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284 Audio.mp3: this mp3 audio file was automatically transcribed by Sonix with the best speech-to-text algorithms. This transcript may contain errors.

Pat Utz:

We're scanning updates across these regulatory bodies and these legislative bodies and we're using AI to figure out what's relevant for a law firm and their clients or, in the case of a company, what's relevant to that company and their business units. If a company has a big labor force, they have to understand how government is changing laws and codes as it pertains to their labor force. Right, the local, state and federal level. There's over 145,000 government entities in the US alone. You know we're talking Congress all the way down to your local school district. Right, that school district is a government entity of itself. So our attack is going out and picking up any updates from these government websites and also picking up signals from news and social media. Thank you for having me on, craig. So my name is Pat. I'm one of the co-founders and CEO at Abstract and we've been at this for about six years now.

Pat Utz:

A little bit of background on us. We started in classical research in natural language processing back in 2019. Computer engineers and electrical engineers by trade, but really passionate about trying to make sense of laws and codes and sort of bring transparency to the opaque, you know, legislating process. What ended up happening is we sort of got tapped on the shoulder by the head of entrepreneurship at Loyola Marymount University and he said hey, I think this research that you're doing can apply to people in the lobbying space stakeholder management tool for lobbyists worked with over 200 firms and then raised venture capital to expand to our current focus, which is, you know, mostly working with fake law firms and large companies with their legal teams, taking in all the changes across regulatory and legislative bodies and using the reasoning capability of AI to distill what may be a risk or an opportunity to that business. So we've got a chance to really get a deep understanding of how the sausage is made and sort of using AI to distill what's actually relevant across all of this.

Craig Smith:

Yeah, and describe exactly what the platform or product does. I had another company on probably four or five years ago that was using AI to identify targets for political fundraising and I found that fascinating. But this is specifically looking at the legislative process and helping lobbyists. So how does it describe how the product works?

Pat Utz:

Yeah. So if you look at our government structure, it's sort of the classical three-tier structure, which is the executive branch, the legislative branch and the judicial branch, and we have a flavor of that at the federal level, the state level and sometimes at the local level as well. So what we're focused on right now is mostly the executive and the legislative branches, and that is in the form of legislative bodies so think a state legislature or Congress and specifically within those legislatures, it is the bill that is being introduced and amended to essentially create a new law or change an existing set of laws. And on the executive side, it's usually a regulatory agency and they're introducing, you know, changes to the rules right that they've set out or which we've seen a lot of recently, their executive orders right, or governor orders at the state level. So these are all sort of key parts of how government dictates what society does.

Pat Utz:

And what we're doing at Abstract is we're scanning updates across these regulatory bodies and these legislative bodies and we're using AI to figure out what's relevant for a law firm and their clients or, in the case of a company, what's relevant to that company and their business units. The reason it matters is usually one of a couple of key examples, which is usually product or labor right. So if a company has a big labor force, they have to understand how government is changing laws and codes as it pertains to their labor force right the local, state and federal level. On the product side, you know, if you're in financial services space, as an example, you need to know what are the changes that may happen on the regulatory set of framework that may impact your product roadmap and your ability to sell in a certain market. So increasingly we see sort of those two sides. The last piece is taxes and finances. So how a government changes laws may impact a company's ability to actually sort of change their tax outlook, and that happens also at the local, state and federal level.

Craig Smith:

Yeah, and how do you do that? Well, first of all, let's go back to the original research before you guys were tapped and told this would be applicable to government. What was that research originally?

Pat Utz:

Yeah, so the research was pre-OLMs, so we were working with classifiers and we called it natural language processing. Very simply put, it was the area subset of AI focused on processing natural language. So the idea was to use these classifiers to essentially take bills and legislation at Congress level and make summaries of it. Right, which is why we called it abstract, and the idea was actually to make a consumer application. We said, hey, if we can make summaries of these complicated bills, we can bring greater transparency to this process as a whole. And I think you know, to this day, it still rings true in terms of our mission of making all this data more accessible for the everyday person. And even though we're a B2B company right, we sell primarily to law firms and enterprises I definitely think there is a path forward in the future for us to expand these services for the daily use case for each individual and does the platform track everything that's published in, say, the congressional record, or does it depend on a user uploading documents and asking for analysis?

