AI systems are becoming universal. Human Discovery builds the infrastructure layer that makes them safe, memory-aware, and accountable, across every surface, device, and agent.
Technology mediates nearly every human interaction, yet the entire digital ecosystem runs on systems that cannot understand emotional meaning, timing, compatibility, or safety.
Human Discovery builds the only full-stack emotional infrastructure: a complete layer that makes AI systems safe, memory-aware, and consent-respecting for any platform, device, or agent.
Real-time emotional cognition for every interaction. Plug-and-play APIs that bring emotional understanding to any product in minutes.
A persistent, user-owned emotional identity layer. Portable across apps, evolving over time, and encrypted by default.
The first graph-based model of emotional relationships. Maps compatibility, resonance, timing, and trajectory between people and agents.
The system-level emotional intelligence layer for devices, agents, robots, and entire ecosystems. Comparable to iOS or Android, but for emotion.
Human Discovery does not use predefined emotional labels. The system autonomously discovers emotional structure from interaction episodes, a scientific approach, not a retrained model.
Landmark studies confirm that emotion in AI is real, measurable, and functionally significant. The science validates the infrastructure opportunity.
Anthropic identified 171 distinct emotion vectors inside Claude that causally influence model behavior.
Frontier LLMs score 81% on emotional intelligence tests, vs. the 56% human average.
The Emotional AI market is projected to reach $51.25 billion by 2030, growing at 9.4% CAGR.
GPT-4 scored an EQ of 117 on the MSCEIT, exceeding approximately 89% of human test-takers.
38% of users use AI chatbots weekly for general emotional support; 22% use them daily.
Sentiment-adaptive AI reduces customer complaint escalations by up to 56%.
Statistics sourced from published research. The 171-emotion-vector finding is from Anthropic's April 2026 interpretability study on Claude Sonnet 4.5. Full citations available on request.
A small, senior team built to ship and scale infrastructure, spanning AI engineering, enterprise go-to-market, compliance, and capital.
Two decades building ventures across media, hospitality, and technology, including a television channel distributed in 22 countries to 100M+ viewers and 50+ US restaurant locations. Leads category vision, partnerships, and capital.
More than 20 years scaling go-to-market and distribution across North America. Built a 3,000-agency broker network and led 180+ benefits-technology integrations reaching 35M+ employees. Leads enterprise relationships and partner growth.
Former Enterprise Compliance Manager at Toyota Financial Services, bringing governance, audit-readiness, and regulatory discipline at scale. Owns operations, compliance, and delivery.
Machine learning engineer and AI founder who builds predictive AI, autonomous trading, and production platforms from the ground up. Previously co-founded and scaled an AI-driven hedge fund to live portfolio management. Owns the Emotional AI Engine and technology stack.
Builds the memory infrastructure and data pipelines behind the engine. Former NASA JPL intern on the Mars Sample Return Toolkit and Lawrence Berkeley National Laboratory researcher, with IEEE-published machine learning research. BS Computer Science, Cal State Fullerton.
We are working with a small number of enterprise partners on early integrations. If your platform is ready for emotionally-aware AI, we want to hear from you.