Startups WithAI: Transforming Data into Decisions: The Senis AI Approach
Senis AI is revolutionising the way business leaders approach data analytics and strategic planning.
Founded by Marco Calabresi and Hugh Halford Thompson, the company is focused on bridging the gap between data availability and organisational capability to utilise that data effectively.
They recognised that many organisations, particularly in the membership sector, struggle with data analytics due to a lack of in-house expertise and many leaders rely on intuition rather than data-driven insights, which can hinder strategic decision-making.
Senis AI’s platform aims to empower leaders by providing a conversational AI that helps analyse data and offers strategic guidance.
This episode explores the founders' backgrounds, the inspiration behind Senis AI, and the unique features of their platform that make data analytics accessible to all team members, regardless of their technical expertise.
Through their innovative approach, Senis AI is positioned to transform the member management space with AI-driven insights, ultimately aiming to become a trusted strategic advisor for organizations.
Takeaways:
- Senis AI transforms complex data into clear strategic direction for business leaders.
- The platform simplifies planning by providing an AI advisor for data analysis and guidance.
- Many organisations struggle with data analytics due to lack of in-house expertise.
- AI lowers the barrier to data access, making analytics more accessible to everyone.
- Senis AI's model includes long-term partnerships with clients for ongoing support.
- The company aims to be the world's most trusted AI strategic advisor for organisations.
Find out more at senis.ai
Transcript
I'm Marco Calabrese Co-founder and CEO At Senis AI, we give business leaders confidence and clarity about their future by transforming complex data into clear strategic direction.
Our AI platform makes planning as simple as having a natural conversation, giving you a strategic advisor that analyses your information and guides your decision-making. You're
Voiceover:You're listening to WithAI FM.
David Brown:Hello, welcome to Startups WithAI. I'm your host, David, and on today's show, we're speaking with Hugh Halford-Thompson and Marco Calabrese.
They're the founders of Senis AI, and they're doing fascinating work in the association member management space using AI. And so, guys, welcome to the show.
Marco Calabrese:Thank you very much for having us.
Hugh Halford-Thompson:David, thank you.
David Brown:Awesome. This is like the first thing after the holidays as well. So we're all just getting our feet back under our desks after New Year's and everything else.
So, thanks for doing this so soon after the holiday started or, sorry, ended. Can you just start off by sort of telling everybody like how did you get this idea and how.
What's in your background that sort of helped you launch Senis AI?
Marco Calabrese:Sure. Well, so I'm an entrepreneur at heart. I've started multiple businesses and business ventures over the years.
My professional background has been in fintech over the last 13 years as a business analyst and product manager, and we founded Senis AI about a year and a half ago to empower business leaders with AI data-driven clarity about their future. And we've identified a really enormous gap between data availability and leaders' ability to use that data effectively.
So, I'll pass you over to my co-founder, Hugh.
Hugh Halford-Thompson:Hey. Hi everyone, I'm Hugh Halford-Thompson. I'm a serial entrepreneur. I've got several exits both privately and to public markets.
My background is really in technology and product and building out companies. We started Senis, as Marco said, just over a year ago, where we saw a niche where really we found AI could help people with better data analytics.
David Brown:How are membership organisations handling this sort of thing at the minute?
Marco Calabrese:Yeah, so they're either doing it manually or they're not doing it at all. And that's the major problem that we've seen.
We've spoken to over 100 associations, and we've spotted some really clear patterns and challenges across these organisations.
And the main one is that most organisations are, they don't have the skill set in-house or if they do for data analytics, that skill set is very much a bottleneck within the organisation. So that tends to lead to leaders having to rely heavily on intuition and gut feel, as opposed to data, in order to build out their strategic roadmap.
David Brown:So, Hugh, you're the CTO, right?
Hugh Halford-Thompson:Yes.
David Brown:So, maybe you can start off by walking us through how the solution works. Like what does it do? Like what's its USP? Why is it better than other tools?
Hugh Halford-Thompson:Yeah, absolutely. So, we actually use AI in various places.
Our platform is about bringing data analytics to the organisation, making it much more accessible to everybody in that organisation. And the way we do that, we use AI both to integrate the data in.
Integrating with systems can be traditionally, it's very painful, but AI is very good at data mapping, very good at writing code to ingest the data. So we use that to put it into our data lake and then we build out a dashboard providing clients with all their data in one place.
And then we've got an AI data consultant that understands that data. You can just have a natural conversation with it. It helps put together strategies, and it makes analytics really accessible to everyone.
Marco Calabrese:Another exciting feature that's currently in the roadmap is the AI-driven dashboards. So maybe you can talk to me.
Hugh Halford-Thompson:Yeah.
So, at the moment, you can have a dashboard that's really simple, like the dashboards in any advert you see where you got a few little charts and everyone understands it, but it doesn't really mean anything. There's very little there or where you do proper data analytics, you have hundreds of charts, you've got, you know, some data.
People who've put this together, they're the only ones who understand it. No one else can really access it properly. And that's fine in big banks where they can hire a whole team who really get this stuff.
