Complex problems. Real-world data. Finance, scientific discovery, retail, supply chain management, manufacturing processes, healthcare, infrastructure planning – these are just some of the areas where you may need to predict future performance or to work out how to optimise that performance against multiple targets. Almost always, you are dependent on real-world data that is sparse or noisy.
That’s where the unique Alchemite™ technology comes in. It can rapidly generate predictive models from sparse, noisy, high-dimensional datasets. You can instantly apply these models for prediction and optimisation, gaining vital insight and aiding decision-making. And Alchemite™ helps you to understand the uncertainty in these predictions so you can assess and manage risk and pick the solution to your problem that is most likely to succeed.
Recorded webinar: Impossible journeys in 15-dimensional space
You have a problem. You’re trying to get value from a dataset that has too many parameters. You don’t have much data. The data you have has holes in it. And you’re not sure what you’re looking for. What do you do? Join us to find out how Alchemite™ can help you plot your route to success.
What's your application?
Alchemite™ works with any numerical or categorical dataset. Maybe you are trying to eliminate fraud or guide investments based on data about financial transactions? Or to work out what equipment to install or fix next using sensor data from in-service devices? Perhaps you have patient data that could inform vital healthcare decisions? Or customer data that could guide your retail strategy? Whatever your application, get in touch and we’ll be happy to show you Alchemite™ and discuss how it might meet your needs.
Software integration partnerships
The Alchemite™ algorithms can also be embedded in third-party software, providing powerful prediction or optimisation capabilities. If you have an application with users who need to get more value from sparse or noisy data, get in touch.
Alchemite™for predictive modelling
- Gap-fill and validate sparse, noisy data
- Auto-generate and refine models that identify relationships in the data
- Quantify uncertainty to support rational business cases and understand risk
- Identify priorities for additional data acquisition
- Identify likely outcomes, even where training data is very limited
- Design inputs to optimise multiple target outputs