Validated psychological measurement for AI — built on thirty years of psycholinguistic science.
Receptiviti provides 200+ validated dimensions of human-AI interaction state, derived from language, independent of the model being assessed, and grounded in a research lineage with 34,000+ peer-reviewed citations.
WHAT RECEPTIVITI MEASURES
34,000+
Independent peer-reviewed citations to the psycholinguistic science behind these dimensions
200+
Psychological dimensions - cognitive, affective, social, stylistic
<65ms
API response in production
Explicit, observable measurement — not model inference.
Receptiviti's scores are derived from validated psycholinguistic frameworks — explicitly computed from language, consistent across runs, and independent of any model's behavior. The same input yields the same score.
200+ dimensions — cognitive load, emotional tone, distress signals, rapport, analytical thinking, authenticity — each traceable to published science. Observable, auditable, citable.
Derived from language, not LLM inference. Measurement that holds up to external review - because the basis of every score is observable.
THE SCIENCE
A measurement framework built before generative AI.
In the early 1990s, Dr. Pennebaker began asking whether the words people use can predict mental and physical health, personality, social behavior, and cognitive state. The answer was yes.
That work became LIWC — the Linguistic Inquiry and Word Count framework — refined over five major versions and validated across 34,000+ peer-reviewed citations.
Receptiviti holds the exclusive commercial rights to LIWC. That same framework is now applied to the question AI teams are actively working on: how AI affects the people using it.
1990s
RESEARCH LINEAGE
Decades of independent validation. A framework not contingent on the current LLM moment.
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Health & clinical psychology
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Personality & social behavior
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Organizational & forensic contexts
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Deception & cognitive load
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AI interaction & wellbeing research
WHO WE ARE
Receptiviti Labs
Receptiviti was founded in 2015 to apply psycholinguistic science across industries. Receptiviti Labs is the AI-focused arm of Receptiviti, dedicated to building the psychological measurement layer for the human side of AI.
Dr. James W. Pennebaker
Co-founder and Chief Science Officer
Created LIWC and the psycholinguistic research tradition at the core of Receptiviti's measurement science. As co-founder, he remains central to its scientific direction. Professor Emeritus, University of Texas at Austin. Elected to the National Academy of Sciences, 2025.
Dr. Molly Ireland
Head of Social Psychology
Social-personality psychologist who completed her PhD at UT Austin under Pennebaker. Tenured Associate Professor prior to joining Receptiviti. Published peer-reviewed research on language, behavior, and AI — most recently on social desirability bias in large language models.
Kent English
VP Engineering
Leads engineering and API infrastructure. Responsible for the production measurement platform — REST API, on-prem containerized deployment, and the reliability that enterprise and foundation model clients require.
Jonathan Kreindler
Co-founder and President
Co-founded Receptiviti with Dr. Pennebaker to build production infrastructure around validated measurement science. Now focused on AI — the largest and least measured deployment of human interaction in history. Leads strategy, AI partnerships, and corporate development.
Kiki Adams
Head of Linguistics
Computational linguist and founding team member. Studied linguistics and psychology at UT Austin and worked directly on LIWC development under James W. Pennebaker. Leads research & development work focusing on how Receptiviti's science can be applied within AI systems.
Jennifer Glista
Chief Revenue Officer
Former Managing Director and Head of Sales, Equity Derivative Solutions at Scotiabank. Fourteen years in institutional sales. Leads revenue at Receptiviti.
Mike Durland
Chief Executive Officer
Former Group Head and CEO, Global Banking and Markets at Scotiabank. PhD in Finance and Operations Research from Queen's University. Distinguished Fellow and Professor at the Munk School of Global Affairs & Public Policy, University of Toronto.
Dr. Ryan Boyd
Academic Partner · University of Texas at Dallas
Assistant Professor of Psychology at UT Dallas. PhD from UT Austin under Pennebaker. His research uses computational methods to study how everyday language reflects psychology - from mental health and personality to interpersonal dynamics and AI. Author of 100+ scholarly papers and co-editor of the Handbook of Language Analysis in Psychology. His work has been cited by the U.S. National Security Commission on Artificial Intelligence.