But in smaller organisations it makes it really inaccessible. What we want to do is bridge that gap, and we're making it.
Firstly, the AI can already understand the charts of data and explain it to people, help them together, strategies.
But also, what we're going to do is have it so that when you talk to the AI, if you ask me a particular question about your sponsors but you're currently looking at a different dashboard, I would take you to that page first.
I would maybe pull in a couple of other charts that are relevant to your question and put this dashboard together and say, okay, this is the answer, and this is the data and how you get to it. So I think that's a really powerful feature that we're bringing out.
David Brown:Interesting. Yeah. I used to do a lot of work in data analytics, around marketing data, digital analytics, web analytics, that kind of thing.
And I can't tell you how many times we went to a client might be someone like, I don't know, like a Lad Brooks or one of the gaming companies or something like that, and they would have, you know, we went in and trained, I think it, I'm calling out Ladbroke, so sorry, Ladbrokes. But we went in and we trained like 100 people on how to use the reporting system and where all the reports were and all that stuff.
And then we started looking at the metrics afterwards, and there were two people that only that ever ran a report, and they only ran about three reports out of the hundred that they had me create.
And so I can totally see where AI would be a massive advantage because then those people who don't really understand the reports or just need to dip into it, you know, because I think a lot of people only need to dip in every once in a while and you know, they just want to go in and ask a question and they just want somebody to answer their question. And I think AI would be perfect for that. Is that, is it the AI element that really gives you guys the competitive advantage?
Marco Calabrese:Yeah, that's absolutely right.
And it really does lower that barrier to entry, you know, between the moment where you have a specific question either about the organisation or the direction in which you're going and the answer to that question that's embedded within the data. So that's the sort of the key point there, of course.
David Brown:So what's your business model? How does the business model work?
Marco Calabrese:So, our business model is that we're looking for. We really have sort of long term partnerships where we include sort of full strategic support.
So, we firstly charge an onboarding fee and a subscription model. So that's sort of standard for a SaaS business.
But what we found is that given that AI and the use of AI for data analytics is very much a novel concept, we've wrapped that SaaS model with a lot of support and consulting as well, where we have a real sort of vested interest in the success of not just the use of the platform, but the attainment and the implementation of the strategies that our platform produces for our clients as well.
David Brown:So what chose you to sort of go with the subscription model? Was there a particular reason or did you get that feedback from customers or how did you arrive at that?
Marco Calabrese:It's certainly what customers in our market are accustomed to. It makes sense. And again, it's about creating that long-term relationship. So, we originally started off by running a pilot, which was very successful and our bridge between this pilot and the existing recurring revenue model was running one-off projects where they were very tightly scoped, but they added value very quickly. And that was very much a stepping stone for us to be able to build up that confidence both in ourselves and in our clients.
And we've now started bringing clients on board on a recurring revenue model.
Hugh Halford-Thompson:I think it's important that because it's such a new concept. Yeah, they don't realise the full value they're going to get, and this could be long-term. I think people would pay a lot more for this sort of thing.
But we will need to make it as accessible as possible and give an easy onboarding ramp where people can sign up, they can get going and they can try it out. When they try it, they absolutely love it. We haven't had, I think, anyone who's used it and not liked it.
David Brown:So just to stick on this just for a second. So, have you built your own AI, or are you using another company? Like, are you using an API into a different AI tool that you use, or how does that work in the background?
Hugh Halford-Thompson:Yeah, so building your own AI, at least for an LLM, that's a very expensive endeavour. If you want to get a good one. For us, that market, the big guys can fight it out. In our tool, we currently use GPT 4.O. We're experimenting with 1.0 with their new one, and actually, we started using CLAUDE more and more ourselves.
Ultimately, there are several that are good enough, so we're using that, and then the way the platform works, we're then wrapping those AIs, those LLMs, with various tools, various workflows. So when you ask a question, it might not only ask one AI; it might ask several and pull things together.
David Brown:Okay, that's cool, that's interesting, and it's probably worth highlighting and tell me if I'm wrong, but from my understanding is that if you're using the API into any of the tools, then whatever you feed through the API does not go into the training data set, is that right?
Hugh Halford-Thompson:It is. And that is, I mean, that's fundamental, right? So, anyone who trains on client data, their business is going to die.
And ultimately we've signed all the agreements through The API for OpenAI, for Claude. I assume they have the same thing. I haven't personally looked at it, but I'm sure they'll have the same thing.
Every single thing that goes through the business API will never be trained on. And if you think about their model, they're not going to make money from us. Making poems or whatever for people.
They're going to make money from big organisations putting in sensitive data and really understanding their needs, and that's across the board. So they've got a big incentive to keep that private.
David Brown:And is there some sort of... Sorry, I'm digging into this a little bit. I'm just thinking about this. So. And I assume there's a cost to that as well. So, that's got to be built into the business model also. And there is...
