Dr. Anand Verma

So if you remember what happened to LinkedIn back in 15 years ago, they start to ask you about, you know, Dr. Tamara, which university did he go to? Hey, when did you last graduate? Hey, which company did you work? So it kept asking you that question. So that progressive disclosure build your profile to be accurate.

So if you remember what happened to LinkedIn back in 15 years ago, they start to ask you about, you know, Dr. Tamara, which university did he go to? Hey, when did you last graduate? Hey, which company did you work? So it kept asking you that question. So that progressive disclosure build your profile to be accurate.

Now it's become a muscle memory, but back in the day, they'll give you a 60%, 70%, 80% profile completeness score. So we are following a similar psychological path, right? So, but in our case, 60% is already there from the public data. So adding the rest 10, 15, 20% over the period to come is where we kind of get a lot of private data from our customers.

Now it's become a muscle memory, but back in the day, they'll give you a 60%, 70%, 80% profile completeness score. So we are following a similar psychological path, right? So, but in our case, 60% is already there from the public data. So adding the rest 10, 15, 20% over the period to come is where we kind of get a lot of private data from our customers.

Now it's become a muscle memory, but back in the day, they'll give you a 60%, 70%, 80% profile completeness score. So we are following a similar psychological path, right? So, but in our case, 60% is already there from the public data. So adding the rest 10, 15, 20% over the period to come is where we kind of get a lot of private data from our customers.

It's a really, really good question. And I think with AI, there's a huge amount of debate on how do you kind of look at that line of ethics versus plagiarism or copyright, for example. So I think two things kind of keeps me awake all the time. One is transparency. And then second is the autonomy, right? So every data that we collect,

It's a really, really good question. And I think with AI, there's a huge amount of debate on how do you kind of look at that line of ethics versus plagiarism or copyright, for example. So I think two things kind of keeps me awake all the time. One is transparency. And then second is the autonomy, right? So every data that we collect,

It's a really, really good question. And I think with AI, there's a huge amount of debate on how do you kind of look at that line of ethics versus plagiarism or copyright, for example. So I think two things kind of keeps me awake all the time. One is transparency. And then second is the autonomy, right? So every data that we collect,

we show the source of where did the data actually originate from. That's the first thing we do. And the data provenance is, and the traceability and auditability is very core to our heart because climate and finance especially is an auditable asset, right?

we show the source of where did the data actually originate from. That's the first thing we do. And the data provenance is, and the traceability and auditability is very core to our heart because climate and finance especially is an auditable asset, right?

we show the source of where did the data actually originate from. That's the first thing we do. And the data provenance is, and the traceability and auditability is very core to our heart because climate and finance especially is an auditable asset, right?

So we want to make sure that the accountants in the financial services organization can look at our data and say, we believe in the source of this data, which means that this is auditable, right? So that's one component. The second thing, which is I think a lot of debate out there, but I really believe that human in the loop is really important.

So we want to make sure that the accountants in the financial services organization can look at our data and say, we believe in the source of this data, which means that this is auditable, right? So that's one component. The second thing, which is I think a lot of debate out there, but I really believe that human in the loop is really important.

So we want to make sure that the accountants in the financial services organization can look at our data and say, we believe in the source of this data, which means that this is auditable, right? So that's one component. The second thing, which is I think a lot of debate out there, but I really believe that human in the loop is really important.

So I might not have all the employees in my organization, but with AI and I'm in the loop, I feel more confident that jobs will get done the way I want it, right? And that's really important, right? So all in all, we feel that, you know, ensuring that AI augments the human agency is really core part of our thesis. AI should not replace humans, it should augment humans, right?

So I might not have all the employees in my organization, but with AI and I'm in the loop, I feel more confident that jobs will get done the way I want it, right? And that's really important, right? So all in all, we feel that, you know, ensuring that AI augments the human agency is really core part of our thesis. AI should not replace humans, it should augment humans, right?

So I might not have all the employees in my organization, but with AI and I'm in the loop, I feel more confident that jobs will get done the way I want it, right? And that's really important, right? So all in all, we feel that, you know, ensuring that AI augments the human agency is really core part of our thesis. AI should not replace humans, it should augment humans, right?

So I think that's been fundamentally the core part of our manifesto of our technical design. And we make sure that we always look back and say, are we crossing the line or are we within our manifesto thesis?

So I think that's been fundamentally the core part of our manifesto of our technical design. And we make sure that we always look back and say, are we crossing the line or are we within our manifesto thesis?

So I think that's been fundamentally the core part of our manifesto of our technical design. And we make sure that we always look back and say, are we crossing the line or are we within our manifesto thesis?