Like the article and some of the comments here suggest, it took years of domain expertise (most of the team comes out of the Tea Institute at Penn State, a research Institute for tea and tea tasting), followed by years of R&D to collect the proprietary data-sets and develop the models. And then it took a year or so to build a product around the AI's predictive capabilities - this isn't the shortest or easiest path, but we're still going strong!
I think companies like this are hard to build, hard to fund, and hard to compete with.
Where I disagree with other comments is on the competitive side; we've developed a few of our own algorithms[2] (not generic or even "played with some options" neural nets / deep learning) trained on specialized and proprietary data set from years of work and collection - now that we've dug our moat, I don't think anyone will be competitive with out specialized AI for modeling human sensory perception and predicting preferences[3] of food and beverage products anytime soon!
[1] www.Gastrograph.com
[2] https://gastrograph.com/resources/whitepapers/local-fisher-d...
[3][PDF] https://gastrograph.com/resources/whitepapers/2017-market-pr...