Nevada Nonprofit · EIN 42-2841879
Stellar Nexus Institute
Advancing AI for national security, public sector adoption, and STEM education
Serving communities the market doesn't serve.
↓
Advancing AI for national security, public sector adoption, and STEM education
Serving communities the market doesn't serve.
Bridging the gap between AI capability and the communities that need it most
Advancing human-AI teaming for military survivability. Building operational tools that work reliably in high-stakes environments where failure is not an option. Our research contributes to the DEVCOM ARL Human-AI Teaming framework.
Making AI infrastructure accessible to small military staffs, school districts, rural governments, and organizations operating under personnel ceilings. When the market doesn't serve your community, we build the tools you need.
Building the next generation of AI-literate professionals through hands-on education, research opportunities, and open-source tools. Connecting classroom theory to operational reality through real-world AI deployment experience.
Stellar Nexus Institute is a Nevada nonprofit corporation founded in 2026 by Scott Ewell and Jeffery Raymond Ewell. The institute bridges a critical gap by providing AI capability and infrastructure to small military staffs, public-sector organizations, and educational programs that lack the resources to build their own. We serve communities that existing technology providers overlook.
We don't just build software — we produce research. Our platform serves as both operational capability and research infrastructure, generating generalizable findings for the Human-AI Teaming research community.
The platform is not just software — it is research infrastructure producing generalizable findings
Models that produce the best analysis are structurally the worst at reliable tool execution. This has direct implications for how AI systems are deployed in operational environments.
A database-driven approach that replaces ephemeral context with persistent state, creating reliable multi-agent coordination patterns for real-world work.
35 failure modes documented and synthesized into enforceable behavioral rules, creating a reliability engineering framework for multi-agent systems.
Positioned within DEVCOM ARL's Human-AI Teaming framework. Each pillar connects to HAT research outcomes, from trust calibration to operational reliability.