Insig AI (LSE:INSG) — Investor Update

0:00 Good morning, ladies and gentlemen, and welcome to the INSIG AI PLC Investor Update Presentation. Corner of your screen. Simply press Q and A, scroll to the bottom, type your question and press send. The company may not be in a position to answer every question received during the meeting itself. However, the company review all questions submitted today and publish responses where it's appropriate to do so. These will be available via your Investor Meet company dashboard, you'll be notified once they are ready for your review. I'd also like to remind you that this presentation is

0:32 being recorded. Before we begin, we'd like to submit the following poll. I'd now like to hand you over to Steve Pretnell, CEO and Dana Ross, ESG Research Director. Good morning. Good morning. Thank you very much. Welcome to everyone joining the call this morning. I guess just to kick things off, we're going to essentially walk through an update of what we've been up to over the last few months and also do some forward looking in terms of how we see the next six to twelve months play out for INSIG AI. I guess before we get into the company update, there have been two pretty significant news announcements this morning, if you happen to be following us or we're alerted. The first one, which we're incredibly excited about, is the appointment of Colm McVeigh as our new Chief Commercial Officer.

1:26 We've been working with Colm over the last six weeks, really, and he's been looking at us as a business from a growth perspective, looking at the demand that we're seeing in terms of the tools and products that we've developed. And we're incredibly pleased that he's joined us this week. Given Com's background in software sales, in SaaS based product sales and helping companies like ours accelerate from a growth point of view, he's an incredible addition to our team. Certainly, he brings a wealth of experience that intersects incredibly well with the suite of products that we developed and the very high level of technical and domain expertise that we have in the business.

2:12 The second news announcement this morning is really around the delivery of the Carvel Clean CLO product. A few months ago, you might have seen an announcement that we've set up a partnership with Carvel to help them develop a new ESG tool in the space. This is a pretty significant announcement today because this is a major innovation in the credit space. Carvel are really leading the way in terms of the way they're thinking about loan based products, how they can bring a real differential ESG approach to debt and debt investing. And from a company perspective, again, it's a significant milestone for INSIG AI to be a part of the development and the launch of this product.

3:06 So two good announcements today. Certainly well timed for this presentation.

3:16 I'm going start off with our purpose, and I'm going to read it because I think it really does describe where we are and where we're going. INSIG AI is the market leading platform for the aggregation and analysis of ESG company information for better and quicker responsible investment decisions and portfolio optimization. There is a little bit of history here. And as a company, we have a very strong core set of skills in cloud computing, in data acquisition, data ingestion, data transformation. And I mean, we've really seen a massive demand for this cloud based machine learning data regressions skill set.

4:02 But more than that, the integration of ESG into our business proposition has really helped differentiate our product offering to the marketplace. And certainly, we speak to investment managers and now more recently corporate clients, we're seeing that the ability to have access to aggregated data in a centralized platform, whereby they can get quick access to information to help make better investment decisions, really is something we're going to hopefully see separating us from competitors in the marketplace.

4:41 Just in terms of the skill sets, and I've touched on this very briefly already, but the combination of technical and domain expertise and data science skill is very important in terms of being able to deliver a scalable, reliable and differential product. Our cloud computing expertise allows us to scale and certainly ingest and store very large amounts of data. More interestingly is the fact that we can do it quickly and we can do it very cost effectively. Given the sheer volume of data that we see in the ESG space based on company disclosures, based on metrics, based on reporting, this part of our business is going to help us expand and grow very quickly and certainly help deliver to the needs that we're seeing coming from our clients.

5:34 The application of machine learning to data is really to help make sense of large sets of information. And using different techniques, different data science, and algorithmic techniques. What we're trying to do is help smart investment managers and smart investors get access to core information, trends, themes, patterns that might be quite difficult to do on a manual basis and certainly wouldn't be able to do in something like Excel. In terms of the expertise or the domain expertise, this is a really, really important pillar to the business is that in order to develop a differential product, you have to have strong internal domain expertise.

6:22 And Diana's team have a rich background in ESG, ESG research, ESG disclosures, ESG analytics. And when you combine all of those three together, we think that you get something quite special.