Pat Utz:

Yeah, so this is part of what took a while to build and develop expertise around. So there's over 145,000 government entities in the US alone. We're talking Congress all the way down to your local school district. Right, that school district is a government entity of itself. So our tech is going out and picking up any updates from these government websites and also picking up signals from news and social media. Part of the unlock here and I think this is going to transform the way a lot of this work, the work is done in other industries is agents and their ability to go out and autonomously pick up these signals and process that right for an application. So that's been an unlock on our end, although we've been working on that for about three years now. But, yes, we're picking up all these signals and we've built that in-house.

Craig Smith:

And including the text of tabled bills or bills that aren't tabled. I mean yeah.

Pat Utz:

Yeah, yeah. So the way it works is there's usually the existing set of codes and laws in that state, city, county or federal entity and then every year or every session, the legislature works on all of the proposed bills. And just for context, you know, in California there's about 5,000 to 7,000 bills a year. That's a lot of bills. And there are oftentimes spot bills that are introduced which have no language at all and their primary focus is to just exist and just go through the process until the very end where they're gut and amended, which implies sort of what you hear right there. The name might have to do with water rights, but it's gutted to be some sort of tax amendment, right. So that's a very common process.

Craig Smith:

Yeah, I didn't know that. In other words, it's sort of like what do they call that? A SPAC or what in Asia they call the backdoor listing, where you have an empty vessel that's listed and then you can fill it with whatever you want. Is that right?

Pat Utz:

That is what a spot bill is. Yes, especially one that's gut and amended, because it's gutted and amended to talk about something else. That's one tactic. Another tactic is using the budget or trailer bills to get policy through. So the thing is that bills that need some sort of appropriation, they need some sort of budget to get something done, they have to go through a separate process and those bills end up being quite large in nature, sort of like this big beautiful act or big beautiful bill that we're seeing at the federal level.

Pat Utz:

And this is where you know lobbying firms, associations, elected officials. They take the opportunity to sort of add their amendments because on the legislative process it's easier to get something through that big bill instead of sort of making a bill in of itself for that issue. So all this information needs to be tracked. Historically, you know, a company or a nonprofit or a law firm had to hire people to actually read these bills and figure out what's relevant From there. They would figure out a strategy if they want to lobby to change it or do nothing and let it become a compliance issue down the line. But AI is fundamentally changing how we think about this process as a whole.

Craig Smith:

Yeah so, but again, the big beautiful bill is an example. Does your platform pick that up once it's published or you're not relying on a user to upload it and ask questions about it?

Pat Utz:

Correct. We're providing the data, we're scanning and monitoring that twice a day. So we have like millions of records every day coming in Um and then for each client.

Craig Smith:

We then use that reasoning capability of these model to figure out what's actually relevant for each of them I see, yeah, can you uh on the on the big beautiful bill, because a lot has been made, uh, of its various amendments, um and a lot, and so when will the few members of Congress have actually read the entire thing? Is there a conversational interface, then, where you can ask questions about a specific piece of legislation like that bill? You can ask lists, all of the things that have been added in that are not directly related to the budget, that sort of thing.

Pat Utz:

Yeah. So that's a really core part of the workflow, which is asking deeper questions about the document and especially relevant when the document is as large as that big beautiful bill and sort of where you know we're building expertise is giving abstract that context on okay, I am an expert at understanding public policy, regulatory changes, right Connecting that bill with other bills, other regulatory amendments, because if you look at governance as a whole, everything's connected, right Even down to the judicial level. So all this is being done with context on what the business does and that is a core part of sort of the value that we're building with OutShark.

Craig Smith:

Yeah, and you said there is a conversational interface for the user.

