top of page

Articial Precognition

Superior predictive accuracy and explainability for better enterprise decision making

Drive business value by seeing the future

Delivering Exceptional Value to our Customers

Our Artificial Precognition technology has brought value to our customers across many different industries by delivering exceptional predictive accuracy. For example, in healthcare predicting Alzheimer's Disease onset correctly 91% of the time in non-symptomatic patients, predicting car insurance claims 98% of the time or reducing traffic delays by 30 minutes in central London. It’s this sort of accuracy that’s given brands like BAE Systems, Lockheed Martin and the NHS the confidence to use our technology.

Avoiding the Pitfalls of Traditional Data Science

In machine learning the more accurate your model gets, the more likely you will fall into the pitfalls of overfitting. Overfitting is when the model's accuracy on the training set is very high but shows many errors on the rest of the data. State-of-the-art machine learning is prone to overfitting with lower prediction and estimation accuracy. The rules linking data to information cannot be easily generalised, especially for the kind of highly nuanced information which is becoming more and more common in our highly digitised world.  Solving this problem with large volumes of data is often very challenging, mainly due to constraints in data availability and limitations in understanding the root cause of overfitting. 


We avoid this trade-off with Artificial Precognition: achieving the accuracy of an overfit model with a general machine learning technology that can be applied in any industry, whether that's a training set or the test set. We achieve this by combining rule-based techniques with neural networks (the best of both worlds), thus achieving unprecedented accuracy without overfitting.

Highly Accurate Algorithms for any Dataset or Industry

Artificial Precognition is a two-step process, using proprietary algorithms to coarse tune large, complicated data sets to more accurately categorise and choose features for further analysis. This process allows us to surface the most predictive elements of a dataset and make highly accurate predictions without relying on traditional data science methodologies. These proprietary algorithms are innately explainable supporting accountable and risk adverse enterprise decision making. What's more, this technology can be applied to any dataset in any industry and is available as a module in Oscar Enterprise AI as a code-free function that anyone can run– not just data scientists. This technology is also the basis of our Aftos capability granting our control system a significant level of inferential thinking.


Blog: Predicting the onset of Alzheimer's with Oscar and Artificial Precognition

The creation of an extremely accurate prediction model isn’t the end of the enterprise machine learning workflow. Until the model actually drives real-world actions, no business value has been created. In this white paper learn how MLops  enables AI deployments and helps create this value. 

bottom of page