6:39 Just to walk through the process, and I think I've had a few questions over the past few months from different investors and from clients asking, what is it that you guys do? You know, AI and machine learning sounds all exciting and very interesting. It also sounds a bit daunting. Try and distill that into something relatively straightforward. And I think this does explain exactly what we do. We have to source information. We source public and private company published documents. So this is information that we can either download from the web, whether it's a PDF, Word document, HTML format. This is information we could get from third party data providers like Capital IQ or Bloomberg or FactSet, and that's really what we would call step one. Step one is let's go and find the data.

7:29 Once you found the data, you have to transform it. Now this is a very important process. The transformation is to get it out of what we would call an unstructured format into a machine readable format. The value of it being in a machine readable format is that you automatically get the ability to scale. You get a standardized format for machines to be able to make sense of the information. And whether this text based information or financial metrics, the transformation process is is very key. Both the sourcing and the transformation part of what we do, we've developed a framework to do this. So we have a data framework that allows us to source, ingest, and transform information really, really quickly and then store it in a centralized, accessible database.

8:17 The optimization piece is really when we get into the machine learning part of of making sense of the data. In in the financial metrics space, this could be the application of various machine learning models to see if there are correlations, patterns, trends, or any predictability in data. And then on the text side of the data analytics, this is where we would apply natural language processing algorithms to the text data to make more sense of vast quantities of information. Part of the demo that we will show you today is suite of 15 machine learning classifiers we've developed to interrogate ESG disclosures in annual reports, sustainability reports.

9:04 And again, this optimization piece is really where the machine learning or the AI component comes into its own. If we simply stopped there and went out to the marketplace and said, we're really clever at getting data, we're really good at transforming it, and we're great at optimizing it, we wouldn't have any clients because the next part of it is where the domain expertise of your investor needs to be able to visualize and interact and interrogate the data. So we've built a set of tools that allow our client base to get the benefit of all of the work we've done, I guess, on the back end without needing to be a data scientist or without needing to be a specialist in data wrangling and transformation.

9:50 So the visualization and then and the enablement to drill down and interrogate and validate, we think is an incredibly important part of the process. So essentially the delivery of our toolkit is accessible information underpinned by what we think is pretty sophisticated technology. One of the questions I've had fairly recently is is, ESG? You know, you know, you're a machine learning data company. You're focused on the asset management industry. You have a suite of analytic tools, portfolio optimization tools and extraction tools why ESG?

10:28 I think you only need to look into any of the latest publications around the stats in the marketplace today. The sheer demand and volume that is occurring in the ESG market space is is massively overwhelming. When we set up our ESG division, it's just over a year ago now, we we had a strong feeling that there was going to be an increased demand in the space, which is why we started to build out our team. It's why Diana came on board with her expertise to help build out the methodology for interrogating data. We never expected the demand to be this big. You know, if we simply look at the market data growth in this area over the course of just this year, the market data size is gonna approach in excess of $1,000,000,000 60% of ESG data spending is driven by Europe.

11:24 The fact that we're a UK centered business means we're very well positioned to meet the demands of both European and UK based businesses. And as we've had a strong focus on asset managers, it's also fortunate for us that they are the heaviest spenders. So they spend almost 60% of the spend is coming from the asset management industry for ESG related market data. Another good reason for us to be in this area is if we simply look at the sheer size of assets under management. ESG assets will potentially hit $53,000,000,000,000 by 2025. Part of our thought process and strategy and growth strategy is we know that the sheer volume of data that is going to be published and disclosed by companies in relation to ESG metrics is going to grow exponentially.

12:21 There are not currently a vast suite of tools or services available for companies to be able to easily interrogate and understand it. Also, fact that the ESG landscape is still moving, So frameworks are changing, ways in which companies are being analyzed, scored, and regulated are changing. So our goal is to make sure that the tools that we provide help investors and corporates navigate this this this tricky but very important market space.

12:55 If we look at just the number of funds launched by theme this year, three thirty ESG funds have been launched up to Q3 this year. Equities are still being dominant in terms of the fund based launches. And credit is growing, representing 20% of new ESG funds. I can quite proudly say we're a very small part of that 20%, which is the partnership they have with Carvel, helping them launch their their CLO fund. And, you know, we certainly see ourselves participating in more fund launches for for for various partners and to help them take advantage of the toolkit that we've developed and to help them put into the marketplace very viable investable ESG products.

