The "Worm" Approach to Edge AI
Inspired by the biological efficiency of C. elegans, our patented Worm technology compresses AI models by 90% while retaining 95% accuracy—enabling real-time AI on resource-constrained devices.
Nature's Blueprint for Efficient AI
Caenorhabditis elegans, a tiny nematode worm, can perform complex tasks like locomotion, chemotaxis (chemical sensing), and even basic learning—all with just 302 neurons.
In contrast, modern AI models often have billions of parameters, consuming massive computational resources. Our "Worm" approach asks: what if we could achieve similar biological efficiency in AI?
By mimicking nature's optimization strategies, we've developed a multi-stage distillation process that strips away redundant computational pathways while preserving the essential functionality—just like evolution optimized the C. elegans nervous system over millions of years.
Model Compression
Multi-Stage Distillation Process
Our patented three-step approach optimizes AI models for edge deployment without sacrificing accuracy.
Knowledge Distillation
A smaller 'student' model learns from a larger 'teacher' model's outputs, transferring learned patterns and retaining 95% of original accuracy.
Neural Pruning
Redundant neurons and connections are systematically removed, eliminating computational pathways that don't contribute to core functionality.
Quantization
Model weights are converted from 32-bit floating-point to 8-bit integers, dramatically shrinking memory usage without significant accuracy loss.
Key Capabilities
Everything you need for efficient edge AI deployment.
Biologically Inspired
Inspired by Caenorhabditis elegans (C. elegans), a nematode that performs complex tasks like locomotion and chemotaxis with just 302 neurons. Our approach mimics this biological efficiency.
90% Size Reduction
Transform large AI models (1 GB+) into lightweight submodels (50-100 MB) through our multi-stage distillation process while retaining 95% accuracy.
Sub-10ms Latency
Real-time inference on resource-constrained devices with latency under 10 milliseconds, enabling instant decision-making at the edge.
10x Power Efficiency
Reduce power consumption from 500 mW to just 50 mW, making AI viable for battery-powered wearables and IoT sensors.
Multi-Architecture Support
Compatible with ARM, x86, and RISC-V architectures. Deploy on Raspberry Pi, Jetson Nano, Arduino, and ROS-compatible robots.
Edge-First, Cloud-Sync
Local autonomy with periodic cloud synchronization. Devices operate independently and sync when connectivity is available.
Technical Specifications
Supported Devices
Real-World Applications
See how the Worm approach is transforming industries.
Healthcare Wearables
Submodels on smartwatches detect vital sign anomalies (tachycardia, oxygen drops) in under 10ms with 99.5% accuracy.
- 50% faster emergency response
- $2M annual hospital savings
- HIPAA-compliant logging
Manufacturing Sensors
Edge AI on vibration sensors predicts equipment failures up to 30 days in advance, enabling proactive maintenance.
- 40% downtime reduction
- $500K annual savings
- Real-time quality control
Drone Navigation
Submodels process GPS and camera data in real-time for autonomous path planning and obstacle avoidance.
- 20% efficiency improvement
- Autonomous operation
- Blockchain-secured logs
How We Compare
| Feature | Google Edge TPU | AWS Greengrass | Tesan Worm |
|---|---|---|---|
| Biological Inspiration | No | No | Yes (C. elegans) |
| Size Reduction | Moderate | Moderate | 90% |
| Latency | Low | Medium | <10ms |
| Cloud Dependency | Partial | High | Edge-First |
| Cost | High Initial | Subscription | Low (Open-Source Compatible) |
Ready to Deploy AI at the Edge?
Join the companies using Worm technology to bring AI to resource-constrained devices.