What We Can Do For You
Inatas' technology provides the ability to produce powerful stochastic models or 'meta-models' of uncertain systems. These can be constructed from data stored in relational databases - or any ODBC source - or as text files. This process can be aided (or replaced) by expert knowledge. These models are not only extremely accurate but also visually representable and intuitively understandable - increasing the confindence of the user, as well as permitting their use as 'aid-of-thinking' tools and intuitive checks on their results. We can train and support your company to utilize our technologies in-house, or undertake project-based or ongoing data analysis/system modeling tasks. Our technology is designed for medium to very large datasets.
Once created, these models are used to produce:
- Decision policies regarding how variables under your control should be set given knowledge that will be available at the time the decision is to be made.
- Predictions regarding the state of variables in the system given available knowledge.
Cases can be drawn from datasets or entered in an ad-hoc fashion. Results can be viewed in-program and exported to relational databases (or any ODBC source) or as a LaTex pdf script. It is also possible to implement automated decision making systems using Inatas technology as the 'brain' - such as in automated trading or real-time system optimization tasks.
Our competitive advantage is our world leading technology. However, while we pride ourselves on providing the most advanced technology to our clients, we also wish to ensure that your use of this technology is easier, quicker and cheaper than any alternatives. We understand that begining the use of new advanced data analysis tools can be intimidating and will work with you to train and support your use of our products and services. We seek to forge ongoing relationships with our clients where we continually work with them to ensure that they obtain the full benefits of our technology. We hope that you will be one of them.