Welcome
Khalomot
AI-Powered Geoscience
Intelligence Platform
“Mining the dreams hidden in data”
Khalomot —
Geoscience Intelligence
Powered by Unsupervised AI
Khalomot is an enterprise-grade AI platform that transforms geoscience analysis through unsupervised learning, unbiased discovery, attentioned understanding, and explainable intelligence. Unlike conventional supervised AI that replicates human decisions, Khalomot discovers patterns and relationships invisible to traditional methods – revealing what wasn’t previously recognized and complementing existing advanced geoscience tools.
The Khalomot Philosophy:
Four Pillars
Khalomot is built on four scientific pillars that enable unbiased discovery and transparent geological intelligence. Each pillar ensures that insights are data-driven, explainable, and grounded in measurable reasoning.
Unsupersived Learning
We don’t tell the AI what to find – we let the data reveal its structure.
- Shemesh: Converts images to vectors without labeled training (image2vec)
- Shahar: Discovers geochemical patterns through clustering, SOM, and PCA
- Kokhav: Explores multiple prediction algorithms without assuming the “best” approach
UNBIASED Discovery
No human-labeled training biases. The algorithms explore without preconceptions.
- Data reveals its own structure and relationships
- Patterns emerge from geological reality, not training labels
- Discoveries go beyond what we “taught” the system to see
ATTENTIONED Understanding
Focus on what matters. Built-in attention mechanisms show where the AI focuses and why certain features drive results.
- Feature importance transparency across all modules
- Mathematical reasoning for every decision
- Why this cluster size?” and “Why this pattern?” explanations
EXPLAINABLE Intelligence
Every result comes with clear, geological explanations.
- Statistical reasoning visible and verifiable
- Mathematical foundations for all conclusions
- Haiku interpretation layer (Claude Haiku LLM by Anthropic) translates technical outputs into geological language
- Built for geologists who need to understand AND trust
The Khalomot:
Three Core Modules
Khalomot is structured around three integrated modules that work together to analyze, interpret, and explain geoscience data. Each module addresses a distinct layer of discovery while remaining fully connected.
Shemesh converts complex geological images into vector representations to discover patterns without labeled training. It reveals mineralogical relationships that conventional supervised tools cannot detect.
- Image-to-vector pattern discovery (image2vec)
- Unsupervised, label-free learning
- Multi-channel mineralogical analysis
- Explainable visual features
Sharar discovers hidden geochemical patterns using unsupervised clustering and self-organizing maps. It reveals geological domains and relationships without relying on predefined classifications or labels.
- Unsupervised clustering, SOM, and PCA
- Label-free pattern discovery
- Mathematical transparency and stability scoring
- Geological domain and alteration insights
Kokhav delivers predictive modeling with full mathematical transparency by training multiple algorithms in parallel. It enables stakeholders to understand not only what the model predicts, but why.
- Multi-algorithm regression (7+ models)
- Full explainability and diagnostics
- SHAP-based feature attribution
- Quality-gated, enterprise-ready predictions