Hugh Halford-Thompson:And that, that's something that I think, like at the moment with our current clients, like ultimately calling an AI, there are expensive ones. It sounds like a coming. But calling GPT4O is pretty cheap per call at the moment. Most of our customers are in the sort of 2 to 30 mil range.
They will have, you know, they might have a team of 10, 15, maybe 20 or 50.
Marco Calabrese:Yeah.
So I mean, the key thing here is that certainly, in the starting phase, we will take on the costs, and we'll essentially bake in the costs of the API calls into the subscription. If as we progress, we find that there's scope to potentially, you know, cap the number of calls and usage, then we'll look at that.
I mean our objective really at the moment is to make sure that we get a lot of usage on our platform. So the more the merrier.
David Brown:Yeah, no, that's, that's fair enough. And I guess, ultimately, you can just put some banded pricing in there.
If you've got somebody who's, you know, doing, you know, 50% more than someone else, then that might have some impact. But then you just go, okay, we're just going to do bands.
And then you just say, look, if it's between this, it's that and blah, blah, blah, and then you can just do it.
Hugh Halford-Thompson:I mean, at the moment it's not really come up with customers because it's actually, it's quite a small cost base for us. But as we go up to.
Yeah, if we land any clients who have say 500 users who are going to use it daily, that's the point where we need to start having that conversation.
David Brown:Well, speaking about those big customers. So, let's talk about market now. So, how big is the market opportunity here and what's the most exciting thing about it?
Hugh Halford-Thompson:Our go-to market? It really starts with associations.
So these are membership organisations that run, they've got memberships, they run events, they run certifications, maybe online courses and education if you think of professional organisations like the British Medical Association or the New York Bar Association.
These are probably some of the best-known ones. They've all got similar business models, they've got similar issues and that actually makes it really repeatable for us.
For just the UK and US markets. If we can capture half a per cent, that's about 400 customers and $10 million in recurring revenue.
Associations are gateways to the industries that they represent, and this is a place we can expand into. So, the broader data analytics market is actually worth 300 billion.
And we have our sites already on the events market, which is very tied to the association world. But then, ultimately, in the long run, our platform can work in any industry where they've got common data challenges.
Marco Calabrese:I mean just to, just to echo Hugh's point, I mean our sweet spot at the moment is really in the associations market, particularly those that have an annual revenue of between £2 and £30 million.
So that's a great sweet spot because they're big enough to have enough data and generally issues either in the quality or, you know, the accessibility of that data, but they're also small enough for us to be able to provide value very quickly.
David Brown:And are you focusing on any particular market? Are you looking at, are you going UK first or are you literally going global? Because I know you mentioned a couple of ones in the us.
How are you going to approach the market in that way?
Marco Calabrese:It's US, UK and European markets. So, all three markets currently.
Hugh Halford-Thompson:And we're really what's driving that.
It's a, yeah, it can be a global business, but we have contacts who do a lot of events for the industry in both us, Europe and UK, and I think that will probably expand to a few other regions as time goes on.
David Brown:Okay.
And I know we talked about talking about traction as well, so can you talk a little bit about what traction you have at the minute in terms of traction?
Marco Calabrese:I mean one of the, one of the key milestones for us was early on was the overwhelming demand that we got for our pilot. We initially were thinking of doing a pilot with just five or six associations and we actually ran a pilot with 13.
And so that was great traction very early on, and that helped us to understand our unique customer's needs and also iterate on our products.
And then our initial paid customers, as I mentioned previously, they came on a series of tightly scoped one-off projects, and now we're starting to see customers come in on a recurring revenue basis. We've also got a very healthy pipeline. So again, on the attraction side, that's very positive for us.
We've been working very hard on building out strategic partnerships within the association space, and some of which are very much industry leaders, like memcom, for example. And AI Insights are already being used at the moment within our clients' board meetings as well.
So, they just bring up the platform during the board meetings and ask the AI questions. That really helps to shape those meetings and directions. And so that's the sort of the key milestones that we've hit so far.
David Brown:I think anything you can do to make board meetings easier is a winner. I can't tell you how many times it would have been so helpful. Both of you are experienced, you know the deal.
But you go and sit down, and no matter how much you prepare, they're going to ask you something that you haven't prepared invariably every single time. And it's. I'm sure it's a sport that they do just to amuse themselves.
But no, having something like this, I can see where the, I can see where the value comes to be able to just go, well, let's look and get an answer. So that would be amazing.
Hugh Halford-Thompson:It's really funny. We get customers for the most part. Customers will come in, and they want help with the strategic direction.
They want help for the board for their strategic direction. That's really what the platform's built for.
But we've had a few customers come in and say, look, I know what I need to do, but the board just won't get behind it. I need data to back this up. And then again, for the most part, it does back it up, and it then helps them with that process.
We actually had one who came in and then they realised that they were wrong, but that helps them make the right decisions moving forward. So yeah, we get a bit of a mix.
David Brown:Can it actually write your monthly board report? That's the ultimate.
Hugh Halford-Thompson:I mean, in terms of. Depends on what you're putting in it.