Pat Utz:

You can just ask questions like hey, like what part of this bill is relevant? Right, Can you highlight pieces I need to focus on? Right, so you don't have to read the bill itself? And we're seeing this sort of flow in other industries as well, where AI is leveraged, you know, with some sort of industry expertise, to analyze a dense document or a dense data set.

Craig Smith:

Yeah, and for your analysis engine, is that a fine-tuned model, open-source model, or are you hitting one of the proprietary models with an API or how do you manage that?

Pat Utz:

Great question. So what I can say is like we are pretty adamant about not building a custom model in-house just because our philosophy is such that the big foundational model companies like OpenAI, anthropic that's their focus they're going to continue pouring millions, billions into that, and we want to leverage benefits of that as they get better. So we are, though, fine tuning aspects of these models in house, and then we're also pairing it with contacts on each business. There's a lot of work that's been done with retrieval, augmented generation, or you know what people call RAG, and that's just providing more documents that you're telling the model. Just pick context from here and don't hallucinate, right, this is sort of like your universe where you can reason. There's a lot of frameworks being developed on that. I just get better every day.

Craig Smith:

But, yeah, to answer your question, we're doing a mix of both and, as we've been developing expertise with the actual models, yeah, and can you give an example of how a law firm, for example or yeah, so let's say, a law firm that's handling compliance for a bank how they would use this?

Pat Utz:

Yeah, so with a law firm, the first issue if they're a big law firm is understanding what changes in regulation and legislation are relevant for which clients.

Pat Utz:

So the first step is having abstract, you know with its context of their clients understand that, hey, this regulatory change is actually going to impact this bank. You know banking industry type client right. Once it identifies that, then it's a matter of calculating how it's going to impact that client right. It may be an impact to their upcoming product that they're developing or maybe an impact to their labor that they have in that state. So, depending on the risk that arises, abstract is able to reason through and calculate, like how that's going to impact the client. And then from there, that's where strategy sort of takes place with respect to, you know, hiring a lobbyist or doing nothing and letting this turn into a compliance issue. Yeah, I will note is that compliance is a reactive measure and I can touch on this in a bit. But there is a difference between compliance and being reactive and then sort of what we're focused on, which is more proactive side of the space.

Craig Smith:

Yeah, and who are the main users? I mean, is it law firms? Is it what the hell government's doing right? Which?

Pat Utz:

is a pretty big universe and only increasing. However, right now we're very much focused on large law firms and in-house legal teams within companies, and those legal teams usually work with government affairs or public policy. Those are dedicated, uh, you know, lobbyists and experts within a company, right, that work I'm about. So those are the two focus areas that we have is law firms and corporates, um, within that realm, uh, you know, sort of varies who's working on trying to understand, uh, trying to understand the government, um, but usually we get pulled from, like, the research people within these law firms or the business development units, um, and then within the company, uh, usually the pull is, you know, whoever's ripping their hair apart about the tariffs that you know they're still trying to figure out, or you know, whatever it is that's coming out that's going to impact the company. There's, there's a it's a big universe of stuff.

Craig Smith:

And that's one question. And and do you, does it cover a global? I mean, for example, you know, the AI act in Europe or GDPR. Does it cover changes? Because that's a whole, nother animal.

Pat Utz:

Yeah. So I mean mean it's a good example. You know, over the last year about 700 proposals uh were created um just on ai policy across the states. And that's because at the federal level, especially with this administration, there is very much this lack of appetite to do any sort of federal oversight on.

Pat Utz:

AI in the US, whereas in Europe, this has become increasingly a priority to regulate at the EU level. So touch on your first sort of question comparing these bills and seeing if there's contradictions is a very core piece of what we do and it's the reason why we have to ingest this data ourselves. It's why we're scraping and structuring all this noise ourselves. So with that corpus of data we can draw connections and ties right and maybe even see if there's contradictions on the international uh sort of stage. It's very much top of mind. Um. You know we're definitely focused right now on within the U? S, but we plan to uh expand internationally um soon thereafter.

