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SaaS Interviews with CEOs, Startups, Founders

Jiva burning $50k/mo after raising $1.3m Seed, Plan to Turn POC's into Paid Accounts

SaaS Interviews with CEOs, Startups, Founders

Nathan Latka

Ceo, Entrepreneurs, Founders, Software, Business, Entrepreneurship, Saas, Startups

4.6683 Ratings

🗓️ 29 November 2021

⏱️ 15 minutes

🧾️ Download transcript

Summary

Create multimodal AI systems

Transcript

Click on a timestamp to play from that location

0:00.0

So, oh, God, I don't have the number, but at least a dozen that are, that are or will be paying soon in our pipeline.

0:09.0

How many are actually paying, Manish? Come on, you must know this number. This is like the lifeblood of the business. How many paying customers?

0:14.0

So zero right now. Okay.

0:16.5

Next month, three.

0:19.5

You are listening to conversations with Nathan Latka, where I sit down and interview the top

0:24.6

SaaS founders, like Eric Wan from Zoom.

0:28.4

If you'd like to subscribe, go to getlatka.com.

0:32.5

We've published thousands of these interviews, and if you want to sort through them quickly

0:36.3

by revenue or churn,

0:38.1

cac, valuation, or other metrics, the easiest way to do that is to go to gitlatka.com and use our

0:43.6

filtering tool. It's like a big Excel sheet for all of these podcast interviews. Check it out right

0:48.6

now at getlatka.com. Hey folks, my guest today is Manish Patel.

0:55.1

He's trained in the biological dark arts of genetics, bioinformatics, and systems biology,

0:59.9

before spending some dark years in algorithmic trading teams and investment banks and hedge funds.

1:04.1

Now he saw the light.

1:05.2

He's co-founded a hospitality software business, a serial CTO, and then took the dive recently

1:10.1

into Jiva.a. where he's creating

1:12.1

multimodal AI systems. Manish, are you ready to take us to the top? Absolutely. All right,

1:17.3

multiple multimodal AI systems for what niche? Basically, we want to target healthcare first, right?

1:25.9

So the problem with multimodal AI is that actually

1:30.5

its application is everywhere. Wherever you look, wherever you're applying deep learning technologies

1:36.5

to learn about complex things, complex things inherently are difficult to understand,

...

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