In terms of stats around the company and how you're doing and all that side, like something that we are planning on putting into the platform.
And I haven't thought specifically about board reports yet, but is anything where you're going in on a monthly basis and asking for a report and looking at the data and writing it up, that's something that our AI looks at the data. AI, in general, can write things up really well. We can pull that together.
And then we're also looking at things like smart notifications where you want to be notified more if a concept changes rather than if a particular metric goes a certain way. So There's a lot of that that we can build in.
David Brown:Do you have any targets that you can talk about publicly or would you rather not talk about those? Like, how many new companies are you aiming to get on, and what's your growth like?
And because there is, you know, it'd be interesting to kind of Understand what your KPIs are around your growth and your traction and stuff like that. But I don't know if you if you're open to talking about that or not.
Hugh Halford-Thompson:So our target at the moment is to get 50 clients on this year. And obviously it ramps up as, as we go. I think we've got for Q1, I think we know what the plan is.
As with any startup, Q2 is a long way away, but we've got a good plan to get there. I think ultimately what's going to decide that, though, is the amount of funding that we get in.
Partly from landing larger clients, but then also from investors because with the right funding we can really accelerate this.
David Brown:And is that 50, is that 50 new this year or is that 50 in total by the end of the year?
Hugh Halford-Thompson:50 new.
David Brown:And how many do you have at the minute?
Hugh Halford-Thompson:So we had was. It was 13 went through the pilot, which by the way, I was hoping that one or two would respond. It'd be great to have five or six.
And then they all said yes. So that was really successful. Some of them, we learned a lot.
There were some of them who weren't the right fit just from the amount of data they had, they were too small, they knew all their members, that sort of thing. But out of the ones in the right sort of niche, most of those are then converted to individual projects.
One of them is already on a long-term contract. And then we sent out, bearing in mind we only started the pilots at the end of August.
We sent out proposals before Christmas for I think it was five of them to bring those into longer contracts.
Marco Calabrese:Yeah, we've had lengthy discussions with those five potential new customers, many of which have already we've already worked with on the one-off project.
So, you know, we're hoping for either four or five of them to come on board on a long term basis over the course of the next sort of couple of months.
David Brown:Oh, that's great. Are you working with all private sector companies at the minute or do you have any public sector clients?
Hugh Halford-Thompson:They're all private at the moment. A lot of them are charities or a lot of them are nonprofits. That's the right word for it.
I think that the charity Sector, there is an option there, but most associations in the US certainly are run as nonprofits. I believe it's the same here, but yeah, so far, it's all private.
David Brown:I know you've probably worked a lot of your own personal networks and stuff, and you've talked about using a partner channel to actually help scale the business and stuff as you move forward, but what's kind of your go-to market strategy been and, and how do you plan on really accelerating the growth?
Marco Calabrese:We've. So, in terms of the go-to-market strategy, as you mentioned, a key, a key area of growth is strategic partnerships.
So by tapping into their customer base, that's going to really sort of accelerate our growth in terms of investment.
We're really looking to grow our sales and marketing team primarily sort of as a starting point and as a second point, grow our development arm as well.
Hugh Halford-Thompson:I'd say our leads come from primarily two places. One is events which are run. There's some industry events where all our potential customers are in the right room. They're fantastic.
We get a lot of referrals and introductions. They are associations of people, people who run them. They love doing introductions.
So that's worked really, really well for us, and they have the platform for it. We've also done some online marketing where we pull in leads directly. Yeah, people are really happy to do those introductions.
Something that's different with this is because it's built on AI. We can really scale a lot faster without having to spend so much, without having to raise so much money.
So, in my last business, we had 15 junior developers and two senior ones. At a guess, we would have one or two juniors, the same senior ones, but the funding required is just not as big anymore.
So we use AI in all sorts of places, and everyone's looking at how AI can help them. There's a lot of hype around that. That certainly helps, but we would need a much, much bigger team without AI.
And honestly, it's far more than I could have imagined just a few years ago. So, yeah, this is absolutely key for us.
David Brown:Yeah, I think Claude does really good coding tools now and some of the coding tools that are out there have exponentially changed how easy it is for people to get started and for people to do things. I mean, in the very beginning, I actually, and I've told this story on a couple of other shows, but I'm not a developer; I have no skills.
I can write SQL, but that's about it.
And when ChatGPT very first came out, I just, you know, everybody was saying, oh no, you can just get it to write an app for you, and it'll just do like, like a Chrome extension. You can just get a Chrome extension, it'll just do it for you. And I was like, really? So I was like, fine.
I tested it, and I just came up with some stupid idea, and I just thought, I don't know, it's like simple text replacement. Like, you know, replace he and she would they or something. Like, anytime you see that in a web page, just automatically replace it and, because that's the only thing I could think of, there's no agenda behind it and. But literally five minutes, it wrote the extension, and it told me how to install it, where to put it and how to do it.
And literally, seven minutes later, I had a working extension.
