A patented graph-based AI architecture that runs in 27 microseconds on a $4 microcontroller — and gets smarter over time without retraining from scratch.
GenesisNode replaces gradient-based neural networks with genetic graph evolution and sparse BFS routing. No matrix multiplication. No GPU dependency. No catastrophic forgetting. Built for the industrial edge.
// the problem
Industrial sensors, ESP32 microcontrollers, and embedded devices have kilobytes of RAM — not gigabytes. Transformer models and traditional neural networks simply don’t fit.
Factory floors, remote pipelines, and mobile assets operate with intermittent connectivity. Cloud-dependent inference fails exactly when you need it most.
When traditional networks learn new behaviors, they overwrite what they already know. Retraining from scratch is expensive, slow, and operationally disruptive.
// the architecture
GenesisNode represents knowledge as a sparse weighted graph of token nodes connected by context-tagged edges. New capabilities are added through per-level scaler nodes with namespace-isolated routing — meaning prior knowledge is structurally protected, not softly regularized.
Each new capability level gets its own proxy nodes, structurally separate from prior knowledge. Training a new task cannot modify what the network already knows. Protection is architectural, not probabilistic.
Edges carry typed context labels. Each capability level’s routing function only traverses edges belonging to its namespace. Multiple independent capability layers coexist on one graph without interference.
Inference is a breadth-first search through the node graph — pure dictionary lookups, no matrix multiplication. This is why inference runs in 27 microseconds on desktop CPU with no GPU required.
A quality gate monitors foundational capability retention during every new training phase. If retention drops below threshold, the checkpoint is rejected. L1 retention maintained at 98.7% across 10 sequential training phases.
Performance figures measured on training-set recall benchmarks and desktop CPU. Held-out generalization benchmark: 6.4% exact match, 12.9% top-5 on unseen common sense triples. Physical ESP32 deployment in progress.
// see it run
Real-time anomaly detection on simulated industrial sensor streams. 99%+ bandwidth reduction vs. continuous cloud upload. Projected battery life improvement of 100x on 3000mAh cell. Business case calculator included.
Vehicle simulation with 6-sensor obstacle avoidance and target seeking. Four incremental capability levels trained sequentially. 70 microsecond inference. Graceful degradation under simulated network packet loss.
Ten sequential cognitive capability levels from word association through reading comprehension. L1 retention maintained at 98.7% after all 10 training phases. D3.js force-directed graph visualization of the live node structure.
Full demo video coming soon. Request early access below.
// where it runs
Detect bearing failure, vibration anomalies, and power quality events at the sensor — before they propagate to the cloud. GenesisNode trains on your equipment’s specific signal patterns and runs inference locally, even during network outages. Target hardware: ESP32 + MEMS accelerometer. Estimated deployment cost: under $10 per sensor node.
Vehicles, drones, and robotic arms operating in GPS-denied or communication-degraded environments need edge intelligence that doesn’t freeze when the network drops. GenesisNode’s reactive fallback architecture continues operating under packet loss and trains fallback behavior back into the learned network.
Production line conditions change. GenesisNode’s incremental training architecture adds new defect recognition patterns without retraining from scratch or degrading recognition of previously learned defect types. Capability grows with your process knowledge.
// the moment
Every major TinyML approach today starts with a neural network and shrinks it via quantization. GenesisNode starts small by design — a genetic graph that grows only the nodes it needs, routes through only the connections relevant to each query, and never touches prior knowledge when learning something new.
This is not a compressed transformer. It is a different computational primitive.
We are in early commercial development, actively seeking manufacturing partners for paid pilot deployments and research collaborators for the non-provisional patent and arXiv submission.
// get involved
If you operate manufacturing equipment, industrial sensors, or autonomous systems and want to see GenesisNode running on your hardware, we want to talk. Pilots are structured as a fixed-scope engagement: instrument one asset, train a model on your signal data, deploy to existing hardware.
For research and partnership inquiries: research@genesisnode.ai
For press and investment: brian@genesisnode.ai