The technology used by BellHawk Systems for most of its projects is typically based on the use of the BellHawk real-time rules-based data collection engine, the MilramX rules-based automated data exchange system, the Activation Framework real-time expert system, as well as techniques such as multi-level neural networks for deep reasoning, adaptive model-based reasoning and planning, clustering, and, where rapid decison making is required, the use of decision trees.
Because real-time AI systems have to make rapid decisions they tend to rely on multiple intercommunicating intelligent agents running on computers where the data resides. These may be complemented by IIOT (industrial Internet of Things) devices to capture and process critical real-time data.
Where data intensive processing, such as model based reasoning and planning, has to be performed in real-time then highly parallel "graphics" processors with Ai capabilities may be used.
Real-time AI systems process data in real-time, as it is created, to come up with immediately actionable information for people or to control automation equipment or processes.
Note that this is very different from most convetional AI projects where banks of high performance servers are used to process masses of historical data to try to glean some useful data.
An important part of this is to present inerfaces that provide immediately actionable information for people. This may be in the form of text-message alerts sent to a cell phone or a flashing red light on a dashboard.
A real-time AI system digests large amounts of data from many sources in real-time and then presents the result in such as way as to provide actionable information in a format that people, with their relatively slow cognitive abilities can quickly digest and act on.
At the same time, these systems need to be self-aware that they are dealing with limited "specific" information and that human beings have much broader "general" knowledge which may countermand the actions suggested by the AI system.