A category-defining solution that represents a step change in capability
A data-first approach improves projects by using all of the data that is being collected to tell the story. Historical analysis collects and presents the data, live data helps with managing the situation in a given moment, while predictive analysis uses the data already collected to forecast how the situation will develop in the future.
Providing simple solutions in complex environments that allows the targeting of resources most effectively
The actionable insights created by this approach let customers take a proactive attitude to risk management, identifying potential problems before they occur and helping improve both regulatory compliance and reducing the chances of people ending up in an unsafe environment.
Quickly transform raw data into actionable insights
Data is only as good as the person interpreting it, and to make the best operational decisions, you need to have access to the most relevant data. Better data gives more insights which in turn leads to better decisions and being able to validate your risk assessment with stronger quantitative data is incredibly powerful.
This is where Ambilytics comes in.
Our software was not created to replace the human decision aspect, rather Ambilytics was created as a support tool that both combines the best of data visualisation and project management systems but then goes a step further, incorporating a suite of analytical tools that help project teams go beyond reacting in real-time to anticipating what is likely to happen.
This approach leads to a decreased risk of issues that will impact human health, reduced project costs, and accelerated project timelines.
BEPA, the newest tool set within Ambilytics was created to address the challenges within the built environment. It uses complex proprietary algorithms to interpret and understand the numerous interdependent factors that influence the built environment.
How It Works
Ambilytics is no code, predictive analytics software that gives you access to the power of data science. It combines information from your remotely deployed IoT connected devices with contextual data sources such as weather. All this data is cleaned, processed, combined, and presented in a way that is meaningful, and easy to understand and to act on.
The machine learning generated statistical models are constantly updated with new data, inter-relationships and trends are detected, and alerts sent when potential problems are identified.