Craig Smith:

Yeah, and you mentioned that there may someday be a consumer platform, because that I don't know how many people are really interested, but it would be wonderful to be able to go to something authoritative and, you know, ask what's being done on self-driving legislation. You know, at the federal level and across the states, and it would. Your platform would pick up all of that right. It ingests only at the federal level, but at the state level.

Pat Utz:

Yes, yeah, so the vision right is across, even local right, local, state and federal being able to abstract and distill what's relevant to the individual, and that should guide who you want to vote for. Yeah, and that should guide who you want to vote for. You know the issue is that we now vote based off of voter guides and whatever we're seeing on. You know, Facebook or friends are telling us right, but 99% of what you know these elected officials do is what we're talking about is legislate, is create bills 99% of the constituent body has no idea what these bills are.

Pat Utz:

So if we're able to make sense of and, and you know, sort of break down the impact that these bills have to the constituents, we should be a much more educated constituent body and therefore elect officials align more closely with what we actually believe in. So that is definitely, you know, sort of like the end goal, the end vision, and that same concept applies to business. You know, it's just different priorities.

Craig Smith:

Yeah, and you, when you were talking about government bodies, you said all the way down to school districts. Does this cover the school district level across the country?

Pat Utz:

Yes, so that's why there's 140,000 plus governments in the US is because of all the special districts, cities and counties. Even your local school district has some sort of board. That board meets on a regular cadence and they're putting together, um, you know, sort of rules that they follow, right, um? And we're able to track that as well, because they're all required by law to post this online. Um, well, wow.

Craig Smith:

Yeah, Wow, and and just cumulatively. Cumulatively, it seems that that the burden of that data would grow exponentially. Do you archive data, I mean as you update data with new information? Do you archive the stuff that's being overwritten? Archive the stuff that's being overwritten, or or is this kind of a living?

Pat Utz:

snapshot, uh, that moves through time. Yeah, it's definitely um, a living snapshot, right, that moves through time. But we, you know we have all the archives. So, um, I describe it very similar to like get uh, github, uh, where you know you have a piece of code and some people add some code and you know that's an addition, they delete it. You know it's an amendment, it's deleted. So we track all the changes and that's all archived. So nothing's lost and there's insights there as well. Right, if there was some, you know, tax policy that came out in California and that was amended over the years. Seeing how it was amended, seeing who amended what, these are insights that are valuable. So this was, you know, very hard to do a decade ago, but funny enough, you know, I predict that the sort of corpus of data here is, uh, it's still much smaller than the foundational model companies and what they're doing, um, although it's non-trivial yeah, uh, and can you uh track?

Craig Smith:

I mean, you were saying you can track amendments. Can you attract where certain amendments originated?

Pat Utz:

Yes, so amendments to policy come from the elected officials, right, as the bill goes through the legislative process, right? So if we have a bill that's introduced today, it has usually about 12 to 24 months to pass through all the committees, right, and then go through the other house, pass through those committees, be voted on unanimously, and then it turns to a bill signed by the governor or signed by the president at the federal level. When the amendments are proposed, that is posted publicly. So we do pick that up, um, and we know which committee amended the bill or who proposed the amendment.

Craig Smith:

yeah, yeah, um, I mean that just comes to mind the, the, the uh, lowering of the excise tax on tanning salons, you know, but they got a lot of press and I've never seen the person identified who introduced that. But I would your system.

Pat Utz:

We'll get closer. Yeah, yeah, we'll get closer. The thing is that amendments sort of come from the lobbying process and the lobbying process, you know, has various aspects to it. So, being very concrete here, you can have a lobbying firm, a contract lobbying firm, you know, testify or talk to the elected official about, hey, we need to change this right Because the client I represent wants this change. It's a very direct way of lobbying.

Pat Utz:

There's also trade associations and nonprofits that bring together a group of companies and they lobby on behalf of those groups.

