The Exchanges

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 →

Adam Compain no published score: no usable exchanges on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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2exchanges match
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Answered produced feed D 5 · C 5 · P 4 · Cm 4 4.60

Q Okay. So give me a, give me a sweet spot here on average. What are, what are these customers pay you per year to use your technology and why do they pay you that? What are they getting?

A Yeah, sure. So typically we're looking at, uh, six figures and seven figure contracts. Typically we're looking at, I'd say low to often mid six figures and then, um, you know, low seven figures per year. The reason the value of the product is so high is obviously because of the value we provide. What we're tapping into is the ability to make customer facing decisions. So if you're a big industrial supplier producing a material that goes into, I don't know, Unilever or Procter & Gamble's supply chain, We're talking about a lot of material and value. So what we help these companies do is make more intelligent decisions about how to distribute that product into a business or into a consumer's hands. And that's on the order of, you know, multiple billions of dollars of hundreds of millions of inventory. So in short, not to ramble here, we make, Better decisions. We help them make better decisions for customer service purposes and actually for inventory management. What I mean is lowering the amount of excess buffer stock that you have to keep on hand to make sure that your shelves and e-commerce sites are full, uh, making different, more efficient transportation decisions. So you can ship, let's say more by an ocean going vessel than spending money to ship by air. And then you actually make your whole supply chain team a lot more productive and Because they're able to manage issues…

AI assessment note: “Typically we're looking at, I'd say low to often mid six figures”

Answered produced feed D 4 · C 4 · P 4 · Cm 4 4.00

Q people call it, you know, OT, uh, you know, to, to ARR ratio, whatever you want, coverage ratio, whatever you want to call it. Um, but that math works, right? That math works, which is important. Um, Okay. Uh, tell me about the first customer. So how'd you have an inside scoop with him? How'd you get them to commit to paying you money up front when you had nothing?

A Yeah, sure. So the company actually evolved quite a bit. We started, uh, selling our software to the logistics providers themselves prior to us evolving the business to sell to The brands and suppliers whose goods are being carried by those logistics providers. So they get big companies like Marist that have ships and containers. That's where it started. We evolved to the biggest companies, you know, you know, just as an example, a company like Nike or Home Depot or big physical, uh, businesses. Um, so anyway, the first customer, uh, the company was founded after some time I spent over in Hong Kong at one of these logistics providers during business school. I came back with a general insight and understanding of a problem space they were experiencing about moving assets around the world with, uh, kind of low predictability, and then my two co-founders and I went at the problem, um, to really address it and see what we could solve. Uh, we had built up, um, General understanding of the problem through interviewing folks in the industry, uh, gained a good vernacular and understanding of the underlying dynamics of the problem, and had basically a lightweight prototype hypothesis on how we could solve it. So through relationships, really cold calling, we found our first customer to work with, um, and work with them on an early prototype, first lines of code, Little mockups and see i…

AI assessment note: “So through relationships, really cold calling, we found our first customer to work with”

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