Human-context systems architecture
team@mentalaize.ai ↗To understand one’s own and another person’s behavior in terms of underlying thoughts, feelings, intentions, and beliefs—without mistaking inference for fact.
Adapted from the psychodynamic concept of mentalizing developed by Peter Fonagy and Anthony Bateman; the final clause reflects MentalAize’s emphasis on calibrated inference. Source ↗
A psychodynamically and neuroscience-informed architecture for building high-resolution yet calibrated models of human context. It supports mentalization in both directions—helping AI interpret the human without treating inference as fact, while helping the human understand and contest the AI’s assumptions.
Context · change · agency
By modeling context as inference—not fact—and keeping uncertainty visible.
Bidirectional mentalization
MentalAize treats understanding as a reciprocal process—not a model silently constructing an unchallengeable story about its user.
Model context, perspective, and change over time while preserving uncertainty and competing explanations.
Let people see, question, and correct the system’s assumptions, confidence, and reasoning boundaries.
Architecture at a glance
Structure signals across interactions and over time.
Model perspectives and possible inner states without collapsing inference into fact.
Preserve uncertainty, observability, and competing explanations.
Let context shape the response while leaving judgment and agency with the person.
A clear boundary
MentalAize is research infrastructure, not therapy, diagnosis, or a substitute for professional or human judgment.
