the likelihood of the observed behavior matching each trait’s profile.
  4. Confidence Score: A confidence score (0-1) is assigned to each trait. Traits with high confidence (>0.6) are considered active.

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Personality Combos (Synergies)

When a visitor exhibits two strong traits simultaneously, they form a Personality Combo. These combinations provide deeper insight into complex behaviors.

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Complementary Synergies

Positive combinations that enhance user capability.
  • Executive Architect (Decisive + Methodical): Deep analysis followed by decisive action. The ideal B2B buyer.
  • Community Catalyst (Social + Playful): Drives engagement and viral growth.
  • Critical Researcher (Curious + Skeptical): Explores widely but evaluates rigorously. Hard to win over, but loyal once convinced.
  • Goal Achiever (Decisive + Engaged): Quick decision-making with long-term commitment.
  • Specialist Commitment (Focused + Engaged): Intense concentration with sustained interest.

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Conflict Patterns

Opposing traits that indicate friction or uncertainty.
  • Tension Pattern (Impulsive + Methodical): Urgency vs. Deliberation. The user wants to act but feels the need to check details, leading to erratic behavior.
  • Stress Pattern (Anxious + Decisive): Caution vs. Action. Often results in “approach-avoidance” cycles (starting a form, stopping, starting again).
  • Navigation Conflict (Lost + Focused): Disorientation vs. Concentration. The user is trying hard to find something but failing.

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Using Personalities

You can retrieve the inferred personalities for a visitor using the API:
Use these insights to personalize the experience:
  • For Decisive users: Show “Buy Now” buttons and quick checkout.
  • For Skeptical users: Highlight social proof, guarantees, and detailed specs.
  • For Lost users: Trigger a proactive support chat or simplified navigation.