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 →

Anur Ravan 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 5 · Cm 5 5.00

Q Yep. Okay. That makes good sense. And how are you, talk to me about the engine you're using. To kind of get these companies. Is it, is it manual or are you buying access to like Clearbit and full contact and some other things? How, how's the system, the engine work?

A So, so it's a combination, uh, it's a combination of a few sources. Uh, we tap, right now we have about 85 different sources, uh, that we tap into. Some of them are the most naive ones, you know, the directories, the websites themselves, um, the, the, the U.S. Patent Office, uh, job hunting, uh, directories and sites. Uh, we do a lot of NLP work on the articles. We go ahead and crawl the articles that every company, that the company being reviewed was mentioned in. And from those articles, we are able to extrapolate positive sentiments, negative sentiments, competitors, and lots of other stuff. Um, so all of these sources are run through our business engine. The business engine has a comparable function. So we actually calculate valuations and, and chances of, of exit, et cetera, by comparing the company to the comparable companies that are engine found. Uh, and we have a business rule engine, which flags We have possible risks and possible opportunities for the company based on the information that we found. I'll give you just a couple of very quick examples. If we see that the company, if we see that a company has been hiring employees or is on a hiring spree lately, we flag that as something which, which the investor should, should note. If we see that a single company, a particular company has, you know, 20% more core engineers than its closest competitors, We flag it as an…

AI assessment note: “we tap, right now we have about 85 different sources”

Answered produced feed D 5 · C 5 · P 5 · Cm 4 4.85

Q I see. This is congratulations on the growth. This is very cool. Um, thank you. Talk to me in terms of, uh, in terms of team size today, what are you guys at?

A We're 15 people. Uh, we have, we have a dev team of, uh, six. Plus a head of R&D. So that's seven altogether. Uh, we have a desk of four, uh, what we call content sort of researchers. Uh, they're not typical analysts. None of them has any financial education, but they curate the result. Every report that is sent out, uh, is a result of our technology, which gets all the information and runs it through the business engine and puts it on the template, which is digestible and easy and easy to understand, but it's then proofread and curated, uh, for, for sake of quality. We basically believe that Uh, the moment you order something which has the, the word report in it, uh, you expect that to be curated or proofread and quality controlled. So that's, that's the team. That's what the team is doing. We have another content writer and then it's my partner and me.

AI assessment note: “We're 15 people. Uh, we have, we have a dev team of, uh, six.”

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