13:44 If I pause for a second and I guess well, first of all, take the sip of water. And if I try and encapsulate the entire business that we have and what we see our product vision to be, you know, in the first instance, if we just look at the dataset, I mentioned right up front that we specialize in sourcing and transforming data. So we have a we have a a data target by the middle of of next year to have acquired 13,000 public and private company data disclosures. This relates to annual reports, sustainability reports, earnings call transcripts,

14:26 and any other publication that they have made in relation to their ESG strategies. Where we see this being significant in in, again, this this 13,000 number is that these are the 13,000 biggest, most investable companies in the world. We know that there are a number of of ESG providers that that that have tens of thousands or thirties, forty, fifty thousand companies that they cover. Our focus really is on both public and private companies where the vast amount of investment capital is gonna go towards.

15:01 And and in acquiring and collecting that data, it's making it easily and centrally available in our in our database. And, again, we kind of think of it as creating a a Google type search engine for ESG based data. Currently, we have acquired around just under 2,000 companies, both in the public and on private side, and our target is to grow this over the course of the next six months. From a markets perspective, we've always been heavily focused on the asset management industry, certainly helping from an investment portfolio optimization perspective. And the shift really is for us is around ESG investing, and there's two parts to that. One is how do you make a sound, well researched, data driven, evidence based investment decision?

15:57 Hopefully, through the tools that we have developed, we can help investment managers and researchers understand what companies are doing from an ESG perspective and that when they make a decision to invest, they have the level of disclosure and evidence that they need to stand behind that investment. I guess that's the the research part of of of what we're offering or the research tool. Then from a fund optimization point of view is once you have made an investment, how is your portfolio performing, and what impact do ESG metrics have?

16:32 So our INSIG portfolio tool, we have ESG based metrics incorporated with financial metrics to allow investment managers to see what is the impact of ESG based portfolios, to try and interrogate and understand changes year on year, whether that's scope one or two, whether it's GHG emissions, whether it's usage of water and waste, and it's really to provide an interaction to where we would say financial and ESG based metrics. Something that's been a coincidental but a very, I guess, a good upside for us is that the exact research tool that we've developed, which we were originally targeting only at the asset management industry, also has a very large appeal to corporate companies.

17:22 Reason being is that companies are required to publish what they're doing in the ESG space. They're also being measured and scored year on year based on what targets they've set, what they've said that they're gonna do, and the interrogation around that and the regulation around that is is is becoming tighter and tighter. So the demand for our ESG research tool we are seeing is filling two markets, which is the corporate business market as well as the investment manager market.

17:52 In terms of the data platform and the data framework that we've developed, this ability to ingest, transform and store information, we see an opportunity down the line to also go into other industry verticals. If we think of data not so much in terms of ESG or or financial market data, if we simply think of data in terms of rows and columns, whether it's financial data or whether it's biochemical data, whether it's clinical trial data, it still needs to be ingested and transformed and stored. And we see a real opportunity for our data framework to have applications in other vertical markets as well. I guess finally, just looking at the document types, again, you know, whether it's published research, whether it's legal findings, whether it's clinical trials, whether it's patent filings, any numeric and or text based data, you know, we are in a position to to bring in and make sense of. And then given our expertise in data science is use sophisticated AI techniques to to really kind of bring the data to life and and put it in the hands of of smart investors.

19:06 Through this, I guess, product vision, ESG certainly is at our core. Having a complete and up to date document set is absolutely critical. Companies publish content all the time. Part of our goal is to make sure that, you know, within weeks of any new publication, that information is in our database, which then allows investors to make timely and informed decisions, you know, using our techniques and the the visualization and optimization techniques that's really to make the data accessible and easy to use. You know, we will be delivering our ESG research tool through a software as a service platform. Given the nature of our skill set, we we are getting a lot of demand for bespoke ESG solutions. And I think that, you know, our relationship with CarVal is a good example of that where they have a specific methodology that's very unique to them. They have proprietary datasets that's unique to them, and we can apply our skill sets to help them develop and grow.

20:16 I guess we pull all of this together into what we might call an integrated product platform. You know, we have a research capability, we have an analytics capability, and we have a data framework capability. These three underpinning pieces of technology really, I guess, we think will set us apart from from from competitors out there in the market, but, you know, which would also help us once we get into a client whether they initially have a research demand. The the upsell then is to, you know, what they can now do with the data from an analytics perspective. And then from a data framework point of view, the expansion or the growth of datasets is is just going to increase. So having the ability to help companies ingest more data and make more sense of their own data, again, we think is going to be quite differential.