And I know that's just an extension in a browser, but for someone who has zero skills and who wants to do some really specific task that they would have had to pay a developer to do in the past, who can now just go and do that themselves, that was amazing. And that was on 3.0, like in the very beginning.
Now I know people are building super sophisticated, you know, software tools and apps and stuff like that.
I mean, there's, there's a guy that I know that has a YouTube channel, and he has just videos where he's not a, I mean, he's a sound engineer by, by trade and has been and was on radio, and you know, that kind of thing. And he's now building apps, and he's just building apps and putting in, you know, showing the whole process on YouTube, and it's amazing.
And I wonder sometimes, though, what's the knock-on effect of that in the long term?
Because you said yourself you don't need as many junior developers, which means there aren't going to be as many junior developers coming through the pipeline in 10 years to be senior developers. So how are we going to get senior developers in 10 years if the junior developers don't have jobs?
And it's not a question for you necessarily, but you know what I mean is industry-wide. It's almost like, well, but will we even need them in 10 years?
Hugh Halford-Thompson:And it's not just developers, it's junior lawyers, it's junior whatever it is. I think a lot of people compare that to the agricultural revolution and things like that.
Tractors came in, and people eventually got new jobs, but a lot of those people who had jobs never got new jobs, and the next generation did. I think there's a particular sort of age group who've just come out of university without AI, who will be, you know, hurt the most by it.
Ultimately, there will be a lot more you can do of it. And I think with, you know, senior developers or senior lawyers and everyone else, how do we train people up? AI is fantastic at education. It is f.
I now have a personal tutor for whatever topic I want, literally with me every day, all the time. So if I want to become a better developer quicker or a better entrepreneur quicker, I've got, you know, I've got that at my fingertips.
So, I think I don't see a lack of senior people coming through. What I, what I worry more about is what is the, you know, new jobs will come.
I think I don't know what they are necessarily, but I think there's a stopgap where there might be a lack of jobs in the market, which you see already in some places.
Marco Calabrese:Yeah, I think there needs to be a. Well, there'll certainly need to be a fundamental change in the current economic model. This is quite key.
So I think as we progress down this journey, there'll be progressively more AI agents, autonomous AI agents, introduced to organisations which can really sort of execute multiple tasks. You know, an orchestration agent which manages the workload across these separate autonomous agents as well.
And I was just, just thinking about what you, what you mentioned earlier, David, around sort of using Anthropic to build out an application. Have you used the new feature in Anthropic, the model context protocol, at all? MCP? No, it's only come out maybe sort of three or so weeks ago.
In fact, I spent a great part of my Christmas holiday just tinkering and really showing my colours now. But it's just, just tinkering with it and that's, you know, that.
So it's this sort of research which I hope is really going to help us to stay innovative and sort of stay ahead of the curve. But I mean, essentially it, it solves the problem of data and external system access for LLMs.
So it's, it's a, it's a protocol which has a set series of rules for integration pretty much across any, any application. So it makes that integration very much seamless.
And that's one of the things that we're certainly going to be looking at over the course of the next, the next few months to look how we can integrate that. I mean, we've got a great roadmap with a lot of features which I know that our customers will love and also good to add that in as well.
David Brown:Yeah, it's all moving so fast, and it's a. Yeah, it's amazing some of the stuff that they can do. And I've always said I can't wait till I have an actual assistant that can do things for me.
And it's finally getting to that point that I know ChatGPT as well can have some now interaction. If you have the app on your computer or whatever, you can actually run it, and it can interact with other apps.
And I take your point about, you know, sort of the, the industrial revolution and all that sort of stuff, but I think in historically what happened is, is that all the new jobs that were created were desk jobs. And now AI is coming for all the desk jobs.
And I don't know where there, where there is to go other than to back into physical jobs again that the AI can't do. Because all, almost all of the desk jobs are thinking jobs. And anything that's a thinking job, it's for the. It's.
I always say AI is coming for the smart people, and no one's ever come for the smart people before.
And, you know, I think that's a whole different situation because we've already automated out the physical jobs, so there's no physical jobs really to go back to.
Unless you want to, you know, have a coffee shop, or you want to, you know, be a, I don't know, hairstylist or something, like, do you know what I mean? And yes, there's going to be a little bit of a lag while robotics catches up. We're totally off-topic, by the way.
There's a little bit of a lag while robotics catches up. So there will be a middle period where, you know, people can go back and like, my son's 17, he's doing his A levels right now.
I mean, he and all his friends are already talking about it. They're like, well, why would I be an accountant?
Because accountants aren't going to be around in 20 years, so why would I want to do that as a career? Why don't I just go? I won't even. I don't even need to go to uni and get in, you know, 30, 40,000 pounds of debt.
Why don't I just go do a trade, and then I can do the trade, and I can make more money doing the trade than I would anyway, and my job is safe, and I can have a whole career in that. And I'm like, can't really argue.
Marco Calabrese:Or you can become extraordinarily talented at leveraging the artificial intelligence technology. Yeah, Piecing everything together and building something.
