This is the recording of the Oracle Startup Idol, which is originally published at https://videohub.oracle.com/media/0_4e9ncjzn. Complete Intelligence won the Best Overall Pitch during the event. Thank you to every startup that participated in this fun event!
Complete Intelligence is a cloud containerized platform for forecasting costs and revenues for better decisions. The real problem that we’re helping people with is the overwhelming amount of data they have. There are two key issues that we’re solving. One is forecast accuracy. Error is a real issue with forecasting of costs and revenues. The other is context. It’s very difficult for people to get the right context for their forecast. Can they forecast that specific component for that specific product line that they need? And can they do it in an accurate way?
We’ve spent 2 and a half years focusing on costs. And what you see here is CI forecasts compared to consensus forecasts for all of 2019. This is looking at energy forecasts. You can see that the consensus errors in the far right are double-digit error rates. CI’s errors are in the far right, and we beat consensus forecast 88% of the time. In many cases, we’re significantly better than consensus forecasts.
Once we solve the forecasting problem, the other is the context problem. We have a product called CostFlow and RevenueFlow, where we take in data from ERP systems and e-procurement systems and process on our platform for high-context, highly accurate forecasts. What you’re seeing is the bill of material for electronic control valved. We have a hierarchical visualization from the business unit, down to the product category, down to the element/component level, where a CFO, etc. can manage the pipeline for procurement. This solves CFO pain points.
The results that we see, this is a client of ours who has a 2 billion dollars in revenue, helping them save 32 million dollars on their cost line, which ultimately adds up to 22 million dollars of free cash flow and 441 million to their valuation.
This may seem like very specific forecasting problem, but ultimately it leads to a better valuation for these manufacturing firms.