21:10 At that point, I have an opportunity to to to take a breath. I'm gonna hand over to Diana Rose, who's head of ESG. She's gonna walk you through, I guess, hot off the press, the latest release of our ESG research tool. And on that point, I'll hand over to Diana. And I'm just gonna share my screen. Thank you, Steve, and good morning, everyone. So I will take you through a really brief demonstration of the ESG research tool, which we launched this week as version one, and it is being iterated already very quickly in response to client feedback.

21:58 The purpose of the tool is to support evidence based decision making in ESG assessment and research. So it's designed to be very simple to use, very transparent, and and intuitive, and enable the the user to drill right down into disclosures. So there are different pathways through the app, but if you think of the information contained in it, like the features on a map, we're gonna start with a helicopter view and then land on a company and go and explore the disclosures from them. The universe page here offers a high level overview of the companies within the app. So we're looking at a demonstration portfolio here, which contains companies from the S and P five hundred and some from the FTSE three fifty.

22:49 So on this page, the size of the bubbles indicates the number of companies within that sector, and the height indicates how many documents are available in total. Clicking on a bubble takes you through to the peer group page. And on this page, you can see how companies compare to others in their sector, in their peer group. Displayed here for each company is the average number of sentences in the company's documents that our models, our NLP models, have scored as relevant to the 15 ESG issue classifiers that we've developed.

23:24 And those 15 issues aggregate up here into their pillars of environment, society, governance, and our own pillar that we've developed called foundations, which captures disclosures that relate to reputation reputation and best practice in ESG reporting. So filtering by by each of these can give you a sense of who's leading and who's lagging in that peer group. If I click on a company, it takes me to the company metrics page. This is the dashboard for a single company, and it's updated regularly as soon as documents become available and are scored through the machines. It's acts like a snapshot on a company's ESG reporting profile with signposts of trends and interesting points that you might want to explore a bit further to focus your analysis.

24:22 Documents available section displays everything that the company has published to date, and this can help highlight gaps as well as act as a signpost to what's available for to for drilling down into. The ESG sentences section is a snapshot of the volume of disclosures relating to these these pillars in their most recent report. And the direction of travel shows the trend in the volume of disclosures over time since 2015, and the blue bar is the company, and the green line is the industry. A quick point to add here is, again, we spoke about this idea of visualizing data. Any individual could go onto the Syntema website.

25:09 You could download each of those annual reports. You could download each sustainability report. But the idea of aggregating the information without even reading a single document, you already get a sense of direction. You already get a feeling for what they're doing. We can see that they're they're serious about ESG. They've they've been publishing ESG based reports for the last almost six years. We can also see that they've been above the industry average and in certainly in the last year are leading in that sense. Interestingly, if we look at US based companies, they certainly are behind the curve when it comes to the level of ESG based disclosures as compared to Europe and UK companies. But, the aggregation of information in the visual way allows, we think, an investment manager or a researcher to get a feeling for what they're digging into before they have to go into it at at a very low level.

26:04 Thank you, Steve. Clicking on one of those ticks on the come documents available on the company metrics page takes me to the document drill down. This feature in the app is essentially the doorway into the database of all the documents equipped with the ESG expert trained classifiers to help navigate to relevant disclosures and support the user in in interrogating the documents efficiently and and targeted. So the 15 machine learning classifiers have gone through training to the point that they're capable of filtering very reliably a sentence that is relevant to an issue out of the whole the whole group of sentences in the document that wouldn't be relevant.

26:54 So this means that in a document of 2,600 sentences, you can start to focus in and narrow down your search to a much smaller set. So here, I filtered by the climate change issue, and I've got a 115 sentences to to to interrogate. This massively reduces the challenge of sifting through a report of that size, let alone seven years of reports of that size, and then all the the other reports that are that might contain disclosures. And you can see here the other the other 15 are all contained here to select from. Combined with the search facility here, you can take quite targeted approach to surfacing disclosures that might be interesting and relevant to your analysis.

27:57 Different people in the space use different frameworks that they prefer. The PCFD doesn't get a mention in the annual report, but we could look at the sustainability report, maybe the 2021. And then we've got something interesting and relevant. So depending on your lens of analysis and research, there are there are lots of different ways that that you can interrogate the company. Taking it a step further, we've developed a document comparison, which is essentially a parallel document drill down. This was in response specifically to a client piece of client feedback.

