Learning algorithm development is one of our core competencies. When paired with our artificially intelligent agents and big data these algorithms can achieve almost magical results.
A few commonly deployed approaches we’ve leveraged in the past include:
- Genetic Algorithms
- Artificial Neural Networks
- Supervised/Unsupervised Learning Machines
Our AI agents, working in concert with our supporting learning machines and data streams, complete a system triad that’s able to autonomously maximize its environment to achieve operational goals. From creating efficient asset management strategies to executing them our AI agents achieve domain autonomy through deduction, learning and problem solving.
In finance, our AI agents operational activities include information classification and activity execution. Unlike systems activity managed by people our AI agents are able to detect and respond to situational changes quicker and with greater accuracy in a verifiable and measurable manner.
Research and Development
The hallmarks of every system implementation entail some form of research and development. We’ve developed highly efficient processes that enable us to deliver highly specialized cross-domain AI software. These processes are brought together by means of our academic, professional and industry partners we’ve built lasting relationships with overtime.
Some high-level characteristics of our processes consist of:
- Research and Theory Development
- Theory Testing and Refinement
- Systems Development
- Life-Cycle Management
Integrated Business Systems
Our AI agents aren’t much use to anyone if they can’t be integrated with other systems and processes. Traditionally, integration involves providing and consuming API hooks with accounting, trading platforms and other similar systems. Architecturally, these bilateral hooks observe vertical, horizontal and star integration design patterns.