About Us

Unplanned maintenance requests are sporadic and can be difficult to determine when they are likely to occur and to allocate resources. This often leads to underutilised resources. Using our machine learning algorithms on historical and real-time data sets we are able to predict when events are likely to occur and effectively schedule the most effective (based on qualifications/ locations etc) resources for these jobs.

InformedActions works with teams separated across multiple locations, servicing a large breath of areas. Predicting when jobs are likely to occur and scheduling resources to complete these jobs, bearing in mind various constraints including; engineer qualifications, location, vehicle type, available tools etc.

Companies include ambulance services, tool hire services, home maintenance, facilities management.

 

What makes us different?
We take the historic and real time insight of your organisation and arm your staff with the knowledge and tools so ensure they can become supremely efficient and productive.

 

How do we do it?
At the heart of our service is our Algorithmic Engine which analyses your real time and historic data to find the most efficient and productive means of carrying out individual tasks. Whether you’re in sales or service our approach ensures your people are supplied with the right knowledge & tools, they ask the right questions or advise to ensure your on-going success.

Seth Nabarro
Seth is a machine learning engineer, who works with our advisers, designing and tailoring the algorithms to the needs of our Customers. Seth studied at  Imperial College of London and UCL where he obtained his degrees  in Physics and Computational and  Machine Learning respectively

Tristan Fletcher
Tristan has experience in applying state-of-the-art prediction methods from Machine Learning in a diverse variety of fields. He is a graduate of the Universities of Cambridge, Sussex and College of London and maintains a fellowship with theses institutions. 

Harry Jones
Harry’s focus is on using state of the art machine learning technology, such as spatio-temporal predictive modelling and dynamic optimisation algorithms for use in mobile asset optimisation. Harry graduated from the University of Westminster and York, where he studied computer science and electronic engineering

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Pip Witheridge

A former Archaeologist, Tank Commander and Deloitte Consultant, Pip has a varied skill set to help oversee the fast growth and strategic direction of Grove.

 

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Marcus Dixon

Marcus’s focus is helping customers make the most of data, whether its Systems Data, IoT Data or Third Party Data, helping understand what is possible. How to reduce costs, improve output  and improve customer satisfaction with advanced analytics.  

 

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Paul Sweet

Worked in cloud computing for 10 years in various roles across the globe, applying his experience of systems architecture, application development and integration to scope, to design and build the platform