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 And so was that, if people wanted that diligence, was it one-off or do you guys have a SaaS model?
A It's a SaaS model. Um, similar to how it's, uh, it's how some of the companies like Zapier are doing like a zaps or 20,000 zaps. So the same way we did tabs tokens. Uh, so different types of assessments, uh, quote unquote assessments could be created, uh, in terms of The user going on, the investor going on and creating like, Hey, we want to learn about financial fundraising, product market fit. We want a data room, but we don't care about X, Y, Z. So it created a custom token cost for that assessment. They could actually turn that into a button and then put that on their website or an email drip. And then using Zapier set triggers to send that off at any point during their process. So it was a SAS model ranged anywhere from 500 bucks a month, all the way up to sometimes 75 K a year or more.
AI assessment note: “It's a SaaS model.”
Answered produced feed
D 5 · C 5 · P 4 · Cm 4 4.60
Q Wait, give me some other examples. Percentage of AUM, you ended up on the cookies, you ended up on the, on the token basis, sorry, credits, but what were the other things you test?
A Uh, so, you know, it's different when you get on the phone and the guy's like, all right, well, we have five hundred million want to deploy and automatically it's like, all right, well, 500 bucks a month doesn't really, you know, cut it for you. So we, we tried percentage of AUM. We tried, um, percentage of the amount you funded into the deals, even though your fund hasn't closed yet. Uh, we tried SAS pricing where it was just, uh, one fee and then it was like blocks of, you know, uh, Assessments. We tried a per assessment basis, and then we tried actually opening it up to founders. That didn't work well, so we went back to B to B and then we went ultra high, like B to B to E, I would say, or B to E to B, enterprise to business.
AI assessment note: “We tried SAS pricing where it was just, uh, one fee and then it was like blocks”
Answered produced feed
D 4 · C 4 · P 4 · Cm 4 4.00
Q Let's put that like in one. So it was a lot of like, sure. That's hard to follow. Uh, what, what did you guys sell for a 10 to 15 X multiple on trailing 12 months revenue? No.
A So we, what I was saying is like, because we spent like roughly 500 to, you know, north of 500 K on physical like payments to either vendors, subscriptions, those actual capitalized costs, uh, the cash portion of the deal, you know, was roughly like the 10 to 12% range. I mean, sorry, X range of that. But the remainder of the deal, uh, terms were taken as equity in pre IPO in, in the surviving court. And that, The percentage that we got of pre-IPO based on what we know about the roadmap and where we're headed with that, um, is it makes up for the difference in where we want it to be in terms of our vision for tabs.
AI assessment note: “the cash portion of the deal, you know, was roughly like the 10 to 12”
Redirected produced feed
D 2 · C 3 · P 3 · Cm 3 2.70
Q That's amazing. All right. And so how many customers did you scale to before the acquisition?
A Um, so whenever even, uh, potential users or investors asked us that question, the, the way that we turned that around was we have some that, you know, ran incubator accelerator programs for thousands of assessments. We had some that use it on a per deal basis. So, uh, in terms of users or customers, that wasn't the best metric to look at it by. I would say that we had over one million lines of iterated code written by the ML and AI system. That's how much the platform was used. Even today, The SBA company that uses it or the SBA lender, uh, from the state of Colorado, we still have passively after they've turned off all their funnels are still dripping through three or four a day. That's just that one person.
AI assessment note: “in terms of users or customers, that wasn't the best metric to look at”
