Since 2008 we have been obsessed with finding the best AI and data insights tools on the planet. Below are a few of our favorite things (but not all). You can see them in action via OSINT drip insights briefing, which uses our machine intelligence, OSINT, and alternative data ecosystem to extract market and geo-political insights.
Machine Intelligence combines multiple machine learning techniques such as natural language processing, semi-supervised, and unsupervised learning to various data sets to augment and amplify human analysis capacities. Ultimately a world-class machine intelligence ecosystem enables one person to do better and faster work than teams of data scientists, strategists, or analysts using traditional approaches. As a result, decisions and strategies are more precise and original.
Open Source Intelligence + Alternative Data is the collection and analysis of publicly available information to support decision-making. This information can come from various sources, including social media, news websites, government reports, and other public documents. OSINT is typically used by organizations such as intelligence agencies, law enforcement, and businesses to gather information on a particular topic or individual. With the right machine intelligence tools, it’s possible to understand any domain or topic to a high level in hours, mitigating the need for expensive expertise for all but the most mission-critical situations. Alternative data refers to data sets not typically used in financial analysis, such as satellite imagery, social media posts, and other unconventional sources of information. This data can provide unique insights into market trends and consumer behavior and is often used by hedge funds and other financial firms to gain a competitive advantage. The key difference between the two is OSINT focuses on publicly available information, while alternative data often include proprietary or hard-to-access data sets. OSINT is typically used for intelligence gathering and decision-making. In contrast, alternative data is often used for investment and trading purposes, but they can be applied liberally in most contexts.
Network Analysis can be used to identify patterns and trends within the data, and to understand how different entities within the network are connected and interact with one another. One of the main advantages of network analysis is its ability to capture complex relationships and interactions between entities. Traditional statistical methods often rely on linear models, which are limited in their ability to capture complex relationships. In contrast, network analysis allows for the analysis of non-linear relationships, which can be more reflective of real-world situations.
Topological Data Analysis (TDA) TDA is based on the idea that data can be represented as a network of interconnected points and that the relationships between these points can be described using topological concepts such as connectedness, and continuity. It is particularly useful for analyzing data that is non-linear or that has a high-dimensional structure, as it is able to capture subtle relationships and patterns in the data that may be overlooked by traditional methods. Ultimately this allows us to identify patterns and relationships in data that may not be immediately apparent using traditional statistical methods.