Research at Enn
We believe useful AI should be private, affordable and genuinely productive. Our research explores how to bring capable AI onto people's own devices, and how to shape it into workspaces that make real work better, not just faster.
AI on local systems
How to run capable models privately on everyday hardware, for individuals and businesses, so sensitive work never has to leave the device. Model selection by system specs, quantization, memory and latency, and the economics of local vs cloud.
AI workspaces for industry
How purpose-built workbenches foster logical, focused productivity for employees across industries, keeping context, reducing tool-switching, and turning AI from a novelty into dependable day-to-day leverage.
AI in education
How adaptive, Socratic tutoring and mastery-based practice actually improve learning outcomes, grounded in a student's own material, with progress you can measure and study you can do together.
AI for kids
How to design safe, age-appropriate, private-by-default AI that supports curiosity and learning for children, with the guardrails, transparency and parental control that trust requires.
More areas are opening up as the platform grows, from accessibility to on-device agents. We'll share findings here.
Build the private-AI future with us
Try the platform our research is shaping, or reach out if you'd like to collaborate.