We analyze each requirement on a case-by-case basis, since it will depend on the man-hours to allocate and this is directly related to the quality and order of the data that will be provided to us. We meet 2-3 times with the client (ideally with both the HSE and IT area) to show later them a technical proposal that we discuss openly so that in a few days we deliver a valued proposal.

We work using an Agile methodology, with which after the development of the second Sprint we are already able to show substantial changes in the processing of the associated HSE data. All this we are achieving after 6-8 weeks. Obviously, the deadlines will depend on the deliverables defined in the design stage of the solution based on the particular requirements of the client.

Yes, since our team of analytics experts have the knowledge and ability to work in multiple environments. However, we must indicate that when it is up to us to develop it, we choose Azure because we are ISV (Independent Software Vendor) partner of MicroSoft, however we could work on any other platform.

With our incident virtual assistant we have managed to reduce hundreds of hours of incidents investigations, eliminating the investigator’s focus view, objectifying the collection and analysis of data, interlocking prevention tools, such as controls, defenses and procedures related to it. All this allows to reduce considerable (and high cost) man-hours in this process. In this way we make accessible a world class prevention tool that today only reserved for TOP10 mining and industry companies, to small and medium-sized companies, contributing to the reduction of accidents.

Yes, each project ideally loads (first «sanitizes») the data obtained from the client, but in case this is not sufficient and/or of quality, it is complemented with the thousands of incidents and investigations that we manage in our own database. of data.

A direct way will be to see the decrease in the severity and frequency of incidents associated with the same activity. In other words, based on a history of «n» months, the change in trend of a certain behavior or deviation can be observed. Another way will be the optimization of the HSE supervision resource, which will be focused 100% on the real activities that statistically and by advanced analytics are indicated as «precursors» of accidents.