Pat Utz:

Right, they're purposefully sort of trying to hide who's the sponsor behind that right, but you just see the trade association. On top of that, they're associations of associations. Right, to further add distance between who's actually trying to propose the amendment and the last piece, which I think is, you know, one of the hardest but one of the most effective, amended. And the last piece, which I think is, you know, one of the hardest but one of the most effective and we saw this a lot with Uber, right, and the contractor sort of definition of the contractor, the gig workers, right, which is using PR and marketing to sway the public to vote and, you know, send amendments to their legislators a certain way, because at the end of the day, the elected official cares most about their constituents. So if their constituents are saying you need to vote a certain way, they're going to listen to them. So if you have the ability to sway the public, vote. That's usually the most effective, but it's the hardest vote.

Craig Smith:

That's usually the most effective, it's the hardest and and those the platform, uh you? You said at the beginning that it it uh helps uh guide lobbyists of for the most effective ways to influence legislation. Does it come up with a plan Like if you want to influence this clause, these are the committees you should approach, or these are the jurisdictions that you should run ads, or something like that?

Pat Utz:

Yeah, so that is what we sort of label as, like, the strategy aspect. It's the most nuanced piece, it's what, uh, you know, to the extent I can share, um, something that, uh, we're very much working on and, um, we're very close to getting to a point where that is, you know, sort of exceeding the capabilities of any individual or team of strategists, because that involves understanding the industry, the client, the existing laws and codes, right Right now that involves law firms, lobbying firms, industry experts, right.

Craig Smith:

But this is what we're able to now accomplish with AI and agents. I have a niece who works in Alzheimer's research and lobbying, and they know which congressional staffers pay attention to that or which congressperson is likely to you know act on their behalf, but it's all through relationships. This would make it more scientific, so to speak, right In that you could, even if you didn't have a relationship, you could identify people who maybe you didn't know about, or firms that you weren't aware of. Is is. Is that right or or correct?

Pat Utz:

yeah, and even beyond that. Um, not to get super technical, but uh, there is, we're just talking about the legislative process, right? What goes under the radar typically is the regulatory process. That's sort of where the bill is implemented, the rules are applied and the people that work in the regulatory agencies are. You know, they're not elected and that is a totally different process of you know you can't really influence them, but you can get really granular and almost get to a level of influence where you know the way that those officials are looking at the public comments for the regulatory agency are swayed one way or the other.

Pat Utz:

But I believe that the personal relationships, so that's not going to go anywhere far anytime soon. It's going to be a while before we have AI legislators. I don't know, that's a whole other topic. But I think what this sort of technology is bringing to the space is, to your point, making it much more calculated and more scientific and, as a result, it becomes more accessible and more transparent. So it should level the playing field in certain ways. So it's it's fascinating, fascinating sort of seeing it unfold in front of our eyes.

Craig Smith:

And how? How do you charge for this?

Pat Utz:

Is this a subscription model or is it by seat? A lot of the value is more akin to what a team of people would do. So we actually do not charge off of seats, we charge off of coverage. The more government entities that we are abstracting for you, the more, and that we charge accordingly. But that's the value that we sort of see tied to the output for them.

Craig Smith:

Right, right, I see. So, yeah, you could have you know down to the school district level, or you could just focus on the federal level, or something like that. You would be charged differently, is that right?

Pat Utz:

Exactly. If I'm a you know big law firm and I'm trying to cover 20 more cities or regulatory agencies without abstract, I'd have to hire more people to cover those government entities. So that's why we charge off a coverage, because the alternative would be hiring more headcount.

Craig Smith:

Yeah, have you had any? Um, I mean, your job is not to affect legislation, is to provide the tools, but have you, have you had any? Uh? Are there any examples of legislation that was impacted by uh people using your platform and by the information that was surfaced?

