<aside> 💡 Before getting into it, we should probably contextualize what Evara AI is all about.
As you will soon realize, Evara AI’s software enables you to carry out your modeling and uncertainty/risk management in a profoundly different, flexible, and more powerful way. To see how, you should think about how you currently incorporate and extract information from data and carry out your modeling (e.g. budget projections given prior years revenue). Crudely we can discuss this process in four steps:
Perhaps you’ve never thought about this, or maybe you have; either way something doesn’t seem quite right with this workflow. Especially the 4th step which seems rather fragile and almost invites human errors! ****We explore part of the reason for this in 🤔 Something to Think About, while ‣ discusses how you can completely change the workflow and fully automate step 4!
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Our mission is to fundamentally change the way you leverage and manage the world's uncertainties. If you think about we manage uncertainties every single day. For example, when you decide not to bring your umbrella as you leave the house, you essentially make a bet, you are betting that it won’t rain that day or that you at least won’t get caught in the rain. You have decided that the inconvenience of carrying the umbrella outweighs the chance of rain; of course you can never know for sure and sometimes you loose that bet and get soaking wet!
The same principle naturally applies to every single industry. However, the kind of uncertainty is significantly more complex. This requires corporations to create and use models to navigate those uncertainties specific to their respective industries and markets they operate in. For example: insurance models may try and predict the required budget to pay annual claims; finance models might do revenue projections; construction development models estimate costs and timelines; and productions models may predict supply versus demand and the potential relationship with changes in GDP. The thing is, all of these models contain, in some shape or form, a variety of hidden variables and drivers all of which necessitate some level of guesswork, which is why we also refer to them as assumptions. For example, a company is launching a new product line. They need to model its performance and as they have no data they need to start somewhere. Perhaps their in-house analyst or CFO has a decent, but broad, idea of how a new product like this might perform in their operating market. The company would then use these intuitions as the model baseline or starting point. Alternatively, perhaps the company can use the performance of a previous product line as a basis for their assumptions - although they probably would make some adjustments as the product lines are different. Of course, once data/actuals start coming in, the company will readjust their model.
However, this is where the problems begin! If you are running this company or if you were the analyst how would you maintain and reconcile your model with actuals? As mentioned above, and as you’ll quickly see as you go through the tutorials and use cases below, this is very much a non-trivial exercise. There is a reason we have yet to find someone dealing with this problem, who would give themselves more than a 7/10 confidence score - in fact one analyst told us at times they were lying awake all night worrying as there was too much at stake. As a decision-maker, you run the very real risk of making decisions based on flawed foundations. Despite the fact you have an amazing analyst team, the fact is that anyone trying to maintain and readjust models, specifically their hidden variables, is simply futile. It is a complete hit or miss, and even worse, most of the time we do not even know if it is one or the other. So we cannot fault the analysts when businesses make bad decisions, and we cannot blame the decision-makers either as the basis upon which decisions are made could be deviously flawed.
This is what we, at Evara AI, are all about. Our software, Scenarios, entirely automates the process of reconciling and maintaining hidden variables (assumptions) in models across all industries such that those assumptions may be used to accurately make future projections. Scenarios ensures accurate readjustments that you can trust, that fully align and are explainable with respect to your model. Achieved many times faster than what is possible today. All you need is to specify your assumptions and record your actuals as they become available. Scenarios is highly data efficient and functions with as little as a single actual/data point. Scenarios takes the guesswork out of the game, renders naive and simplistic modeling obsolete, and provides better and earlier decision-making foundations.
Getting Started with Scenarios - Your Automated Statistician
Frequently Asked Questions (FAQs)
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