Conflicting sources can be a signal, not just a problem

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 14:42

   But we would also have alternative data like satellite information or credit card information or whatever. And so on the buy side, my number one question is, are you looking at my data? which is probably not, but how do you assure people? How do you integrate with systems that you don't necessarily even see? Yeah, and I think there's two questions in there, which is the first one is if there's so much data available, how do you know what's important?

2. 15:09

   So when we're looking at X data, it's a natural conversation when we speak to people or let's even expand it out to any types of data. One thing we found with people is they really will have their trusted people who they'll follow of these voices in industry, especially in commodities. There'll be the suppliers of commodities. There'll be the LNG, all of these facilities who will be active on there because it's the quickest way for them to also give a status update.

3. 15:40

   Wow. So it's interesting because we found that a lot of our users will tell us about this. So it's like, oh, OK, so you find it as a trusted source because you believe this certain individual sketchy right and so it's it's one of those things where we have found a way to uh try and filter out as much as possible but when we are showing it to someone we give a like confidence score of okay someone is saying this one thing but this is everything else happening around the conversation because what's interesting to people is if there are divergent sources like an example would be how many ships are going, how many planes are going above us right now. If Reuters is saying something or Bloomberg is saying something and Wall Street Journal is saying something, they may not all agree. But the fact that they don't agree is a point of data because is there an opportunity

4. 16:33

   there? Because no one knows. So that's how we try and give people information. And then the second question is, you know, people's data sits everywhere. And I think there's different types of integration. The first one is what about like their messy PDF data? That data is what we've built is like a vision language model to understand it. So we'll take the data that they can upload to their cloud version of Moshi.

5. 17:00

   And then we actually go and we break it down and then build it up in our database. And there are certain types of data where you just have to connect to their systems. And then that's like more of a conversation. Is it important to, I assume you're running this vision language model yourself, is it important to own that? Or would you want to lay out, I mean, there's any number of vendors, Reductor is like a non-document processing one.

6. 17:27

   How important is it to have your own models versus just use whatever's off the shelf? I think that's a question that a lot of companies are battling with right now. The first question for us is, if someone's giving us their data, we don't want to be sending it to a third party. because there are systems out there which can do these things for you. But you want to make sure that you've got that agreement on how are they processing that data?

7. 17:52

   What are they doing with it? That's the first question. The second one is, if you don't have a team of engineers, then that is a great solution. But if you do have a team of engineers like us, a fun fact is most of the team has in some capacity worked with NASA, then they want to build the models. but not everyone has that. And so, you know, I think it really depends like where you are as a team. Yeah, got it.

8. 18:21

   Okay, and then coming into your talk,