Mission
Gamakon builds geometry-native AI systems designed to operate in extremely high-dimensional spaces. Our goal is to provide a unified framework for search, navigation, optimisation, and reasoning, enabling intelligent systems to perform reliably and scalably across complex real-world environments.
What We Do
Gamakon's research and engineering efforts are organised into four complementary pillars. Each pillar addresses a fundamental capability that modern AI systems require — memory, movement, learning, and reasoning — all expressed within a unified geometric space.
UltraDim Search
AslanDB - Angular Similarity and Locality for Astronomical N Dimensions
- High-recall fast search across ultra-large vector spaces, up to a million dimensions
- Next-generation multimodal and agentic AI workloads
- GPU-native acceleration
- Enterprise-scale query performance
BlueWorld Navigation
A new "Radix-Voronoi" index delivers a range of exceptional new capabilities for working with hyperspherical vector data.
- Pre-computed geometric atlas over hypersphere surface
- Hyperspherical "geohash" for stable vector addressing
- Multi-resolution hierarchy
- Compression-aware indexing
- Native anomaly detection
Geometric Fitness Functions
GFF
- Optimisation across tens of thousands of objectives
- Holistic evaluation of complex systems
- Applications to RL and design optimisation
- Stable behaviour in high-dimensional trade-offs
Gamakon Reasoning Engine
Completely novel evolutionary neuro-symbolic program synthesis method. It treats Navigation as the mechanism for Reasoning. Waypoints explain how it "jumped to conclusions"
- Abstract reasoning through geometric representations
- Transferable strategies across problem sets
- Integration of symbolic and geometric approaches
- Pathfinding through ideas, not just data
Interactive Demos
Explore our geometric visualizations demonstrating key concepts in sphere sampling, blue noise generation, and hierarchical navigation.
Blue Noise on Spheres
Explore uniform sphere distributions with Thomson electrostatic repulsion, geodesic grids, and K-nearest neighbor analysis.
Hierarchical Navigation
Three-tier hierarchical structure demonstrating multi-level navigation patterns on hypersphere surfaces.
Interactive Pathfinding
Set markers and explore random walks through geometric graph structures with multi-level edge connectivity.
Why Geometry-Native AI Matters
Modern AI systems increasingly operate on vast vector spaces, spanning text, vision, robotics, simulation, and multimodal environments. Traditional tools struggle as these spaces grow in size, complexity, and dimensionality.
Geometry-native AI offers stability in extreme dimensional regimes, interpretability through structured geometric behaviour, and robustness across noise, uncertainty, and heterogeneous data. By building AI systems around hyperspherical and geometric principles, we unlock new capabilities that conventional methods cannot reliably reach.
Get Involved
Gamakon is building the foundations for geometry-native AI. We welcome collaboration with research groups, industrial partners, robotics and agent-oriented teams, and organisations seeking early access to our technology.