XENOPSYCHOLOGY LABS · APPLIED PRACTICE
We help organizations evaluate artificial systems as behavioral entities—how they reason, communicate, fail, respond to pressure, and interact with people in the environments where they will actually operate.
Discuss an engagement →01 · THE PRACTICE
Benchmarks can tell you whether a system completes a task. They often tell you much less about how the system behaves when context is ambiguous, users become emotional, instructions conflict, memory accumulates, tools fail, or incentives shift. Xenopsychology focuses on that behavioral layer.
02 · SERVICES
A structured profile of how an AI system behaves across reasoning, uncertainty, communication, instruction conflict, role stability, memory, and failure conditions.
Behavioral map · test matrix · risk patterns · recommendationsEvaluate how an AI behaves at boundaries: ambiguity, conflicting instructions, emotional pressure, authority, escalation, refusal, persuasion, and uncertainty.
Scenario suite · boundary analysis · deployment findingsForensic behavioral analysis after a model or agent behaves unexpectedly. Reconstruct the interaction ecology, identify contributing conditions, and distinguish isolated output from recurring behavioral pattern.
Incident reconstruction · contributing factors · recurrence testsDesign AI systems around trust, dependence, authority, attachment, disclosure, tone, and role clarity—not simply interface usability.
Relationship model · interaction principles · behavioral requirementsPrivate briefings for boards, founders, product leaders, and institutions that need a clear model of what current AI systems are, how their behavior differs from conventional software, and where governance questions actually arise.
Briefing · Q&A · decision framework · follow-up memoHelp teams define precise terms for new AI behaviors, agent roles, evaluation categories, policies, and internal frameworks so engineering, legal, product, and leadership are speaking the same language.
Lexicon · ontology · definitions · naming framework03 · WHAT WE LOOK FOR
04 · ENGAGEMENT METHOD
The method adapts to the system and question, but the principle remains the same: define what we are testing, vary the conditions, compare the results, document uncertainty, and translate findings into decisions.
Collect representative interactions, architecture context, intended role, constraints, and failure examples.
Design targeted tests that vary prompts, memory, tools, context, authority, and environmental conditions.
Separate one-off output from stable tendencies across repetitions, variants, models, and observers.
Connect behavior to architecture and interaction ecology without overstating hidden internal states.
Turn findings into language usable by engineering, leadership, policy, product, and end users.
Recommend concrete changes to prompts, roles, escalation, interfaces, tests, documentation, or governance.
05 · WHO THIS IS FOR
Our work is most useful when an AI system interacts with people, makes consequential recommendations, uses tools, maintains memory, acts with partial autonomy, or occupies a social role users may trust.
START WITH THE SYSTEM YOU HAVE
Bring us a model, agent, interaction problem, unexplained incident, or deployment decision. We will help turn it into something observable and testable.
Start a conversation →