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.
A serial entrepreneur of two decades across media, hospitality, and technology, including a television channel reaching 100M+ viewers in 22 countries. He leads category vision, partnerships, and capital.
Two decades scaling go-to-market across North America, with a 3,000-agency broker network and 180+ integrations reaching 35M+ employees. As CEO, she drives enterprise growth and partnerships.
Former Enterprise Compliance Manager at Toyota Financial Services, bringing enterprise-grade governance and audit-readiness to a company built on trust. He owns operations, compliance, and delivery.
A machine learning engineer and AI founder with an MBA, who previously co-founded an AI-driven hedge fund and quant firm. As CTO, he architects and owns the Emotional AI Engine and technology stack.
A former NASA JPL and Lawrence Berkeley Lab researcher with IEEE-published work, she builds the memory infrastructure and data pipelines behind the engine while pursuing her master's at UC Berkeley.
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.