Our tools

Armed with domain experts to bridge the gap between machine learning and geoscience, GoldSpot’s front-to-back solution extracts training data for the eventual use of machine learning. GoldSpot solves problems presented by big data and unutilized layers of geological data. This significantly decreases risk, while increasing the efficiency and success rate of mineral exploration. GoldSpot leverages this technology by: working with premier mining companies to vector ore; staking its own claims to option or joint-venture; and by developing its own trading algorithm to buy and sell mining stocks.


Our pillars


GoldSpot Discoveries will clean and standardise all your data – paper and digital – returned in updated and queryable formats. Your data is examined by industry veterans and academics for prospective targets, applying knowledge to your deposit geology, while utilizing the latest exploration techniques and field experience. As industry leaders at the forefront of applying machine learning to mineral exploration, GoldSpot will apply proprietary technology to examine the data for targets which may not have been identified through conventional exploration techniques. These targets are vetted by GoldSpot’s experts as valid before proposing them to clients.


GoldSpot Exploration’s mandate is to use robust datasets and machine learning to generate prospectivity maps. In one case, GoldSpot was able to find 86% of the existing gold deposits in the Quebec Abitibi, but only needed 4% of the total surface area to do so. In real life application, this significantly reduces exploration time and costs. GoldSpot plans on running a prospect generation model, where staked claims will be optioned off in exchange for shares and royalties.


GoldSpot Investments will revolutionize investing, and will have a significant edge compared to larger investment boutiques and hedge funds that have interest in resources. GoldSpot's advantage centres around utilizing geologic-specific data as one of its data sources. Using a quantamental approach,  this geological data is combined with market, company & other quantitative data that drive investment decisions. Machine learning assists in determining which companies are a buy, and which are a sell.

Investor relations