Scott McIsaac & Len Landale, Co-Founder's at Helios Core AI. Helios Core AI is a new, forward-thinking company offering a fresh take on driving outcomes with AI at scale. They focus on delivering practical, transformative AI solutions that simplify complexity, unlock insights, and optimize business performance. With a commitment to innovation and measurable results, they help organizations elevate their operations and achieve success in an AI-driven world.

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In this episode of the AI Minds Podcast, Scott McIsaac and Len Landale, Co-Founders of Helios Core AI, share how they’re building AI-first services for real-world impact.

They reflect on their transition from large enterprises to startup life, driven by a mission to make AI practical, personal, and powerful for businesses and individuals alike.

Scott and Len discuss Helios Core’s dual focus: helping SMBs navigate AI complexity and developing Kokoro, an AI companion preserving voices and memories of loved ones.

The conversation explores how to ground AI in real use cases—from voice agents improving rental operations to ensuring AI doesn’t sprawl into solutionism without value.

Scott introduces Kokoro’s emotionally resonant design, aimed at connection and comfort through AI, while Len outlines how to prioritize impact in a noisy AI market.

Listeners will gain insight into building AI with purpose, balancing emotion with utility, and crafting systems that serve both business goals and human needs. In this episode of the AI Minds Podcast, Scott McIsaac and Len Landale, co-founders of Helios Core, share how they’re building AI-first services for real-world impact.

They reflect on their transition from large enterprises to startup life, driven by a mission to make AI practical, personal, and powerful for businesses and individuals alike.

Scott and Len discuss Helios Core’s dual focus: helping SMBs navigate AI complexity and developing Kokoro, an AI companion preserving voices and memories of loved ones.

The conversation explores how to ground AI in real use cases—from voice agents improving rental operations to ensuring AI doesn’t sprawl into solutionism without value.

Scott introduces Kokoro’s emotionally resonant design, aimed at connection and comfort through AI, while Len outlines how to prioritize impact in a noisy AI market.

Listeners will gain insight into building AI with purpose, balancing emotion with utility, and crafting systems that serve both business goals and human needs.

Show Notes:

00:00 "Embracing AI in Startup Ventures"

03:16 "AI-Focused Managed Services Launch"

06:35 Client Solution Needs Framework

11:32 Rapid Market Evolution Impacting Solutions

15:27 "Efficiency Tools Boost Sales ROI"

18:42 SMBs Invest in AI for Efficiency

21:18 Recreating Voices for Clients

23:33 Digital Immortality Through Voices

More Quotes from Scott & Len:

Demetrios:

Welcome back to the AI Minds Podcast. This is a podcast where we explore the companies of tomorrow being built AI first. I am your host, Demetrios, and as always, this episode is brought to you by Deepgram, the number one speech to text and text to speech API on the Internet today. It's trusted by some of the world's top conversational AI leaders, startups and enterprises, some of which you've probably heard of, like Spotify, Twilio and NASA. In this episode, I am joined by the co founders of Helios Core, Len and Scott. How y' all doing today?

Scott McIsaac:

Great, thanks for having us.

Demetrios:

I know that this is not your first rodeo into company building. You started a company in 2004, you got that company acquired and then you went on to live in a new company after that, but you got the bug again to start something once you saw AI come out. You were in the managed service space in your last lifetime. We could say now you've brought a little bit of that forward to the AI world. I would love to hear, Len, if you want to kick us off with the inspiration behind Helios Core.

Len Landale:

As you said, we kind of got out of what's a large enterprise world back to founder run and startup space, which has been fantastic. So, personally, that was a big motivation for us to get into something that was creative and really frontline, helping businesses solve problems as opposed to budget spreadsheets and politics and all the other things that go along with being in a larger organization. So that was first and foremost in us getting out and doing something kind of in the startup world again. But bigger than that, we're both huge proponents in how this is going to change the way that the world works, Artificial intelligence and the future of work, the amount of things that you can get done, the things that you can create. Leveraging AI is a huge motivation for us.

Len Landale:

We've had a great time building what we've built so far and helping customers kind of get their feet under them in the AI space.

