Psyemic
Psyemic applies clinical psychology methods to the safety, performance, and wellbeing of human–AI systems. The company helps organizations design, test, and deploy AI that interacts directly with users, examining behavioral dynamics such as over-reliance, influence, and vulnerability through realistic testing. Its approach is particularly relevant for high-stakes environments where human judgment and trust significantly affect outcomes.
- Artificial Intelligence
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
As AI systems become more capable and embedded in decision-making, their safety and performance depend on the interaction between technical design and human behavior. Systems that perform well in testing can still create risks in practice when users over-trust them, defer judgment, or rely on them in unintended ways—especially under pressure.
Solution
Psyemic brings a clinical psychology perspective to AI safety and deployment, studying how people understand, trust, and challenge AI systems in real-world contexts. The company helps organizations design, test, and deploy human–AI systems that remain safe, effective, and reliable by examining behavioral dynamics such as over-reliance, influence, and vulnerability through realistic testing. This approach is particularly relevant for systems that interact directly with users, where these dynamics shape outcomes over time. By applying a discipline built around understanding behavior, judgment, and risk in complex, high-stakes environments, Psyemic addresses both technical and human factors in AI deployment.
Target Audience
Primary customers are organizations deploying AI systems that interact directly with users, particularly those in high-stakes or safety-critical environments where human judgment and trust significantly affect outcomes.
Features
- Application of clinical psychology methods to assess behavior, judgment, and risk in high-stakes human–AI environments
- Realistic testing of human–AI interactions to identify risks that emerge from user trust, deference, and unintended reliance
- Focus on behavioral dynamics such as over-reliance, influence, and vulnerability in user-facing AI systems
- Support for designing, testing, and deploying AI systems with attention to both technical and human factors
- Emphasis on safety, performance, and wellbeing of human–AI systems in real-world use