You know, I, I certainly see a sort of an entrepreneur revolution as well, where folks start off as, you know, to, to Hugh's point, I mean, AI has been instrumental in, you know, the quick growth of our business because of how we're. We've weaved in AI into every fabric of what we do. And I think for folks such as, you know, as, you know, as your son, that would be a great skill set, I think the fundamental skill set to have going into the next sort of five to 10 years.
Hugh Halford-Thompson:I think part of the issue though, where we're looking at it, I'm answering these questions based on the AI that we have today accessible to everyone.
David Brown:Exactly.
Hugh Halford-Thompson:There's already the AI we have today that's not released to everyone. But it's great that you can make Chrome extensions. And years ago, my brother made one that splits on a Mac.
It was annoying to resize your Windows all the time. It would split, split them and stick them one side and the other. That's now built in. So, he stopped getting revenue from it.
But you can now build that extension with ChatGPT.
What happens when you go on the Chrome extension store and say, I want to be able to split my windows across the screen, and it just makes it on the fly? Then, that option for entrepreneurs to come in has just disappeared.
And I think there are vast swathes of business models that are being built up very quickly and will be destroyed very, very quickly. Yeah, they're valuable in the short term, but it's really hard to see what's going to come further down the line.
Marco Calabrese:I mean, to that point, I mean, the purpose of an entrepreneur and a skill set an entrepreneur has is like intrinsically, is versatile. Right. So, the idea of an entrepreneur is one that builds something, right? What by any means necessary is if that. If, if it.
I mean, an entrepreneur is a problem solver.
So yes, we'll get to a point where AI becomes increasingly good at building and problem-solving, but having the skill set needed to think about, okay, well, what tools do I currently have at my disposal? What skills do I need to develop in order to be more efficient in utilising those tools? And who is it that I can help? Right.
If you can answer those three questions, then I think you're going to be indispensable regardless of what technology is available because then you'll be able to harness any technology that's out there and set up systems. And actually communication, I think, is one of the most important skills to develop regardless of what technology is available.
Because people are still going to be asking you questions about what you know, how you can help, and what value you bring to the table if you're able to articulate that, then that puts you ahead of your competition, and your competition is still going to.
David Brown:Be human for the foreseeable future. Right, okay. We've totally got completely off track, so I apologise for that. Let's try and steer it back to sort of the conversation.
So, let's think about competitors. So, who are your competitors?
I know you said earlier in the conversation that there were several companies in the market that were doing similar things and the, the competitive landscape in AI is intense at the minute. Like every company, even if they're using machine learning, they call it AI and you know, so everybody's got an AI tool.
So how do you, you know, I guess, what are the companies that you see as your biggest competition in this market?
Hugh Halford-Thompson:The first time I was an entrepreneur, I looked at the startups next door and saw them as competition and ultimately, my competition was IBM, and we were all looking in the wrong place. And lessons learned. That company went well. But you mentioned LLMs. They're battling it out at the moment.
There's a lot of parts to the AI market, but as LLMs get better, that kind of for us and companies like us, that just lifts everyone. Our real competition are data analytics providers.
So, small organisations will generally have siloed data analytics inside various solutions that they use. This could be, say, open rates, click rates, et cetera in your MailChimp campaigns or your active campaign email campaigns.
It could be analytics in your CRM like HubSpot or Pipedrive or anything at the bigger end where you've got the more sort of global competition. You've got companies like Microsoft's Power BI.
You've got Tableau that provides a single view across all of an organisation's data in a nice single place. So connecting those silos, that is very expensive to do. It can be a painful process using the traditional methods.
So what we're doing is really bridging that gap. We want to provide high-end analytics to small and medium firms at a fraction of the cost.
And then additionally we make it more accessible to the whole team. If you don't have millions of dollars for your data team, we make it accessible to everyone.
David Brown:Sorry to go back and ask a technical question, but when you're querying the data, do you need that in a single data warehouse? So do you need them to take the data from different places and put it in the warehouse, or does it?
Or are you able to work on like a more federated model where you can actually go out and pull the data from wherever it needs to be, pull it together and then send it for analysis? Do you need to have that extra layer where you've dumped everything together?
Hugh Halford-Thompson:So I could phrase it as either or of those, but the. Yeah, realistically, we connect in, we ask clients for API keys and others to connect into their systems.
What we'll do is then, so we're read-only for analytics, we don't need to write back in at the moment.
So we'll pull all that data in, we convert it to a standard data model on our side, which is where all the clients that we're serving being really similar helps out a lot. I say standard; it's semi-standard, but there's a core to it that stays the same.
And then we put it in our data lake, and then we're effectively doing lots of queries for that, for building out the charts and then translating all that into language for the AI so that it can then help you through it and walk you through it as well.
David Brown:Okay, cool. All right. Yeah, that's with my public sector hat on.
I was just thinking about how that might work in a practical environment because I know some of the other tools have challenges with that as well. And so, you know, like you said, you end up with a bunch of different individual tools that can only see the one data set that they're looking at.
