Intellegens sparse data AI engine encaspulates our unique deep learning algorithm, capable of training models from data which can be as little as 0.05% complete.
Trained models can be used to make new predictions, identify errors and maximize a set of desired parameters (design mode)

Use cases


Proven applications with the following type of problems.


  • estimation of values previously only accessible by expensive, empirical, experimentation
  • ability to estimate the endpoints in complex, multistage, multi-ingredient processes
  • qualification of estimates by robust and meaningful quality metrics indicative of uncertainty
  • ability to identify and correct outlier data and to suggest empirical experiments that will improve overall uncertainty of the model
  • computationally efficient and scalable from small matrices to big data
  • large amounts of incomplete anonymised, numerical data
  • numerical data combined with models or graph functions


Implementation and delivery

Alchemite™, can be used standalone to generate models or easily be integrated into existing software stacks and workflows.


Alchemite enables the configuration of a number of hyper-parameters to specify, for example, number of hidden layers, time to train or level of accuracy recquired.


The tool is easily configured through a simple parameter file and data is managed through simple data files


A management console can be delivered as a fully managed, on demand solution or deployed internally on a per user or enterprise license basis.


The interface allows for non-technical users to easily train and deploy networks, using local or cloud based compute resources to manage jobs. Trained models can then be used through a number of easy to use interfaces or shared and tested via public facing API's.


Please get in touch to see how we can help

See how our unique AI can solve real world problems