An AI Model is a computational system trained to process data and perform tasks such as classification, prediction, or decision-making. These models are built using machine learning or deep learning techniques, where the system studies large amounts of historical data, learns patterns, and applies that knowledge to new situations.
For example, Hermes AI relies on models to detect market-moving news and flag events that could impact financial markets, while Orion AI uses models to analyze company fundamentals, track technical indicators, and interpret market sentiment. Each model is trained for a specific purpose, making it highly effective in its domain.
AI models can range from narrow models, which excel at a single task like detecting fraudulent transactions, to more general models that can adapt to a broader set of activities. In business, they’re already being applied in fraud detection, investment forecasting, recruitment screening, customer service routing, and dozens of other areas where speed and accuracy matter.
The performance of an AI model depends on several factors: the quality and size of its training data, the algorithms powering it, and how well it’s tuned and updated over time. Businesses often run multiple specialized models together, creating a more accurate and reliable decision-support framework that draws strength from different perspectives.
At its core, an AI model is what allows AI systems to “think” to process information, recognize patterns, and generate insights that help businesses move faster and make smarter choices.
An AI Model is a trained computational system that processes data to perform specific tasks such as classification, prediction, or decision-making. It’s built through machine learning or deep learning techniques, where the system learns patterns from historical datasets. For example, Hermes AI uses models to detect market-moving news, while Orion AI uses them to analyze financial fundamentals, technical indicators, and sentiment. AI models can be narrow (task-specific) or more general in capability. In business, they’re used for fraud detection, investment forecasting, recruitment screening, and customer service routing. The performance of an AI model depends on the quality and quantity of its training data, the algorithms used, and its ongoing tuning. Businesses often run multiple specialized AI models together for higher accuracy, creating a robust decision-support framework.
