Data ≠ Information
While many believe the proliferation of data will improve decision making for businesses and individuals alike, data itself is just a nascent raw material for the modern era. Oil didn’t help us cross oceans until engines were built.
Thasos is an artificial intelligence engine for extracting actionable information from the real-time locations of mobile phones everywhere, based on the largest repository of high-quality location data after Google and Apple.
Excel- and Tableau-friendly: Convert billions of locations across years of history into organized information for key metrics, including customer visits, hours worked, trucks on the road, and many more.
Pre-computed features: Customize your information feeds by selecting features across three core dimensions:
Predictive analytics: Our web-based interface grants you access to dozens of analytical tools common in finance and data science.
In order to make mobile phone location data useful, we must define the world in terms of latitude and longitude coordinates. A geofence is a set of such coordinates that, when connected by lines, forms the boundary of a building or other location of interest.
The geofencing operation adds thousands of verified locations to our database daily. Use our pre-mapped locations for companies across the globe or geofence your own to measure human activity anywhere.
Every Thasos geofence includes a proprietary quality rating and loads of meta data that enables selection of locations based on features such as when they initially opened for business, the average daily visits per square foot, and nearby businesses within a user-defined radius.
Benchmarks: Compare information feeds against known metrics reported by thousands of companies. We’ve built an extensive database of “ground truth” so you can quickly assess the accuracy of the measurements you make before relying on them.
Ground truth: The concept of ground truth embodies known or trusted observations reported by third-parties of customer visits, hotel occupants, airline passengers, hospital inpatients, football game attendees, city populations, and many other key performance indicators at specific times and places. By comparing observations derived from mobile phone location data with those known to be true, we can assess the accuracy and expected error of measurements made with our platform.
Scales with AI: Feeding ground truth to our AI models enables them to learn from their mistakes and better detect, understand, and correct for upstream data anomalies and systematic changes at scale.