And they. There's no way to kind of see across the organisation unless you go for something like a tableau, which is enormously expensive.
Hugh Halford-Thompson:And the problem there is like integration is a pain. Data mapping as part of the integration, that's something that you farm out to the cheapest place you can.
You get the intern or the lowest-paid person to do it. It's important, but it's long and boring and low-paid, and AI is fantastic at it. So we have it just about to.
David Brown:So, can AI do it now?
Hugh Halford-Thompson:Now, it does all our data mapping. It writes the code with that data mapping to pull it in.
We've got frameworks and structures that we put in place, and it's doing bits of it, but it's fantastic at all that. So that whole integration sort of phase has transformed.
And I don't think like people see the, you know, the conversational interfaces, they talk to AIs in various places what they don't see unless they're more techie and, and in that industry is the amount of things that it's changing in the background and all the workflows and everything else that's being done behind the scenes.
David Brown:Yeah. So everybody's got AI. How do you stay ahead of the competition?
Marco Calabrese:Researching, constantly innovating, constantly refining our roadmap. That's really what we do.
So, as I mentioned previously, I was looking at the model context, protocol and anthropic, seeing how we can weave that into streamlining data ingestion and integration across applications. Further siloed systems is a huge challenge for many membership organisations that we've spoken to.
So how we stay ahead of the competition is by constantly looking at what's going on. And it's, you know, it's a, it's a daily, daily change. Every single day, something new pops up, and it just, you know, blows you away.
So we're, we're constantly, we're nimble enough to be able to take advantage of these opportunities very, very quickly. We've got to, you know, we've got to a very streamlined team. Our operations are tight, so we can, we can move quickly and iterate quickly.
Hugh Halford-Thompson:If you already have a data analytics platform, you've integrated all these systems, you've got a whole load of platforms that you need to support, and it's, yeah, they're all, by the way, they're all putting in AI somewhere.
Generally, summarise this or write that some are doing a little bit more, but it's like fundamentally, we're coming in with a blank slate at a time in history with, with, with AI just coming out where we can make things, we can architect things in a better way in a cheaper way to support with less staff, less people internally. So it just means we can move a lot faster.
David Brown:And you, you talked about a minute ago, you know that you were able to work with a much smaller team. So I know I've got the two of you on here, and on the website, you've got four people.
Is it literally just the four of you at the minute or is there more team than that? And can you talk a little bit about that?
Marco Calabrese:I'm honoured to be working alongside some of the greatest people I've ever had the pleasure of working with. John Cafton is technically gifted. He's been a very close friend of mine for 15 years, and we've worked on multiple projects together.
He's been building trading platforms for Tier 1 and Tier 2 investment banks for hedge funds. He also built an algorithmic trading platform to help retail traders write code and trade on the Markets.
So he's our sort of amazingly skilled developer. We've also just had Guy Halford Thompson recently joined, who's Hugh's brother. And maybe Hugh, you can talk to his experience.
But just a final key point in our team is Alex Debar, who is the ex CEO of Naylor Association Solutions. You know, extremely skilled executive and our strategic advisor as well.
Hugh Halford-Thompson:Yeah, Alex gives us the connection to the association market. He knows all the people we need to know in the States, which is the bigger market for us anyway.
And then as always, and then yeah, Guy, my, my brother, we built out several companies together in the of which we took public. He's gone on and taken, I think, one more public and one he sold to a public company.
He's raised over £30 million from investors and had a lot of success.
He's been guiding us through how we build this up, how we structure it, how we work with investors, a whole load of it on building a company, which is quite a skill in itself. And I think the team works together really well. I did think several times that we would need to hire junior developers.
We've kind of covered this already, but at the start of the business, there were probably three or four moments I thought, okay, we got to hire someone to do XYZ, even if it's part-time or whatever. And then AI stepped in and fixed it. So now, whenever we think we need to do something new, we go to AI first.
Marco Calabrese:And finally, our newest member of the team is Christian Libeta, who has been in sales and marketing for software solutions very similar to ours for the last 15 years. So he's handling the distribution side with.
Hugh Halford-Thompson:Myself, I think like building up a company is like something where you learn a lot at the worst possible times and having a team that's gone through it before with, yeah, with Marco, myself, Alex, Guy, John, like we're, I think everyone in the team has been an entrepreneur, but we've been through it before. We've got a good understanding of technology, we've got the connections that we need.
We know how to build and scale tech companies, and frankly, with AI, it's, I'd say it's no different, but we, you know, we need less staff. We can do it with a smaller team, but ultimately, it's another technology.
I'm going to say, going to what you said earlier, in its current form, it's another technology we can leverage to automate all the stuff we wish we could automate before. And then it's a case of just staying on top of A fast-moving industry.
David Brown:Yeah, that makes sense. Have you seen the T-shirts that say, "This is my second rodeo?"
Hugh Halford-Thompson:I haven't, but this is my fourth successful. Well, we'll have to reflect on that in a couple of years, but that's.
