ABOUT US
Our Mission
To develop neural-native AI systems that: Learn from real-time brain signals Visualize inner mental states Enhance and amplify human cognition Redefine what it means for a machine to be intelligent
Our Vision
A future where AI is no longer disconnected from the mind, but emerges with it. Where thought is visualized, emotion is understood, and cognitive energy is amplified. In this future, AI does not replace us — it evolves with us.
Our Mission
To develop neural-native AI systems that: Learn from real-time brain signals Visualize inner mental states Enhance and amplify human cognition Redefine what it means for a machine to be intelligent
Our Vision
A future where AI is no longer disconnected from the mind, but emerges with it. Where thought is visualized, emotion is understood, and cognitive energy is amplified. In this future, AI does not replace us — it evolves with us.
We are a cognitive architecture lab.
We design new kinds of intelligence — not by simulating the brain, but by fusing with its signals. At the intersection of neuroscience and artificial intelligence, we are engineering architectures that grow, reflect, feel, and evolve. We work with neural data like EEG and fNIRS. We build architectures like Kolmogorov-Arnold Networks, spiking systems, and gradient-free models. We imagine minds — and then prototype them.
What Do We Believe?
Artificial Intelligence today is built to see and predict. We believe tomorrow’s AI must be built to think, dream, feel, and become. Perception is not cognition. Neural signals are not noise — they are code. Biological intelligence is dynamic, self-structuring, and energetic. AI should be too. We are not trying to commercialize consciousness. We are trying to understand it — and then co-create with it.
Remote Connectivity
Break free from traditional boundaries. Work from anywhere, at the comfort of your own space.
The Journey
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The Beginning
Neuraif Lab
With the goal of creating a new kind of AI.
First Breakthrough
Biological Intelligence
Developed a prototype that learns from neural signals.
First Publication
KANets
Published our first paper on Kolmogorov-Arnold Networks.
Next
Graph Neural Networks
Exploring the potential of GNNs in cognitive architectures.