Demetrios:

So, Scott, can you break down a bit, Little bit of what you mean when. Because I know that we talked before we hit record about B2B product that you have and the B2C product. Let's focus on the B2B product for now. And you come at this with a bit of a different angle than others, and you say, we just want to help folks solve their business problems. The platform and the way that we go about that is Almost secondary.

Scott McIsaac:

So when we launched the business, the idea was we were going to help companies navigate the complexities of AI. We kind of look back 15 years ago where cloud was and it's we think AI is kind of in the same place. There's a lot of options, there's a lot of failed projects, there's a lot of complexity that's being built with, without security focus, without cost implications in mind. We really wanted to help the clients navigate that complexity. Kind of fast forward to a couple months after we launched and we talked to a lot of clients and we realized that what we are actually building is the same thing that we built in a past life, but with AI first. So it's an AI focused managed services platform and we're not going to go out and sell the platform independently, we're going to sell it as platform plus services to really help our clients. So we can use the platform to accelerate the journey on AI and scale AI into their businesses and then we're there long term with them to help them really kind of manage this, make sure it's scaling into their environment, make sure they're using it, make sure they're getting their ROI and actually the outcome that we said we were going to be able to build them and versus some low code, no code tool that they use to push into there.

Scott McIsaac:

So really we built a services based AI company but there's a platform that goes along with it to deliver that.

Demetrios:

The first thing that comes to my mind is the use cases. Because right now I'm sure you know better than anybody, we're still exploring what is possible and what potentially you can create with AI. And so I think about this idea which I really like, I get to interface with you, not a product that has to support my use case. It's almost like you are the one that I'm back and forth with and you have to support my use case. And I wonder on your side, how does that work when you are trying to figure out the product to build? Because I can imagine you can either get like feature sprawl since there are so many use cases that you can support, or you end up becoming a Swiss army knife and you have a little bit over here, a little bit over there, like can you walk me through a bit of your thinking on how to build the best product in the background but be almost like the API for the customer.

Scott McIsaac:

I think the biggest challenge of any company is really understanding the use case and Even taking it back even further. What's the data? What data do they have? is it in a place that's usable? we kind of start with this, call it consulting. First approach, where we'll come in, we can operate a couple weeks, sprint. Part of that is education. Part of that is extracting what the use case is and what the challenges of the business. Again, we're not trying to build something to find a problem. We're trying to build something that solves problems so we'll understand what the problems of their business is. Where do they spend time? That could be a very simple use case, that could be a very complex use case.

Scott McIsaac:

It just depends on what the client is looking to solve at that point. What we do see is that sprawl, it absolutely happens because they start thinking about, I can build this thing to solve that problem. But I have this thing that could also plug into it. So for us, that's where we have to kind of step back and we really break it down into three categories. If you buy something right now and it solves these problems, what are the musts? what does it have to solve right now? Then you go on top of that and say, what are the nice navs? If it could also do these things and you could call that useful that what would you want? And then the kind of crystal ball future, what is the long term vision? What do you want this thing to do? So we kind of try to break it down into that is if this product is successful, what is it doing for you? And the basics, if it can't do anything but these things, it's successful.

Scott McIsaac:

If it can't do those things, it's a failure. So we try to break it down in those categories to really understand it. And it's actually helped because clients then start thinking about it and don't start putting everything in the kitchen sink from that offering and from.

Len Landale:

Our perspective and what we're building in the technology stack. There's a potential risk that it could try to be everything to everyone. And part of that goes back to understanding the use cases and applying the right tool for the job. So we're not trying to be the everything to everyone platform. We're also not under the impression that everything is a nail and we've built a hammer.

Len Landale:

So if there's other tools that they can leverage maybe to accomplish their goal. We're going to educate and say, hey, this is a better use case to just use ChatGPT. Just go be creative over here. If you're really trying to get your operational AI in your business and you're going to get an agent to interface, make decisions, do tool calling and really take part of your business process and truly make that into agentic AI, then that's a better fit for what we've built. But it doesn't mean that there aren't other solutions that we can help advise or implement or have conversations around. So there's a distinction too with kind of what we're building and what we're trying to do as a company. Helping customers solve their problems is ultimately our goal.

