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All right, we're gonna welcome Pavel Overtchouk. Did I get it right?

Yeah, almost perfect.

All right, and you're from Alviss?

Alviss.ai.

Tell me about what you guys are doing here at CES. Give me your elevator pitch.

Yeah. So, what we're doing is that we are building a platform to analyze scientific data online. We analyze the quality of the data and we extract insights from it. Then we redistribute the best information there is scientific information, to our users in the different applications that we have built. So the main one is called Alviss- Read Science Better. It's on the app stores already and what he does is that it allows users to select what they are interested in, and they will get the best publications, recent ones about it that just came out in an in ranked order by quality, right? So it allows people to do easily their scientific watch.

That's cool. So if I'm interested in let's say, what's happening in the solar field, as far as solar panels and solar energy, I can select that topic, and then you guys will go out and curate the best quality, the top ranking, the newest stuff, the newest science. Where are you sourcing the material from? Is it just the entire web or are you going to education resources or scientific papers? Where are you pulling data from?

So the data is mostly coming from journals in pitch sales, actually. So, the web in general, we are using a few that are public databases, as well. The goal is for us to find what's best out there and we analyze it, and then we rank it by quality. So the sources are, there's a lot of them and so what we're trying to do is just find what's best and redistribute to our users and find what's best for them, given their centers of interest, domains of interest.

So it's primarily science topics, so someone that wants to know about what's going on with dogs, a specific breed of dogs would not probably be a candidate for your app.

I think if they put in dogs, they will get veterinary papers or something like that.

Right.

Yeah, but the breadth of what we're covering is pretty large, actually. We are very good in biomedical information, we're very good at covering almost everything there is about it but, we are actually broader than this. So if you were an engineer, if you like to have new information about solar panels, as you were saying, or the chips you were talking about chips, you will have the best information, the recent one there is out there about it and ranked by quality, right? So that's the concept.

So how do you determine then who's right and who's wrong? How do you determine whether someone, is peer-reviewed articles? Are those the ones that get the highest rank? What is your stack? How do you figure out this is the best and this is the worst?

Yeah, I'm glad you talked about peer review because this is actually what you're trying to automate, basically, the peer-review process. There are more than 14 million papers that are proposed to journals every year so 14 million you can't handle.

That's right.

You just can't handle it. The usual way to do it is to have experts read it, two or three of them, and say, "Okay, this one is fine. I'm going to put a few comments to improve the paper or something" but right now, it's just not possible. There is so much pressure on professionals and it's a worldwide effect. So everyone wants to publish. Even countries are actually in competition to publish more, you know, better. So you have a lot of papers, and they all need to be in theory, validated, read by experts and stuff, but it's just impossible. You don't have enough people. Sure.

So if let's say there's an article that comes out on biomedicine, and let's say it hasn't been peer-reviewed, does your app give the opportunity for someone to provide feedback on that content?

Yeah. So, I'm glad you talked about it because you can see it from two points of view, either it's a paper that has been published, and what we do is actually look at what it's talking about the sources, the source of the information, who published it, who wrote it, everything. On the other side, we're looking at the feedback the paper has been having on the internet. So social media stuff like that. So it's kind of a general, like, it's peer review on steroids, actually.

So most likely if someone reads something in your scientists, and maybe they put it on Twitter, to their colleagues, hey, go check this paper out, it may be of interest to you that almost as a form of self-validation of that content,

Yeah. Now, you take this one, you multiply it by 100,000 and you analyze what is actually being said, and how positive it is, and how much it has been retweeted and all that, you get a sense of what we're doing. So it's obviously an algorithm, an AI algorithm that we're using that analyzes everything, and that is able to generate like an index to actually rank the papers. So this is basically what we do. What you were saying also, I would like to know if a prospective paper is worth talking about, is worth publishing, actually and we recently actually published a tool to do that. Exactly that. I called it the Editorial Assistant. It's kind of an automated reviewer, actually.

Right.

So you copy-paste, your obstacle, your text, your perspective of the perspective paper and it will get analyzed like immediately online. It takes a few seconds and this is something that we just published on the internet. It's an app.

So this, you say there's an app already available to be able to consume this content. So is there a web-based tool or is it all app-based?

Yeah. We're thinking about these in terms of user experience. We're trying to do apps for all of this and some of our apps are on mobile. The obvious returns better are the mobile and web app, so everybody's covered. We're on Google Play, we're on Huawei, we're on iOS, everywhere and the Editorial Assistant or a few of our other apps are actually into the Alviss dashboard. That is only online for now. Yeah.

So is it a paid app? A subscription? How do you guys make money? What's the business model and what's the goal we're here for CES?

Yeah. So the goal here is essentially visibility, I think, pretty much for everybody the same. So we're trying to get visibility and just show what we can do and showcase our applications that are, I believe, groundbreaking actually and very innovative. So this is the main goal. The Read Science Better, it's a free app, you can download it and use it. We are trying to monetize through ads in sponsoring as usual. The editorial, our assistant right now is in beta. You can use it it's online but we will probably make a paid version for it because you need the power to run some algorithm so and we have a few other apps that will be paid for like the Expert Explorer, we build a search engine to find experts. This one will be paid for as well.

Well, I tell you, it's always great whenever there's a tool where you can separate the junk from the good stuff is always a winner for me, Google tries, but often it's biased by so many different things not necessarily in accuracy or on authority. So it's good to see what you guys are doing here. So Pavel, thank you for coming on. Also the website is A-L-V-I-S-S.ai Let me say that again. A-L-V- I-S-S.ai. So definitely check it out. Thanks for being on the show.

Thanks for having me, it's a pleasure.

Absolutely.

TPN CES 2022 coverage is executive produced by Michele Mendez. Technical Directors are Kurt Corless and Adam Barker. Associate producers are Nancy Ertz and Maurice McCoy. Interviews are edited by Jo Mini. Hosts are Marlo Anderson, Todd Cochrane, Scott Ertz, Christopher Jordan, Daniele Mendez, and Allante Sparks. Las Vegas studio provided by HC Productions. Remote studio provided by PLUGHITZ Productions. This has been Tech Podcasts Network Production, copyright 2022.