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supplier and over the last six months , I ’ ve noticed an increasing number of late deliveries , and performance issues . We can use that in conjunction with external data to provide greater visibility of risk exposure .
Contextualizing data
“ Of course , we don ’ t just deliver a onesize-fits-all solution , we also provide the human element : we are all about artificial and human intelligence . We contextualize data for each individual client . So , for example , client A and client B may both work with the same supplier , and therefore , will be exposed to the same risks . However , the impact of those risks may be very different for each client . Client A may have five other suppliers for the same item for example , whereas client B may not . Similarly , inventory level and scale of operations may not be the same . Therefore , the human element adds the layer of contextualization and insight . The promise we want to make to our clients is that if something happens , we will inform accordingly and help assess the ways in which it will affect their business specifically ,” Prerna confirms .
“ When I hear people talk about AI , while they enthuse about the volume and velocity of data available , they often complain about the level of complexity . Somebody working in procurement will not have the time to trawl through layers of data , which is where we come in . Our risk rating is dynamic . We curate and contextualize data , personalize it further , and then only pass on the
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