THE GAP
What's missing from current AI architectures?
Modern AI systems handle psychological state implicitly — through LLM inference influened 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.
PROBLEM
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.
WHERE IT INTEGRATES
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's outputs have properties that generative approaches cannot reliably provide:
LLM INFERENCE
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Sensitive to prompt phrasing and temperature
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Shifts unpredictably across model versions
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Interpretations embedded in generated text, not structured variables
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No audit trail. No reproducibility.
RECEPTIVITI MEASUREMENT
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Same input, same output — every time: 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.
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Numerical variables in a defined schema: Not interpretations embedded in generated text. Usable directly in system logic, evaluation pipelines, and analytics.
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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.
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External validation: Measures are grounded in a relationship between linguistic patterns and psychological constructs that has been independently verified in peer-reviewed research — not a model predicting what something means, but a measurement of something empirically shown to exist.
Implicit inference influences model behavior. Explicit measurement makes that behavior observable, testable, and controllable.
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 same science behind 34,000+ peer-reviewed publications in psychology, medicine, and behavioral science. The core finding is that patterns in language use are stable, largely unconscious indicators of psychological state, consistent across contexts and populations.
The relationship between linguistic patterns and psychological constructs has been independently verified, repeatedly. That is what distinguishes this from inference: it is not a model predicting what something means — it is a measurement of something that has been 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.
Further reading.
GET STARTED
Make psychological state observable 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.