Our Vision
At Omnibio we are committed to developing better, greener, intelligence, directly from nature
Core Principles
Energy Efficiency
We build energy-efficient, continuously learning neural systems to control cooperative robots in real-world sectors such as warehousing, logistics, mining, and shipping.
Adaptability
Our networks learn continuously. Whether responding to new environments or changes in robot configuration, they adapt in real time and no retraining cycles or manual intervention needed.
Resilience
Like natural organisms, our systems are fault-tolerant and decentralised. If one agent fails, others adapt. If the environment shifts, the system evolves with it.
Our Long-Term Goal
Closed-Loop Robotic Ecosystems
We are building systems that not only act but sense, learn, predict, and adapt across time.
Our vision is to create fully autonomous robotic collectives that:
-
Maintain their own internal state using energy from the environment.
-
Coordinate and cooperate using shared predictive models.
-
Scale across industries without exponential energy costs or cloud dependency.
Why Bio Hybrid Intelligent Systems?
Biological systems are the most energy-efficient and adaptive processors known.
-
A human brain runs on ~20W and outperforms any supercomputer in real-world inference.
-
Biological neurons learn from sparse, continuous interaction—not batch updates.
-
Self-organisation in biology provides robustness without centralised control.
By combining wetware (living neural networks) and neuromorphic software, we tap into the architecture of intelligence itself. Not by copying biology—but by learning from its principles.
Our Impact
Climate
We reduce reliance on energy-intensive GPU training, enabling low-power intelligence at the edge, suitable for remote, off-grid deployment.
Logistics & Warehousing
Our systems enable adaptive swarm control, reducing downtime, improving throughput, and responding autonomously to operational anomalies.
Autonomy
We bring real-time adaptation and true autonomy to robotics, moving beyond static pathing and reactive rules to systems that think and evolve.