28:47 And his point was about the the challenge of greenwashing and trying to hold companies to account year on year in their disclosures and to identify gaps and and whether targets are being met and held held companies held to account. So this makes the task of scrutinizing for inconsistency and repetition much more manageable. And equipped with this sort of granular detail, a user is in a much better position to be able to inform themselves, which is essential if if on investor journey towards best practice in ESG, you want to go towards engagement with the company itself. Well, one quick example is and I mentioned this around US based companies and their level of disclosure.

29:40 So if you take somebody like Adobe, sort of very large US based entity. If we look at their 10 k filing year on year so quite interestingly, I mean, here we've set it to climate change as their disclosures. And very quickly, we can see that somebody's done an incredibly do good copy and paste job for each of their subsequent annual reports. Now this is not good or bad. This is simply we can see evidence of of what they've said around climate change within their 10 k document. Now they may have a sustainability report. They may have a sustainability strategy or an environmental strategy, which is fine. But at least we know that if we go into their annual report or their 10 k, we're not going to find a lot of information around what they're doing. And certainly year on year to kind of measure whether or not there is a strategy, this is not the place to find it.

30:39 So this, I wouldn't say, is a criticism of Adobe. This is simply us being able to say very quickly that the 10 k is not the place in which they're really doing any kind of ESG based disclosures. And and as Diana said, we're, you know, expanding the documents here to include all available content that a company would publish. But I think this is really a nice example of how, you know, certainly from an investor perspective, questions could be asked of, you know, the CEOs of various companies to say, what is your strategy? How is it that there is no change year on year? Where is the evidence that you're doing something? Amazon is actually another interesting one where they have very, very little ESG based disclosure in in any of their annual reports. And then given the size of them as a business, given their dependence on very large data infrastructures, server farms, their usage of of natural resources, water for for cooling, you would expect to see a lot more from them given the size of that business.

31:44 And finally, the search capability enables quite a different lens on the dataset. Underpinning this is quite a powerful feature called an elastic search capability, which can surface disclosures across the entire database of documents. There are ways to to kind of construct that, but you can also just see let's use TCFD again, the task force for climate related financial disclosures, which is growing as one of the leading frameworks for ESG. And it's it's an emerging trend, essentially. It can be used in lots of different ways, but you can spot quite interesting patterns just by the sheer scale of the data in in the database.

32:39 And as as issues emerge in ESG and they come to the awareness that sort of the front of people's minds, we call it sort of dynamic materiality. This is quite an interesting tool for people to to see how companies have anticipated those those trends and are responding to them. So where we see the value in this kind of search capability is that, you know, ESG is synonymous with with lots of acronyms, and this is only going to expand and grow over time. But interestingly, you know, in this particular dataset, it's about a thousand companies. The earliest reference to TCFD was 2018. Ecolab made reference to it. You know, we can go and then interrogate and see what they've said.

33:29 But also if we look at a company like Etsy, you know, this is an online consumer website. They're, I think, quite in tune with their audience And they are certainly making quite a big statement in terms of the number of references, what their strategy is, how they're doing it, where they can find more information around how they're trying to build their ESG their ESG business. So, again, just having an ability to drill across everything can allow, I guess, whether it's a corporate that's doing comparisons to other companies in its industry or from an investor perspective to see, hey. Who's best in class or who who's who's not there at all? Because sometimes no disclosure is as telling as a lot of disclosure.

34:17 That then concludes the demo, and I think we'll just switch off screen sharing. And we'll go on to some well, we'll wait for yeah. Gary. Steve, thank you very much indeed for your presentation. Ladies and gentlemen, do please continue to submit your questions using the q and a tab situated in the right hand corner of your screen. Just while the company take a few moments to review those investor questions submitted already, I'd like to remind you that recording of this presentation along with a copy of the slides and the published q and a could be accessed via investor dashboard on the investment company platform. I'd also like to remind you that your feedback is important to the company. And immediately after the presentation has ended, you'll be redirected for the opportunity to provide feedback in order the company can better understand your views and expectations.

35:06 Perhaps before we just move into the live q and I did have one pre submitted question, which reads as follows. Could you kindly verify how many sales you've made and what is your pipeline? Great. So thank you for the first question. One of the challenges we have with working with asset managers is the confidentiality we have to sign. There's unfortunately, there's not a huge amount I can say in terms of the the the number of sales that we made, but I can talk about the pipeline. And if I think of just this last month in October, we saw 15 new clients. And of those 15 clients, eight have already converted into proposals. And then from a proposal perspective, we now then need to sign them up. And, again, this is the the work we've been doing over the past the past year in developing each of these product platforms and and product solutions has now put us in a position to have these active sales and commercial meetings.