Pat Utz:

Yeah, so like um. You know, one example is a city, um in the state of California that's one of our customers and you know there's like a and there's a dam that they were working on trying to get funding for it, and so Abstract was able to identify that this is a relevant bill, that if you were to amend a section of it, you will be able to uh obtain funding for the city to fund this dam project no pun intended, uh, literal dam for, uh for the lake Right. So, um, that was an example. Abstract identified something that's relevant. Help them, you know, sort of provide an amendment to that bill and the bill was able to be codified and they're able to, you know, obtain that funding to go forth that's sort of a best case scenario for us.

Craig Smith:

Yeah, Does the platform? Can it draft legislative language to propose?

Pat Utz:

Yeah, so drafting is part of sort of the generative AI aspect of what we do Right now. There's a couple of use cases in terms of generating content that we've been exploring Includes that also includes like reports and whatnot. But the short answer is yes, like that's part of like the generation of content that we are creating around this.

Craig Smith:

Yeah, I mean you can imagine that it could be really powerful in generating lobbying campaigns across all of the various channels. I mean, does it do that where you identify a piece of legislation and some language that is going to affect your company and you want it to change and abstract, gives you kind of a plan of attack and then it generates emails and legislative language to propose and you know, web apps, ads and all kinds of things. Does it do that?

Pat Utz:

Well, yeah, that's so. When we talk about the strategy and the overarching, you know, next steps like that, what you're mentioning are all aspects of that, right, that that is a key focus. Um, and it's what we're, you know, sort of fine-tuning and developing with our current customers. So I can't speak too deeply about it as of today, as we're developing and keeping some of that under wraps. But I would say thematically, what you're hitting on is right on the nail.

Craig Smith:

Wow, yeah, I mean I would encourage you to open it up to the public because there is. You know, there's a lot being written right now about people using AI to generate false narratives or deep fake videos and how that's eroding democracy. But the other side that I've always imagined is that AI could be a tremendous accelerator or driver of participatory democracy.

Craig Smith:

Where you know individuals or communities could use a tool like this to see how you know different proposals at different levels of government are going to affect them I know, in in my town there's this perennial uh, uh proposal to you know, rebuild the downtown and and you, you know you it'd be a full-time job to treat keep track of what's going on there. It's like every few weeks you read something but and you feel a little uh out of the loop. But if, if you have a tool like this, you you could uh check it uh daily. Or or or another question does uh, does this give alerts if you follow a particular subject? Would it, would it uh alert you that something's changing that you should pay attention?

Pat Utz:

to In real time. Yes, that is a core, core part. So you know you're looking at this downtown rebuild. You know, if the city council met and they uh, I don't know decided, hey, we're actually going to kill the planner, the planters, and we're going to do trees instead, right, you get an alert saying hey, I know you care about this because I have context on the fact that you live here and they just said that they're going to introduce trees. Um, do you want to reach out? Maybe you know, maybe you want the flowers back.

Craig Smith:

Um, but that's a really good example of sort of how it would alert you and do that based off of context of are really large corporations and law firms. Could it be made? Available at a price that individuals or communities could afford.

Pat Utz:

Yeah, so that's I mean. This is why we're starting here and then expanding, you know, outwards towards the consumer. It is very expensive, right, especially now in the earlier phases of AI is why we're an enterprise grade software. It's very bespoke, very custom white glove solution but, as I said, you know, I really I see this as an investment to make this more accessible for the masses and I don't think we're that far off. I think in the next five to 10 years it's going to happen. And it's like with any technology, right, you look at, you know it's an overplayed example, but Tesla didn't start with everyday car. They started with the premium cars and sort of got things right before they were able to make that available to the masses.

Craig Smith:

Yeah, and what's your view of democracy, that you're looking at this closely, because I'm a great believer in the strength of America's democratic institutions. But there's a lot of concern, as I said, about disinformation. But for years now I've talked to people about how AI could knit together the populace and sort of take a real-time measure of their opinion and that could be really powerful for democracy. So do you think the tools like this are going to shore up democracy? I mean, I spent a lot of my life in China and there's a concern, both among some Chinese, but certainly among people outside China, that the AI surveillance will be used to lock down a descent and and reinforce the poor, the communist party, so it can be used against democracy.

