Wallscope’s platform is a versatile data processing engine that uses AI to link and contextualise data, making it easier to search, navigate and report large amounts of information. It includes an AI Toolkit which can be used to build bespoke solutions and interfaces, coupled with a powerful Visualisation Studio. Discover new insights by integrating textual, numerical and other data sources, presenting them in a way that suits you and your business needs.
Is your data ready for AI?
Everyone knows any good solution starts with really understanding the problem and identifying the opportunities. At Wallscope, we start there.
Our team will work with you to understand your organisational needs, challenges and ambitions.
All organisations have an underlying wealth of knowledge, but using this effectively is not always easy. Some forms of data are harder than others to access and process, so often this valuable information is left untapped. Meanwhile, volumes of data continue to increase in multiple formats and across disparate locations.
Wallscope will help you prepare your data and your organisation for AI, empowering you to get new value from data that was previously hidden or unusable.
We use a range of technologies which are based on Linked Data principles and Semantic Web techniques. By linking and contextualising your data, we make it easier to navigate large amounts of information.
Our technology works across your current systems and file types, so there’s no need to replace or replicate data - maximising efficiency and improving your workflow.
Knowledge Mapping is a crucial stage in your digital transformation journey. During this three-week process we will meet with relevant staff and stakeholders to explore your organisation’s ambitions and goals. By examining your data infrastructure, information governance and security, we’ll be able to suggest how Machine Learning models can add value to your business.
This exploration phase will cover the following:
- Analysis of business challenges and key objectives
- Analysis of data sources, types, formats, and infrastructure assessment
- Description of Machine Learning models and tailored use cases
- Implementation plan, including requirements and timelines
- Initial design and interface ideas and wireframes