36:05 So, you know, the the I'd say that 40% of the meetings we have are inbound. People are reaching out to us. It's a very specific space in ESG, so what we're doing is interesting to both corporates and investors. And then the balance of our our client meetings are us reaching out to to clients directly. So we certainly see a a a big increase in terms of the number of sales coming through the door. The addition of Cole McVeigh as our Chief Commercial Officer also underpins the demand we're seeing. We see a massive growth potential. We need experience to help us navigate this market. And certainly, his experience is going to help us build this pipeline really quickly.

36:54 That's fantastic, Steve. Thank you, indeed. Obviously, investors have submitted a number of questions during the presentation today. If I could just hand back to you just to click on that q and a tab and where appropriate to do so, read out the question and give you a response, please. Great. So we have a question here from Josh M. What is the length of a typical sales cycle now? Think I that's a good question for a couple of reasons. If look back a year ago when we were selling what I would say a sophisticated portfolio optimization and analytics tool that includes models like k nearest neighbors, random forest, bridge regression. It's a slower sales cycle because not only are you selling the tool, but you're also selling and helping people understand what these sophisticated models are doing.

37:42 On the ESG side of the business, from the time in which we speak to somebody to the time at which we put a proposal in front of them, that's around three weeks. And and, again, the ESG research tool is is relatively new as in in the last six weeks. We've been putting it in front of clients. So at this stage, we're seeing a sales cycle of of around four weeks. As we, I guess, build our commercial as we build our commercial, I guess, execution strategy, we would hope to bring that down to to less than to less than four weeks. But currently, we're seeing it around a four week cycle from initial meeting to to proposal and then, you know, looking at closing.

38:35 Also, Josh m, out of the 11 warm leads in the admission document, what percentage have converted? 40% have converted. And, again, we are somewhat limited in terms of what I can disclose. But part of our and if we look back to the admission document in April, May this year, the strategy at that stage was a five product strategy. We've refined that really into a two product strategy, which is the ESG research tool and the portfolio optimization tool with a strong ESG underpinning. And, you know, we really just see us pushing that agenda. And so products like Exelatin and and and and INSIG data, we've we've come put on the shelf for the moment because we think we're going to make a lot more money selling the the ESG based products.

39:37 When are the next set of financial results due? This will be at the end of the year. We will publish our interim financials. Thank you, David h. Let's see. Sorry. There's a lot couple of longer ones. I just have to read them first. So a question from Marco. I think it's related to the sales cycles. I'll read out the question. Before new technology can be introduced, the people and processes must be in place to optimally use it, which in the case of data will involve the data maturity assessment of what the actions need to be taken to address any cultural skills, governance, and or quality issues alongside the tools and architectural changes.

40:32 How much of a consultancy lead time have you allowed for this maturity to take place with new customers before you're in a position to sell them new licenses? That's a really, really excellent question, and there's two answers to that. So the the first answer is in the context of companies that are behind the curve and investment managers that are behind the curve in terms of interrogating and understanding the ESG landscape, in terms of how they evaluate the companies that can invest, The the lead time is is a lot less because they don't have anything. So the fact that they can have centralized access to company based data that doesn't require manual downloading, that doesn't require, you know, huge amount of human time spent reading through PDF documents, that part of the process is quicker because we have something that is immediately useful.

41:33 Where we have relationships with what I would call more sophisticated ESG investors that have been doing ESG investing for a longer time, there is a bigger demand and that is longer because they have more specific requirements around the types of metrics that they want to integrate into their analysis. So in that context, we would be, I guess, integrating, you know, third party ESG metrics and scores in addition to their own proprietary scores and data on top of ours. So the concept of us doing bespoke ESG solutions really services that second example that I've given. But there are a lot of companies that that need help now and that are happy to take what we have as a basis.

42:23 So I think we kind of see this two two halves of the same same apple. Let's see. Could you give could we give insight into the competitive landscape where INSIG AI sits? I know a Swiss company called RePres perform similar ESG database services. Thank you, James g. Yes. So it would be very naive to assume that we were the only the only player in the space. Yes. ESG, obviously, is a big area. Looking at the stats that are that that are that I referenced earlier, there are a lot of players in the space. RepRisk is certainly a, you know, big company that provides certain amount of analytics and database access.

