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 →

Jatin Naroda no published score: no usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.5/5 from 6 produced feed exchanges 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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Answered produced feed D 5 · C 5 · P 5 · Cm 4 4.85

Q Nice. That is slick. What were some of the most common use cases? Was it TRMs? Was it something else?

A Uh, mostly it was client portals and like internal systems. Um, so let's say, um, you are a marketing agency and you have multiple clients and many projects running on those clients. Um, you wouldn't want to give them access to an ocean database. Uh, what you'd want to give us create an app where each client can see only their ongoing projects and tasks, et cetera. So we built like a very, very solid, a very advanced set of personalization and data restriction features, um, uh, that actually help, um, like a person can see only their, uh, relevant roles, et cetera. So client portals, member portals, uh, internal order management systems. There are a lot of garages that use our product. Uh, a lot of educational institution that use our products. Um, so, uh, to manage like, um, just record keeping, uh, placements, so on and so forth.

AI assessment note: “mostly it was client portals and like internal systems.”

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

Q Nice. So it sounds like you, you clearly had product market fit. You had tons of happy customers. Customers. Loving the product. What made you ultimately decide to sell the business?

A So it was, um, a few personal factors, uh, primarily that dictated that we sell the business. Um, mostly for Samir and I, we have been on this journey for six years. So Notion Apps wasn't our first product. It was utilized at App, then it's Notion Apps. And then, um, we, we thought the business, one, we were both exhausted, we were continuously building and we were in on mode for like, Quite a long time. Just wanted a break. We just wanted some capital to see us through that break period. And then the second reason we thought it, the business is better suited in the hands of someone who could grow it faster. Um, because it requires some level of, um, energy and vitality that you can breathe into the product, especially with such a fast changing market, because going from no code to prompt based applications, it's, It requires a lot of vitality that you have, that you breathe into the product, and we, um, we just felt it was the right time to hand up the business to someone else.

AI assessment note: “one, we were both exhausted... second reason we thought it, the business is better suited”

Answered produced feed D 5 · C 5 · P 4 · Cm 4 4.60

Q That's amazing. Did any of the use cases surprise you? Like when you first started the business, I'm sure you had a few in mind. Did you have any users doing things that surprised you?

A Actually, the surprise came less from use cases because when we were building utilize dot app, we saw a lot of use cases. And we saw similar kind of use cases here in Notion apps as well. Um, but the surprise came, uh, both for Samir and I, it came from the fact that people would create like for T screens. So for T, for T screens to manage their business in one app. So imagine like in WhatsApp, imagine if you had 40 tabs and each tab is doing its own workflow. And, um, they just found a way to like weave every aspect of their business under one app. So that is what we found like the most interesting and exciting and surprising that we, with the sample apps we put on the websites, they are like website, they're like relatively simple. And then you look at some of the customer apps and they're like, okay, holy shit, this is, this is quite a lot.

AI assessment note: “the surprise came less from use cases... it came from the fact that people would create”

Answered produced feed D 5 · C 4 · P 5 · Cm 4 4.55

Q Nice. Speed from a buyer is huge. If they're willing to move quickly and close fast, uh, that's usually almost always, uh, worth a reduced price. Um, what'd you do to prepare for the acquisition? Did you put together any specific materials? Did you, uh, prepare P&L, anything like that?

A Yeah, I think, so after the first listing, Samir looked at the first listing when we, like, listed for the second time. He was like, this listing sucks, so we need to improve this, uh, the listing in that second, kind of, um, in a rebirth on a choir. So, um, and, uh, so Samir helped with a lot of aspects of the listing. We, um, The rest of the aspects I took up, we put up more analytics, PNL, um, we, we tried to communicate it, uh, better and any questions that we got from the initial bios, I created kind of a document where, and I keep answering those questions and like, why, what about your competition? What about threats? So on and so forth, whatever questions I got, I tried to frame it as a question and kind of add it to the document so that If you read it, you have answer to all the questions, and then we can proceed instead of like us wasting your time or you wasting our time either way. So, um, P&L, more analytics, uh, better vision, um, better like churn, strike calculations, so on and so forth, LTV, CAC, everything, uh, we kind of try to put it as comprehensively as possible.

AI assessment note: “we put up more analytics, PNL... I created kind of a document”

Answered produced feed D 5 · C 4 · P 4 · Cm 4 4.30

Q Totally understand. That makes complete sense. Um, if you were talking to another founder and they were looking to get acquired, what's, uh, Couple pieces of advice you think you give them?

A Um, I think if we divide the journey into two parts, um, one is like listing, um, uh, so list, when you list, list with as much information as you can, um, have a price that is good to attract buyers. So it might be lower than the price that you were imagining, but just, just a tiny, uh, fraction below it so that you can attract more bids. I think that is what we got wrong the first time. We kept the price too high and ended up suffering with like conversations, but not materially useful conversations, because in, even in those conversations, the buyer, the potential buyer would say, oh, this price is too high. And the thing is that what you're trying to gauge in those early conversations is that you want to have like a lot of velocity from buyer's end. And it is at a good price that the buyer thinks that they got a steel deal. And that is when the velocity from their end comes in. And if One is, if, if it's priced too high, then the velocity just doesn't exist. And then listing it again and again is, um, at some point of time, there is a cost of diminishing returns. Um, and like renegotiating or re bringing down the price and like there, there is, there are some drawbacks to it. So price it well, hopefully the first time around, uh, take a quiet steam self, um, help and kind of pricing it, um, use our own judgment. But yeah, so there's the pre Acquisition kind of part. The, th…

AI assessment note: “I think if we divide the journey into two parts, um, one is like listing”

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

Q Nice. And then how did you eventually Uh, land on the buyer that you sold to? Was it the offer that they made? Was it the terms? Was it you just got along with them?

A Actually it was, um, so, so this was June when we started kind of this process. We had some commitment to an investor who had invested some money with us. Um, so, uh, there was some implications there. So we couldn't sell the business right away without taking care of those implications. Um, and then the market was moving really, really fast and, you know, with The AI app builders coming along. Um, the perception of the market is changing, was changing pretty rapidly. Um, and I think a lot of buying and selling from what I understood happens based on perception. So if I think I can build this whole product, which might not be true in reality at all, just by white coding, I might not be willing to put money behind it. Um, so it is the perception that kind of Uh, drives the sale, at least the price. And, um, I think we, we tweaked the pricing a little bit. We reduced it after we got sort of the investor, uh, sign off. And then we were at kind of a sweet spot where in the, um, the, the buyer had a lot of intent. Um, so even if it was like, uh, a few thousand dollars here and there, the intent was very strong. Um, from the buyer and that is what kind of made us to take the final call that, okay, if this person is so, so interested and wants to move everything as with, at the speed of light, this is better than like haggling. So that is kind of where we, um, made the compromise and …

AI assessment note: “the buyer had a lot of intent... wants to move everything as with, at the speed of light”

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