Len Landale:

And I like the way that you phrased it is we're that interface. They don't have to know everything that's going on in the AI world. We're going to take a lot of that burden on educating ourselves, building, underlining technology into what we've built because we don't want to also have to do everything from scratch. Deepgram is a great example of that. We're using tools in our platform and our technology stack that are specialized to accomplish what we need to do. And then we're building, agentic tool, calling into perhaps their back end systems to make the agent do work in their organization. So it's really bringing all that together and helping them kind of understand that world and then build those, use cases like Scott said and then go execute and ultimately long term remain engaged to help them evolve those and maintain it, make sure adoption's there, tune it as it needs to be tuned.

Len Landale:

Those I think are critical differentiators for us.

Demetrios:

I had a friend who spent about six months building an HR chatbot back when IT chatbots were all the rage and everyone was trying to see how they could implement AI to in their business. And so the quintessential typical use case I think that everyone's mind went to was well, let's create this HR chatbot. And you can ask it when you have Holiday or anything about the employee handbook, whatever it may be. And he then told me, he said people weren't thinking through this because you spend six months, they didn't really get anything that worked that well. And so they ended up having to scope out another six months to really get there to where it was reliably Giving answers. And they weren't even sure that if they spent that extra amount of time on it, they could get to something that is reliable. And he said, and at the end of the day all we had was a damn HR chatbot that made no impact in the business.

Len Landale:

It didn't onboard new people to the business. It didn't help them with open enrollment and walk them through things. It's funny, we've gone down the path on the HR front a few times with a partner of ours who's in the HR space and a prospective customer who wanted to build an HR chatbot. And it's a good use case to just surface information more readily. I'd rather ask a chat a question about my HR policies than go to the website that's got 130 PDFs and try to find that data myself.

Len Landale:

There's value that it can provide. But you're right, it's really kind of scratching the surface. And I think funny you say six months ago the complexity of building it was much higher. now you could probably build that with a lot of things that have been introduced into the market because the rag data coming in is a lot easier than having to build your own rag vector database and all the things that you would have maybe done six months ago. So the evolution of the pace of change is also maybe changing the use cases that people look at. That was a great idea six or nine months ago maybe now it's something you could do with a tool that does. Let's look at Microsoft ecosystem if you're in it and you can just expose the data in a secure way to your team and they can answer questions about it. Great, that's helpful. It doesn't change the way you grow your top line of your business though.

Len Landale:

If you've got an outreach process that, right now you've got people calling and trying to educate potential buyers on your product and you could turn agentic AI that could have a conversation. Not the 10 people you've got calling, but 10,000 phone numbers out there. You could then do something at mass scale that would have a meaningful impact on your business. So I think that's where people have to kind of understand where the technology is going. Look at the use cases in the business and say what's going to have this 10x performance increase on what I'm doing? Either efficiency in my operations or top line Growth or cost savings, cost efficiency. There's a lot of things that you could kind of go down that path with.

Scott McIsaac:

And let's say that the chatbot, let's say it was an amazing idea, and if it was used correctly, it could either save the company a massive amount of money or it could generate revenue in ways. Even if you build it, you still have to figure out how you're going to scale it into the organization. How are you going to get your users to actually use it? And I think that's part of where we come in, because we're not just building it, we also want to help you scale it into the business. We want to make sure We're monitoring and make sure it's being used. We want to make sure that people understand how to use it. You can't just throw technology and go, hey, look at this amazing tool.

Scott McIsaac:

People look at it once and then forget about it. So it's about constant education, constant training, constant reinforcement of the tool. And that's where we come in and help as that trusted partner on the.

Len Landale:

And the feedback loop, like Demetrios you mentioned, you don't even know if it's giving the right answer. Well, we've also built a framework, a portal into our framework, so that the human in the loop can go, flag the silly answer and the hallucination, and then that goes back into tuning and getting a better prompt and maybe refining the data. So having AI in your business at scale, you have to have some of these things considered. And that's what we're building into our stack, so that when you get there, you've got a mechanism to say, it didn't do a good job here, or I didn't anticipate this edge case and It blew up. And my user community is giving me feedback. I need to then have Helios come in and fix it.

Len Landale:

So that's part of the whole enterprise thinking, too, around adopting AI.

