Building simpler infrastructure for AI

Rachel is a strategic technology leader who helps organizations turn complex AI ideas into real, working systems. She has led large, cross-functional teams and research efforts, guiding enterprise-scale AI projects from strategy through execution. Known for bridging technical depth with business clarity, she brings teams together to deliver solutions that create measurable impact.

Austin Cook is an AI engineer and researcher focused on building practical tools that help teams use advanced AI more effectively. He has led and contributed to widely used open source datasets and systems that are now relied on by leading research and industry groups, including work cited by NVIDIA. His work blends hands-on engineering with a focus on making powerful AI more accessible and easier to work with.

Garrett is a philosopher and researcher focused on understanding consciousness, intelligence, and the role of information in minds and machines. His work explores how insights from philosophy, neuroscience, and AI can clarify what it means for systems, human or artificial, to think and know. He brings a deep theoretical perspective to real-world questions about technology, ethics, and the future of intelligent systems.

Peter steers the direction of future technology while improving the company’s core products. His background spans machine learning, hyperdimensional computing, and representation learning, with doctoral research focused on hyperdimensional approaches to AGI. He has authored numerous research papers and designed key parts of Servamind’s early systems, including the initial versions of Serva Encoder and the AI Wrapper.

As Head of Product Engineering, Victor focuses on leading the design and delivery of efficient, scalable software that connects innovation with practical execution. With experience in embedded systems, algorithm optimization, and cross-functional development, he works to align engineering decisions with product and business goals. His background in hardware-level optimization and system performance helps create AI products that balance computational efficiency with usability, translating HDC research into scalable, real-world applications.