Taking up this point, "The company has also just recently...

Taking up this point, "The company has also just recently launched its cloud based enhanced screening platform for heart disease, specifically starting with aortic stenosis. EchoSolv has been designed to support faster, more accurate detection of aortic stenosis." I work with cloud financial data platforms and there's a key architectural advantage by putting data on a remote, co-habited analytics and data platform.

The traditional models of data capture and storage for analytics see infrastructure disparate, in different structures and owned and controlled by organisations that may or may not be inclined to negotiate with one another, and traditionally, to what end would they bother unless they were able to form a consortium and see obvious financial opportunities. These often fail early, as they require funding and a great deal of cooperation from businesses not designed primarily to do that work. All of which translates to a big barrier to bringing all of the data into a single database and analytics platform that is crucial to tuning the AI or simple analytics so that they recognise patterns.

With a cloud platform, you can collect the data at source for each instance of a scan [or other transaction]. The transformation of the data structures then occur on the way into the single instance of the cloud database and the effort to transform rests with the company providing the database - EIQ.

Picture signing a deal with a hospital that collects data at the point of the scan. And imagine they conduct 10,000 scans per year [40-ish per day assuming weekday scans]. Then charge a ludicrously low $100 USD per scan. Then picture signing deals with 100 hospitals [anywhere in the world] in the first year. 10,000 x 100 x $100 USD = $100m USD in revs year 1. Maybe throw a $100k USD license fee onto each hospital. Another $10m in revs. You'd probably leave that charge out in the first year as it is a hurdle you kind of don't need until sales rev up. Anyway, we can all model the financial impact of going out cheap and ramping up charges as take-up rates accelerate. Play around with the per-transaction fees, year-2 license fees kicking in and the number of hospitals for a bit of a laugh. What's slightly less obvious is the potential quick growth in the statistical criticality of the database of scan results.

The real point here is to demonstrate that there's loads of quick, linear cash-flow to be made with a cost-base for the data transformation work with each new hospital that consumes part of that extra unit of cash flow, rather than needing to raise funds to transform a multitude of historical databases and then copy them over to the cloud - to increase the pool of samples the AI has to tune itself into a better version. The transformation work is done by a relatively small team that becomes highly expert in how you get good, transformed and quality assured data into the database.

Now add in a range of different "pattern recognition" type scans such as Mammograms.

I'd expect to see a good deal of effort going into distribution from this point. If it's reliably saleable, implementable and supportable, get cracking! Money's not as cheap as it was 2 years ago which clearly discounts the flows a little more steeply. With good cash flow, though, you get the opportunity to go back to retail investors who are relatively less studious and more inclined to have their imaginations captured by these types of scenarios. I really like the story of this company. It's an easy one to sell to investors.