David Brown:Easy to get one. So this is my fifth rodeo. I'm like, yeah, I could probably have one of those. Cool. All right, let's get to the important thing.
So funding, financials, that sort of thing. So, are you guys looking to raise money at the moment, or are you okay?
Are you, are you sort of, are you eating your own dog food and sort of able to bootstrap or what's going on there?
Marco Calabrese:Yeah, so I mean, we're fully self-funded at the moment with growing revenue. We've got a really clear path forward towards £10 million in annual recurring revenue from the association market alone.
And we're looking for strategic investment certainly to accelerate our growth at the moment.
David Brown:And what would you do with that money if you got it?
Hugh Halford-Thompson:So the platform's fully working already so we can onboard customers. They're using it already; they're getting the value. So now we want to accelerate onboarding new customers.
I think we've, yeah, there'll be a few tweaks we do with the, with the process as we accelerate that. But ultimately, we need to invest in sales and marketing and really scale that first and fast. From there, we can expand the development team.
I think as we do more and more integrations, there's going to be more work there, but it won't be; it won't scale anywhere near as it used to, and that will accelerate our roadmap, and then we'll need to build. We don't know exactly how many customers per customer success person yet. We'll kind of fill that out.
But over time, we're going to need some people to support customers more ongoing as we grow that customer base.
David Brown:And thinking about those strategic investors that you'd like to have, there's all different types of investors that invest at different levels. So, what sort of investment are you thinking? Are you talking like hundreds of K? Are you talking low millions? Are you talking tens of millions?
Because those are all very different in sort of investor profiles. What do you think would do the best for you at the minute?
Hugh Halford-Thompson:Yeah, so we're considering whether we need to do, whether we should do a smaller round first, that ultimately if we do a bigger round, we're giving up more equity earlier on. The most efficient way to raise money and the riskiest is to only just raise Just enough money each month.
Ultimately, I think we do a small round now in the hundreds of thousands. And then as we scale, then it's going to be a question of how fast can we scale, where are the bottlenecks?
If we, you know, do we want to put a million behind it, raise a few million and really accelerate that? I would say once we're beyond that, looking at the tens of millions, we'll be a different company. And I'll, I'll answer the question again.
David Brown:That's fair. And have you, have you done your advanced assurance thing for EIS and all that?
Hugh Halford-Thompson:We were looking at that just yesterday, actually. I remember it took six weeks the last two times I did it.
I understand their process has got a bit more streamlined now, so it should be a couple of weeks. And also both times I've done it before. We were a Canadian company with I. Lots of complexities.
But yeah, we will get that done before the round, of course.
David Brown:Okay, brilliant. Right, so I think we're at the end. So, who wants to talk about the vision?
Like what's, what's your grand vision, you know, for the next sort of five to 10 years? What, how do you think the company is going to evolve, and where would you like it to go?
Marco Calabrese:Yeah, so I mean, picking up where we spoke about, you know, a different sort of economic model, I mean, I certainly see the world moving away from manual interaction with multiple applications and data sources. So that's sort of the starting point.
What our vision is, is to create a single entry point into key systems and their data and interact using natural language. So, we want to transform how organisations make strategic decisions.
So, really, sort of levering that barrier to entry and making sophisticated analytics accessible to everybody.
And ultimately our goal is to be the world's most trusted AI strategic advisor and have AI agents running a great portion of our own business in order to automate things and free up our.
Hugh Halford-Thompson:Time just to jump in a little bit. There's a couple of successes, like some of the success that we've had. That's really, as I said, my favourite points that have come back.
We've had some users that we've onboarded that have tested the platform and yeah, we get, we get some people who are quite a bit more data savvy, some, some less so. We've had several now who've come in, they've tested out, they've said, look, I'm really sorry, guys, I'm not a data person.
These charts are just, it's not for me, but I spoke to the AI, and I got everything I needed.
Now, that is super powerful people with no understanding of analytics by their own admission; they get the support that they need to talk to their data. And I think that is going to be transformational as it gets rolled out across all sorts of organisations.
David Brown:That's the sound bite of the show. Nice one.
Hugh Halford-Thompson:You skipped the question where I had it written in, so I just chucked it in there.
David Brown:No, that's perfect. That's perfect, guys. Hugh, Marco, thank you very much. I think that's everything unless you want to have one last word at the end.
Marco Calabrese:What I would say for anybody that's on the fence of, you know, maybe using AI is just like to say that it can truly support you in far more ways than you might think. AI is now more accessible and cheaper than it ever has been before.
It really does help crystallise your thoughts and drive you in a direction that you're seeking to get to.
David Brown:Brilliant. All right, gentlemen, thank you very much for your chat today. It's been amazing. And for listeners, thanks for you.
Thanks to you for joining us today as well.
If you're a startup looking to showcase your AI-driven innovation or a sponsor wanting to support the future of AI entrepreneurship, visit withai.fm and learn more about how to get involved. And that's it for today. So thank you very much, and we'll see you soon. Bye. Bye.