Every argument clarity score on this site is built from rows on this page. Each
question and answer was assessed with names hidden, the host's own answers included, on
four things from 1 to 5:
directness (does it answer the question asked), coherence (do the ideas follow),
precision (concrete details and clear references), compression (says a lot per word). The weighted
mix (30/30/25/15) is the exchange score. A person's published score averages their exchange
scores on raw tape only, at least 8 of them, shrunk toward the cohort mean.
Full method →
Answered produced feed
D 5 · C 5 · P 5 · Cm 4 4.85
Q 2015. And I mean, where were you? Were you an engineer on the engineering side or the architecture side? How'd you get the idea?
A I had, I was on the, the nothing side. I was on the legal side. I have a law degree from Germany. Um, I focus on intellectual property law and I was writing a dissertation about what's the biggest problem we're going to have with technology and an IP law. And the answer was three D. You know, we have, we went through this with it. Um, but we kind of adjusted, the market adjusted. We have iTunes, we have Netflix. Um, when it comes to three D, the problem is that, uh, no one can really protect intellectual property. So we thought, okay, we can't just track a file down. We have to actually know what a file is of what it's similar to what's, what's in it, et cetera. So we have to actually understand what three D models are. And we looked at all the technology that was available and we realized Uh, bless you. Uh, we realize. We realized that, uh, nothing was actually built to understand three D from a three D perspective. Everything was just a two D, uh, technology like point cloud or whatever, trying to understand three D. So we actually created something that would, uh, identify IP theft. And then we brought it to market. 2016. We've showed it to a bunch of companies and they came back saying, well, that's great and all, but my God, we could use this in engineering. We've used this and just seeing if we can manufacture something.
AI assessment note: “I was on the legal side. I have a law degree from Germany.”
Answered produced feed
D 5 · C 4 · P 4 · Cm 4 4.30
Q That's great. So a third of 50, we'll call maybe 15 or something have converted from free to paid. Is that accurate? Sure. And I mean, is that the model? You kind of give them a free usage, get them addicted, and then they have to pay? And if so, like, what's the What creates the forcing function to move them from free to paid?
A Uh, we don't typically go free to pay with companies. We do that. Um, those were, these were earlier, uh, deals that we had signed up or alliances that we had formed essentially with these companies who had unique perspective into the software with larger companies. Sometimes we'll do a trial where they'll be able to try it out for, you know, 30 days, maybe even 60 days or something with some users and see, make sure that it does what we say it's going to do. And it does. Um, but typically, um, you know, It's, it is a pay to play thing. You do pay for seats. Um, but we have demos. We're willing to, uh, let people play around with it and make sure that, you know, it really does have the effect that they think
AI assessment note: “we don't typically go free to pay with companies.”
Partly produced feed
D 3 · C 4 · P 4 · Cm 3 3.55
Q Yeah. Really interesting. Okay, so how many, how many users have you scaled to today? How many folks using it?
A That's a great question. So, uh, there are quite a few licenses out there being used, and the reason it's hard for me to put an exact number on is because we have some institutional agreements with, like, uh, FISNA, the US Space and Rocket Center, NASA Space Camp, um, and, you know, and that, the latter case, they have, um, you know, 300,000 people a year coming through who have access to it, so I don't know how many people are actually utilizing it at any given time. In the case of Purdue, they have 1400 people who can, uh, use it at any given time, too, so it's hard to say how many are actually using it at any one Time. I could look it up, but maybe, okay, let's do a simple.
AI assessment note: “it's hard for me to put an exact number on is because we have”
Not addressed produced feed
D 2 · C 4 · P 3 · Cm 3 3.00
Q Yeah, that's great. Okay, good. So, two million raised to date, 15 in Ohio, um, Hoping to get to five million AR by the end of next year, somewhere a little less than a million bucks right now. At least you have vision to that. What did you grow at over the past 12 months? So where were you exactly a year ago?
A So up until, um, end of August this year, um, we were primarily focused on, um, proving out all the different, uh, software applications because we had switched from IP protection to engineering. So we had all these companies switch over, test it, test it, make sure we can integrate it. And then also we were going through, uh, with some very, very large companies, some of the largest companies in aerospace, uh, et cetera, manufacturing, uh, medical devices and whatnot. We were working with them on, uh, you know, Proving out the concept, going through one layer at a time, and, uh, working our way up to, like, the C-level in each of those companies. And so to do that, we said we were primarily focused on not adding on too many small, what's called smaller customers at the time, because our concern was, uh, since we were so small, our support staff was our development staff. And so he said, let's think longterm here. So we didn't, uh, we wouldn't allow anymore. We kind of cut, we kind of kept the lead list quiet. We kind of said, we're not going to be trying to actively sell to too many of these people until after that happens. And then after, uh, August and September, we started, we said, okay, now we can open up the channels. Now we can actually start to have the sales happen. And so we started recruiting for our sales.
AI assessment note: “we were primarily focused on not adding on too many small, what's called smaller customers”