43:13 INSIG AI, again, another one. So does Thomson Reuters. So does Bloomberg. I think where we differentiate ourselves is that at a at a at an individual company level, we can help them with this specific portfolio. So as opposed to having access to a all singing, all dancing platform, but that doesn't necessarily meet exactly what your requirements are, That's, I guess, where we sit and we can help. The other part is the integration of third party datasets. So we're actually working with a client at the moment where they use rep risk. They also use ISS data, and they have an MSCI feed. So they have multiple third party feeds. The transformation pieces where we can help them, where we wanna integrate and take out segments of each of those datasets, transform them, and apply their methodology to the data and then make it available to them.

44:10 In terms of where we do fundamentally believe we are are different is the development of our 15 machine learning classifiers. We've used a machine learning model called BERT. It's a Google developed model that was trained on 1,500,000,000 sentences. They used Wikipedia as a training set. So using a BERT based NLP model means that our classifiers can understand English in context. This is a very, I guess, differential piece of technology. The reason we can do it is that we have in house expertise to scale and run very computationally heavy algorithms.

45:01 This is not something the average company can do. Big companies like Google can do it. Facebook can do it. IBM certainly can do it, but your average data science team will not have the computational power or expertise to build 15 BERT based classifiers. So that is an example of where we think we will be differential in the space to make sense of of datasets. Josh m has another question. Just so let's wait six months and see what the geographic revenue split is. You know, obviously, with our relationship with Carvell, that's a a US based client. We have a we have two other US based clients. I mean, we interestingly, on the ESG side of the equation, the The UK and the European market is is ahead of the curve, or certainly they are leading the the the ESG revolution.

46:02 So I think we're gonna see a larger proportion of ESG revenues for our ESG tools coming out of UK and Europe ahead of the The US based players. And then I think from a bespoke solutions perspective, I think our US based clients will probably dominate from that point of view. And and those US based clients are what I would call the innovators in their areas. They're really trying to use the latest technologies to differentiate themselves from from their competitors. So I would say that from an ESG research tool perspective, The UK and Europe will be where we see most of our revenue certainly for the next probably year. And then on a bespoke solutions basis, I think The US might be where we see most of our our income coming from.

47:01 So I think David h has a question. You mentioned a lot of companies contact you regularly through products. What rough percentage of sales go to companies you approach as opposed to companies that approach you? Let's see. I mean, the our sales strategy, I guess, as a small business and also as a as a as a historical consulting business has been word-of-mouth or has been referral based. At the moment, it's still, I would say, 70% of our sales come through introductions that we know, people that we know. But what I've been incredibly encouraged by is, you know, the the blogs that we've been publishing, the content we've been putting out, the the demos we do of the products is getting a lot more interest.

47:52 And so I think that that shift we'll start to see will change. But, again, also with the appointment of Colm, our strategy is to do a lot more aggressive marketing and targeted marketing around what we're doing. So I think that that split will will change quite quickly over the next quarter. Steve, I think you've pretty much covered all those questions you can from investors. And, of course, if there are any further questions that do come through, you will have the ability to respond to those and publish those responses where appropriate to do so via the investor me company platform. So on that basis, if I could just ask you for a closing snapshot before we redirect the investors to give you some feedback, please. Great. Thank you. Well, first of all, thank you all for your time today.

48:39 I think for us, we are incredibly excited about the space that we find ourselves in. You know, we have a team of 27 highly talented, highly motivated people that are working with what we think is cutting edge technology in a hugely exciting space. And, you know, being a part of of of of a real investment revolution and being able to to add to how people are going to think about, invest in, and interrogate company based data, I think, really bodes well for us as a business. We're super excited about the future. And, yeah, I look forward to doing this update again probably in the next quarter to start to share the successes that we're making based on our current growth strategy.

49:31 That's fantastic, Steve. Diana, thank you again for updating investors there and for the demonstration. Can I please ask investors not to close this session? You'll be automatically redirected for the opportunity to provide your feedback in order that management can better understand your views and expectations. This will only take a few moments to complete and is greatly valued by the company. On behalf of the management team of INSIG AI PLC, we'd like to thank you very much for attending today's presentation. That concludes today's session. Thank you, and good morning.