Risk management should be seen as one of the most critical steps of a construction project, in the actual building process. A strong business plan ensures streamlined operations, benefiting from your learnings from past projects and helping your team to make decisions avoiding risk, and improving the efficiency of your organization. It also allows your teams to gain confidence as they keep executing projects following a risk-aware approach, anticipating issues, and mitigating risks preventively. The earlier you detect a risk the cheaper is to manage potential consequences, avoid large negative impacts on your budget. This leads to increased income over time. A simple but solid methodology ensures your ROI.
Large construction corporations use many different digital tools to control projects and workflows. Documentation management, project management, or contract management tools are just three usual examples. Many of these tools become obsolete and do not allow leveraging smart features including advanced analytics or AI. Besides, very often these tools lack interoperability, making it almost impossible to perform any type of transversal analytics.
Even when organizations try to come up with unique digital platforms that include all these tools and solve interoperability issues, large construction sites are usually run by consortia involving different construction companies, and tools may be imposed by the client or other partners in a consortium. Therefore all these issues still persist.
On top of this inefficient digital ecosystem, we execute many different workflows. Most of the workflows in large and complex projects are well documented. Sometimes this happens because you are following the guideline of standard contracts (e.g. FIDIC). Sometimes it happens because your organization has decided to digitize workflows to guarantee quality.
Many of these workflows are repetitive and keep repeating in similar ways time after time. Think about the workflow to generate, review and approve documentation, non-conformities management, change management, or contract management. When these workflows do not work well, projects may suffer from very significant budget overruns and delays.
How do you monitor these workflows though? Management tools allow your organization to execute many of these workflows, but you cannot control risks around these workflows in an easy way. In many cases, by the time you realize your project is going to be in trouble, it is too late.
Beawre offers a SaaS-based Continuous Risk Control for Workflows. Our innovative solution monitors each business process in real-time and recalculates risk severity automatically, using AI-based predictions. There may be thousands of concurrent workflows running in parallel. We monitor all of them and let your organization understand the actual operational status. Our solution makes it possible to automate manual processes automatically according to the workflow's evolution or mitigation action for a certain detected risk. We provide business intelligence and reports to make you risk-aware.
Different from other tools, Beawre solution controls risk on any workflow and allows organizations to avoid unnecessary costs and delays as well as to understand and optimize their workflows. Our solution does not only read project schedules or other data sources as other solutions do, but we understand the workflow and can predict the future execution based on past executions.
AI by itself may just be a buzzword. From clustering algorithms to the most sophisticated Deep Learning techniques, they all fall under the AI category. We adapt the AI algorithms to each customer's needs. For many of our customers, explainability is essential. If they make a decision based on the level of risk they need to understand how this was computed. Some other customers prioritize accuracy over explainability. In other contexts, the amount of data may be large, and more sophisticated AI techniques, like transformers, can be used to learn aspects of your ecosystem. In some other cases, less data is available at the beginning and you need to resort to statistical approaches first, and let our solution automatically switch to more sophisticated options when enough data is available to train better and more accurate models. We have decades of experience in this field, let us help you understand the best approach for you.
We have selected the most relevant and up-to-date technologies to implement our solution and to store and manage data. These technologies include Kubernetes, .NET Core 3.1, React JS, GraphQL, Rx, LinQ, SignalR, AzureSQL database, and ML.NET among others.