Pat Utz:

But yeah, I'm just curious, since you are in this space, what you think I think that, uh, a lot of what we see now is a result of companies um, you know, I'll be be very clear like companies like Meta, right, where their sort of North Star was retention and eyeballs right, and that's a North Star that can be taken in directions that are not productive for society, right, and sort of focus on amplifying content that attracts eyeballs but maybe not for the right reasons, right.

Pat Utz:

And so, you know, I think a lot of sort of where we're at right now is a result of this increase of what I call noise and silos of noise that are, you know, very much catered towards communities, you know, based off what they care about, and the original intent was to create audiences, you know, for meta, for Google and whatnot, to serve advertisements and monetize upon.

Pat Utz:

But you know, as a lot of us now know, that same technology is what led to a lot of these silos, which created, you know, at the worst end, extremist groups, right, and and so, I think, in order to, uh, you know, from an optimistic perspective, see like where things are going, um, I think this is now a main concern across most executives, uh, at companies, across most of the government officials as well, I think, the fundamental fabric of American democracy is still built on the constituents, us people, and I think that the people that we elect the elected officials.

Pat Utz:

They succeed or die based off of our, our vote, right, and so I think what's going to happen is there's going to be a greater sort of like shift in people trying to figure out what are the elected officials doing um, and and that's going to be amplified through all these channels. So I have an optimistic viewpoint where I think ai is going to help us bring more um accessibility and sort of more factual updates on what the officials are doing um, but we're going to get a lot more. You know fake data and fake. You know news um around that as well, uh. So I know that was a roundabout way of saying uh's hope, but it's going to be a nuanced and complicated road ahead.

Craig Smith:

Yeah, and that the fake data or disinformation. How do you guard against that slipping into your?

Pat Utz:

So we don't have much of that issue on our end because our data, by nature, is all official. It's all facts, right? It's the official documents that governments are outputting. The only sort of areas where there can be fake data is maybe in the analyses or the strategy and documents that AppShare creates. But this is all worked on by professions, right? Um? I think we're. One of the areas where fake data becomes problematic is when it's used to sway the public a certain way, um you know, especially when that's not factual. Um, so that's that's my read of the the.

Craig Smith:

Yeah, do you work with news organizations?

Pat Utz:

So we have I can't disclose here but we have. Yeah, we're working on partnerships with news like organizations. We're actually in that process right now. I think news and social media is a critical component now to understand, uh, the pulse of government also, because a lot of your local state officials and federal they're tweeting and they're putting things on linkedin, right, and so you may want to know about that tweet because they're talking about the next bill that they're going to change, right.

Craig Smith:

So it's a critical component yeah and uh, I'm just thinking it would be, you know, amazing if you partnered with a news organization. They had, uh, a website that that just gave this kind of analysis of of for the big beautiful bill or anything that comes up. So, because you're right, that's Marxian-American wrestling on people, but the people are grossly uninformed or misinformed in many cases, and you know that's the breeding ground for populism, uh, but if you, if people could actually see what's being done, understand that, uh, it would be a much healthier, uh, constituency I, I cannot agree more.

Pat Utz:

I think that's a. I'm gonna note that down. Um, I think that's fair. Yeah, uh, yeah, could be a good, uh, sort of free marketing for us too, right, while doing some good. I like this, I like this.

Craig Smith:

Where do people find you? Uh, is there any sort of a freemium, uh uh layer that allows people to look at the product before signing up? Is it self-service SaaS or is it? Do you have to engage with a salesperson? All of that?

Pat Utz:

Yeah, so long story short. You can just Google my name, patrick or Pat Uts. I'm actually Argentinian, so some parts of the internet will say Patricio, if you're bold enough to type that in. But just Google me and you can find my LinkedIn and information. But everything about abstract is at abstractus. You can also just Google abstract US. The way that we operate, again given its enterprise and white glove, is um. We have all the information on the website, but we essentially walk through a sort of intake process and scope everything out if people want to learn more.

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