The psychological measurement layer for AI systems

Receptiviti converts language into 200+ validated psychological variables — cognitive load, emotional tone, risk orientation — giving AI systems a real-time read on who they're talking to, not just what was said. Engineered for production integration into safety, alignment, and personalization systems.

POST /V2/ANALYZE/WRITTEN → RESPONSE · 58MS

{
"cognitive_load": "high",
"analytical_thinking": 34.2,
"emotional_anxiety": 71.8,
"risk_orientation": "elevated",
"authenticity": 58.3,
"stress_indicators": {
"acute_stress": 68.1,
"cognitive_strain": 74.5
}
}

What's missing from current AI architectures?

Modern AI systems handle psychological state implicitly — through LLM inference influenced by prompts, shallow sentiment signals, or heuristics embedded in system logic. The problem isn't the language model. It's the absence of a measurement layer alongside it.

Problem

Implicit inference

Psychological state influences system behavior without ever becoming an observable, structured, controllable variable.

Prompt sensitivity

LLM-based inference is sensitive to prompt phrasing and shifts unpredictably across model versions.

Solution

Explicit measurement

Structured, auditable psychological variables — explicitly computed, consistent across runs, independent of model behavior.

Five integration points. One API call.

Input conditioning

Score user language before it enters the model. Use measured cognitive load, emotional state, and communication style to condition system behavior based on what is actually present — not what is assumed.

Output evaluation

Apply measurement to model-generated language. Assess whether outputs are calibrated to the user's measured state. Detect drift in emotional register or cognitive alignment over time.

Session-level evaluation

Maintain an explicit representation of psychological state across a session — as a structured variable, not implicitly held in context. Patterns invisible at the single-turn level become visible at the trajectory level.

User safety

Detect shifts in interaction state that signal distress or overload — grounded in measurement rather than heuristic triggers. A one-off signal handled differently from a pattern accumulating across time.

Evaluation & red-teaming

Assess whether model outputs move psychological signals in intended directions. Identify cases where responses inadvertently increase stress, anxiety, or cognitive load. Condition results on user state, not just task type.

Why measurement outperforms inference

LLMs generate language. Receptiviti measures it.

A well-prompted model can reflect sensitivity to emotional state or generate labels for it. But approximation and measurement are not the same thing.

Generative inference is sensitive to prompt phrasing, shifts unpredictably across model versions, and embeds interpretations in text rather than returning structured variables.

Receptiviti measurement

• Deterministic measurement:
  Regardless of prompt design, model version, or temperature. The variable you store today can be compared to the one you compute six months from now.
• Numerical variables in a defined schema:
  Not interpretations embedded in generated text. Usable directly in system logic, evaluation pipelines, and analytics.
• Auditable, reproducible, governable:
  Explicit measurement creates an audit trail. Implicit inference influences model behavior with zero logging or transparency. Making state explicit is the precondition for governance and systematic improvement.
• External validation:
  Measures are grounded in a relationship between linguistic patterns and psychological constructs that has been independently verified in peer-reviewed research.

The distinction

Cognitive and psychological measurement, not sentiment analysis.

Traditional sentiment analysis classifies language as positive, negative, or neutral. Receptiviti converts language into structured psychological variables — cognitive load, analytical thinking, emotional anxiety, risk orientation, urgency — that remain consistent across runs, comparable over time, and usable directly in evaluation pipelines and system logic.

The science

Grounded in behavioral science, not model weights.

Receptiviti's measurements come from validated linguistic frameworks. The relationship between linguistic patterns and psychological constructs has been independently verified. This is not a model predicting meaning, but a measurement of something empirically shown to exist.

Deployment

Two integration paths.

• API
  Submit language at any point in your pipeline. Receive structured psychological variables in response. Supports real-time and batch processing.
• Containerized
  For privacy or data residency requirements. Runs entirely within your infrastructure — text is not stored or transmitted externally. HIPAA-ready.

Make psychological state measurable in your AI system.

Psychological state is already influencing the behavior of your AI system. The question is whether that influence is something you can see, measure, and act on — or something that remains opaque and difficult to systematically improve.