About XyloIQ

XyloIQ is an AI behavior research lab developing approaches to runtime governance for trained neural networks. Our work focuses on a specific question: How does behavior emerge as a response is being formed - and how can that process be governed in real time?

‍The Technical Position
‍Most approaches to AI control operate before inference (training, alignment, fine-tuning) or after it (filtering, guardrails, retraining). XyloIQ is focused on inference itself - the moment a response is being formed.

‍The Work: KIM
‍KIM is the architecture developed at XyloIQ to address this problem. It operates during inference, informed by underlying model behavior, and is designed to be model-agnostic across pre-trained models. Initial controlled experiments have been conducted on production AI systems, evaluating how behavior changes when governed during response generation, with results that are consistent and measurable.

‍A Broader Direction
‍The broader objective is not only to increase intelligence, but to ensure that intelligence behaves reliably in practice. This requires moving beyond static training and post-hoc constraints, toward a deeper understanding of how behavior emerges under real-world conditions. KIM is one component of a broader architecture under development at XyloIQ.

‍Founder
XyloIQ was founded by Justin Ginsberg in 2025. Before founding XyloIQ, Inc., Ginsberg spent over 25 years building and operating a marketing company, working closely with organizations across a wide range of industries. Through that work, he developed a practical perspective on how technology is adopted in the real world: capability matters - but trust determines whether something is actually used. His work at XyloIQ focuses on how AI systems behave as they generate responses, and how that behavior can be governed at inference to improve reliability and consistency in practice.

This work reflects one part of a broader architecture under development at XyloIQ and is supported by US and international patents pending related to AI system behavior and real-time governance.