The Next Platform Shift

The Great Unbundling
of AI

We are moving intelligence from the centralized cloud to the edge.
Privacy. Instant Latency. Total Autonomy.

The Competitive Imperative of Edge AI

The cloud-centric era of AI is hitting a wall. Massive energy costs, privacy risks, and latency bottlenecks are creating an unassailable strategic advantage for the company that claims the edge.

Data Sovereignty & Privacy

Data never leaves the device. Essential for **HIPAA-compliant healthcare** and enterprise secrets. Eliminates the risk of centralized data breaches and surveillance capitalism.

Zero-Latency Inference

The speed of light is a hard limit. Local inference removes network round-trips, enabling millisecond response times critical for robotics, drones, and real-time biometric alerts.

Economic Efficiency

Shifting from CAPEX-heavy cloud GPUs to distributed consumer hardware (NPUs). No per-token API fees, no massive cooling bills, and functional operation in offline environments.

The "Edge First" Strategy

By 2026, 60% of consumer devices will be AI-capable. Our software stack prioritizes local NPU execution for 95% of tasks, creating a "privacy-walled" ecosystem impossible for cloud-native competitors to replicate.

2026
Projected Tipping Point
Under the Hood

The Architecture of Efficiency

BitNet b1.58

1.58-BIT TERNARY WEIGHTS

Traditional LLMs use 16-bit floating point numbers (FP16). We utilize a radical new architecture where every parameter is constrained to just three values: {-1, 0, 1}.

Multiplication Ops Replaced by Addition
Memory Footprint 3.55x Smaller
Energy Consumption 71.4x More Efficient

Unlocking Local Intelligence

This isn't just compression; it's a paradigm shift. We are decoupling intelligence from computational cost, making powerful AI viable on battery-powered devices.

1

Native 1-Bit Training

Unlike post-training quantization which degrades quality, BitNet is trained from scratch in low precision, maintaining high-fidelity reasoning.

2

Hardware Synergy

Our models are optimized for the latest consumer silicon—Apple Neural Engine, Qualcomm Hexagon, and NVIDIA Orin—unlocking "server-grade" AI on battery power.

Product I: Medical AI

AuraHealth

Agentic Longevity Orchestration

The AI Health market is projected to reach $188 billion by 2030. Yet, current leaders like Apple and Whoop are reactive trackers. AuraHealth is a proactive "Agentic" AI that doesn't just watch—it acts.

Powered by on-device Edge AI, AuraHealth bridges the gap between your biometrics, your calendar, and your physical environment.

The Biological Calendar

Aura negotiates your schedule with other AIs. It blocks high-focus work for your "Circadian Peaks" and pushes stressful meetings to times of high physiological resilience.

Environment & Energy Loop

Syncs with Matter/HomeKit. If your cortisol spikes, the lights dim. It even pre-cools your room for deep sleep using decentralized energy credits at the optimal price/time.

Privacy-First Edge AI

Uses local LLMs to process sensitive health data. HIPAA-compliant by design because your data never leaves your device.

AuraHealth Agent
Daily Orchestration
Digital Twin Projection (2031)
-2.4 Yrs Bio-Age
Projected Cardiovascular Health
Top 5%
Recent Autonomous Actions
Rescheduled 'Q3 Review'
Recovery score low (34%). Moved to Tuesday 10 AM.
Pre-cooled Master Bedroom
Synced with REM cycle prediction. Energy cost: $0.04.
5+
Daily Orchestrations
99.9%
Privacy (On-Device)

Target Launch: Q4 2026 • Beta: High-Performance Professionals

Product II: Physical Autonomy

AeroScan AI

"Agentic visual reasoning for the unstructured world."

Modern logistics is crippled by connectivity dead zones. AeroScan deploys autonomous drones with local reasoning capabilities. They don't just see; they understand—identifying unlabelled parts without Wi-Fi or GPS.

Perch & Scan

Software logic that lands drones to scan high shelves, saving battery.

Label-Free Rec

Identifies items by geometry and texture, not just barcodes.

Swarm Sync

Drones collaborate locally to resolve ambiguous items.

4-Bit VLA

Vision-Language-Action models running on-chip.

SYSTEM ONLINE NPU LOAD: 84%
98.5%
Accuracy on
Unlabelled Parts
+40%
Area Covered
Per Charge
Processing: Part #XYZ-99 1.87ms Latency

Leadership

Pioneering the next platform shift in artificial intelligence.

ZH

Zan Huang

Principal Investigator

Leading the architectural pivot towards high-fidelity, edge-native models. Focused on data curation and efficient training methods to combat linguistic collapse in AI.

EM

Evan McMullen

Principal Investigator

Spearheading the strategy for decentralized intelligence and the shift from cloud-dependency to user-owned cognitive tools.

RM

Ryan Mondalek

COO

Chief Operating Officer.

Collaborators: Gabriel Price, Rohan Raju