We apply artificial intelligence to the discovery of critical minerals. The clean energy future depends on finding copper, lithium, nickel, and cobalt at scales never before achieved. We're building the intelligence system to make that possible.
"Through advanced artificial intelligence and comprehensive geological data synthesis, we accelerate the discovery of critical minerals essential for humanity's clean energy future."
The world faces an unprecedented mineral supply challenge. The technologies of the clean energy transition require vast quantities of specific metals.
An electric vehicle contains 2-4 times more copper than a conventional vehicle. A single offshore wind turbine needs 8 tonnes of copper, 2 tonnes of rare earth elements, and 3 tonnes of zinc. Grid-scale battery storage, charging infrastructure, and upgraded electrical systems all demand minerals at scales the current supply chain cannot provide.
Current projections indicate that by 2035, copper supply will fall short of demand by over 10 million tonnes annually. Lithium demand is expected to increase 40-fold by 2040. Without dramatic acceleration in mineral discovery, the energy transition stalls.
Discovery rates have collapsed just as demand accelerates.
For a century, mineral exploration followed a consistent pattern: geologists walked the surface, identified outcropping mineralization, and drilled to confirm subsurface extent. This methodology discovered the deposits that supply today's mining operations.
But the surface has been searched. The easy-to-find deposits have been found. What remains lies deeper, beneath sedimentary cover, in more remote locations. These deposits require sophisticated methods to identify, and traditional exploration is struggling.
The numbers tell the story. In the 1990s, major exploration programs achieved discovery rates of approximately 1-in-10. Today, that rate has fallen below 1-in-100. Exploration budgets have increased while discoveries have declined. The average time from discovery to production now exceeds 16 years.
The mining industry is spending more and finding less. Without a fundamental improvement in exploration methodology, the critical minerals required for decarbonization will not be available when they are needed.
Aserela addresses the exploration challenge through the systematic application of artificial intelligence to geological discovery.
Decades of geological exploration have generated vast archives of drilling records, survey data, and research reports. Much of this data has never been digitized, integrated, or analyzed with modern computational methods. We extract and synthesize this historical information, transforming dormant data into discovery intelligence. Deposits dismissed decades ago may warrant re-evaluation with AI methods.
Mineral deposits exhibit characteristic geological signatures across geophysical, geochemical, and structural data. These patterns are often too subtle or complex for human analysis to detect. Our machine learning models, trained on verified mineralization examples, identify these signatures with accuracy that exceeds traditional interpretation. They see what human experts cannot.
No single data source provides definitive evidence of mineralization. But the combination of geological, geophysical, geochemical, structural, and remote sensing data creates a multi-dimensional fingerprint that dramatically improves target confidence. Our platform integrates over 50 data source types into unified predictive models. The whole is far greater than the sum of parts.
We've assembled the world's largest integrated repository of exploration-relevant geological data.
Our platform ingests data from over 50 distinct source types: satellite imagery updated daily, geophysical surveys spanning continents, historical drilling records we've digitized from paper archives, geochemical sampling results, academic research, and government geological surveys from 195 countries.
Machine learning models trained on verified deposits analyze this integrated data to identify mineralization signatures. They generate probability surfaces quantifying the likelihood of economic mineralization at each location. Exploration teams access these insights through interactive mapping tools, target ranking systems, and portfolio analytics dashboards.
The platform functions as a living map of the Earth's mineral potential, continuously updated as new data becomes available, enabling exploration at unprecedented scale and efficiency.
In 2024, our methodology achieved definitive validation.
Our algorithms identified a massive high-grade copper-cobalt deposit in the Democratic Republic of Congo. The deposit lies beneath terrain that had been explored and dismissed by multiple previous campaigns using traditional methods.
Where human geologists saw nothing compelling, our models detected a convergence of subtle anomalies across geophysical, structural, and alteration data that indicated high mineralization probability. Subsequent drilling confirmed a world-class deposit with grades significantly above regional averages.
This discovery represents one of the largest copper finds in three decades. It validates our core thesis: that AI-driven analysis of integrated geological data can identify deposits invisible to traditional exploration. The techniques that found this deposit can be applied systematically across the globe.
Aserela is headquartered in Sherman Oaks, California. Our distributed team works across time zones to provide global coverage.
Aserela LLC is a privately held Delaware company. We partner with exploration companies, mining operators, and government geological surveys.
We focus exclusively on critical minerals for the energy transition: copper, lithium, nickel, cobalt, rare earth elements, and related commodities.
We collaborate with organizations committed to accelerating critical mineral supply. Whether you're an exploration company, mining operator, government survey, or investor, we'd welcome a conversation about how we might work together.