No I'm far from being anyone special. I am a consultant in the aerospace sector (engineer + masters in BS administration). So what I do is help people buy large fleets of aircraft, the maintenance contracts etc, and then set up and advise on the asset management of those fleets. So what mCloud does, we've been doing for decades on aircraft. We buy software to manage the assets very similar to AssetCare, but much more complex.
For example, AssetCare's heat exchanger product changes maintenance from a scheduled maintenance event (i.e. replace/service every x months) to a on-condition predictive maintenance (i.e. replace/service when AssetCare tells you to). We do this on a much larger scale. We have Reliability and Maintainability engineers that help us massage large/complex maintenance programs such that we limit downtime, etc. We are always trying to eliminate maintenance requirements that aren't required or repackage the certain out of sequence maintenance requirements into larger scheduled maintenance events. i.e. if we are overhauling the aircraft, might as well do this or that now etc. This is very valuable because it's not the service cost, it's the downtime that cost money. If you can plan that better, it's worth a lot.
New aircraft are becoming more and more on-condition as they increase the number of sensors on them. A simple example is a magnetic particle sensors in engine oil that lets you know when to change the oil (we've had this for decades). Some cars have this now too. More complex aircraft like fighter jets have strain gages all over them to estimate fatigue index on the structure. The modern maintainer spends his or her day resetting fault codes after each flight.
So I've dealt with these aircraft software companies many times and seen the money they print. Replace aircraft assets with building, O&G facility, Heat exchanger, wind turbine, etc. There are tons of assets that cost a lot of money and who are not optimized. It's a really long tail here.
A little technical:
When I was doing my engineering degree, I specialized in control systems (e.g. robotic controls etc). The classical way of doing robotic controls is through feedback loops wit linear models etc. But I took some grad courses as well where we were using machine learning to automatically learn the robot's dynamics to control it. One type of machine learning is using neural networks (NN), but any type of learning algorithm is "AI". You can take a NN or some other type of algorithm and feed it random data over and over on the robot arm or whatever. You use a formula to update the nodes of the NN (i.e. the error or difference between your input command and the bad output you got) and eventually the NN starts to output the right voltages to control the robot arm based on your desired command. You can do the same thing with HVAC or whatever. For example, the "Digital Twin" is just some machine learning algorithm (e.g. neural net) that has correctly learned whatever you want to control.
So I also consult on aircraft simulators. An aircraft simulator is just a digital twin of an aircraft for pilots to train. Now they don't really use machine learning or AI to model the aircraft. They are still at the stage of using engineering formula's (e.g. force = mass x Acceleration) and estimated coefficients they estimate from flight testing to simulate the aircraft. Alternatively, you could throw a bunch of sensors onto an aircraft, fly millions of iterations of speed, angle of attack, landings, takeoffs etc - take all that data and pump it into a large neural net. Then you would have a model of the aircraft without one single engineering formula. But you can't really do that with a new aircraft that just came out. And you would probably have gaps if you didn't have enough data etc.
So I get the technical aspect. Modelling a heat exchanger or a wind turbine is a lot simpler than an aircraft.
So I guess to answer your question, I'm up in front of management all the time talking about billion dollar acquisitions. Talking to finance people (like these analysts) who only know numbers. So you do your best to answer questions in the 15 minutes you have. Which is why I don't really pay 100% attention to deconstructing every sentence in a conference call. Plus from my experience, so much changes in a month. Especially in a startup. So you need to give a lot of wiggle room. If we were a $1B rev business, things can change a little slower and you have a longer term strategy etc.