Demetrios:

Have you found any frameworks or ways of approaching the choice of use cases? Because as you mentioned before, like, people's eyes get big, they want to do it all, and you have to start somewhere. And so maybe you think about, well, we have a percentage of success on one axis and impact on the bottom line on another axis. And we want it to be as high up on both of these axes as possible. Or are there other ways that you look at that?

Scott McIsaac:

So that's actually the hardest part, How do you show the value to the business that they're actually going to get out of this.

Len Landale:

Right.

Scott McIsaac:

And we've seen a couple different ways to show that one is what savings are you actually generating for the business? I mean, we've done it through, hours in labor. So we were working on a sales agent essentially and we showed the roi. Basically you don't have to hire two more salespeople because we can generate and make your current people more efficient with the tools that we can create. So it was done through more of an efficiency in time savings. Like if you spend six hours, doing cold emails out to your clients a week, if every rep does that, we can free back six hours of their time by automating that or cold calling. We can have the agent call out and make those calls on their behalf. We've also, in that same sort of use case we're having it actually listen to the sales calls. So think about having an agent on the call with you when you're talking to one of your clients and it's giving real time feedback of listening to what the client is saying and then suggesting ways to sell that product to that client.

Scott McIsaac:

So driving top end growth because you're selling more things that you might not have thought about, if you're just on that call listening to them. So we're looking at it in a couple different ways of trying to really show that roi. But it just really depends on the use case and it depends on what the client's goals are. If it's top end revenue, which we try to steer that conversation to saying, AI doesn't have to just be a cost saving things. It can help you generate revenue too. It can help you grow your top end. And so we have different metrics we'll look at from that perspective or if they are going into savings, we can show that as well.

Demetrios:

So I want to Len, did you have some. Before I jump.

Len Landale:

I was going to say some of it depends on the maturity of the business too. So we're helping companies at all levels of the spectrum, enterprise startups, all in between. So the use case Scott was talking about that it's easy for someone who's running their own business to recognize, okay great, if you can get my team 10 or 20% more efficient, I don't have to hire more people. Therefore this is value creation. You get into a larger enterprise that maybe has security compliance concerns, regulatory concerns, how does it fit in with a complex application landscape that's growing up over time.

Len Landale:

They've got a very different set of criteria as to what's important and where you might put your energy. So part of that's again given the background that we've got 20 years in managed services, having those two areas to pull on and how to speak to someone who's maybe running a startup or a small enterprise and positioning AI as a revenue growing cost saving agent they can leverage and grow over time. a lot of their concerns are just finding good quality hires today. Well, if you can offset the need to have turnover in a business that's a huge cost savings too. So there's a lot of ways you can look at it depending on who you're speaking with as well.

Demetrios:

Who do you typically engage with and see the most appetite from for this kind of stuff? Is it the small or medium sized businesses or the enterprise Y types?

Scott McIsaac:

That's a little bit of an odd question because. Or not we're gonna have an odd answer to that question because we started out thinking that we would always go after the mid enterprise. The call it 1 billion to 5 billion space. Cause that's where the space we played in in our past life. But what we find with those clients is their biggest concern is the data side and trying to build a data platform to be able to get ready to use AI. So how do I take SAP, how do I take Oracle or JD Edwards or whatever ERP it might be and get that data centralized and build a real data lake so that I can actually use it. The place that we were not thinking we were going to go is that SMB space. And what we found is there's a huge appetite in the SMB market to make investments into the AI world because they need to become more efficient.

Scott McIsaac:

They know that if they can't compete with their bigger competitors that they're going to risk potentially going out of business. So we saw that there is a big appetite to make investments and that was we have a voice agent that we built around Deepgram specifically that's actually taking all of the front end calls for that business. And basically they're a rental business. They do heavy machinery and excavator type rentals. And this AI is now sitting on the front of his business, taking all his calls, doing all his orders, troubleshooting with his clients and ultimately it freed up all his time to go focus in other place. And this is.

Scott McIsaac:

They're small, right?

Scott McIsaac:

They're an SMB space business. So we were not thinking we were going to go into that space, but there's a pretty big appetite in it.

Demetrios:

And a lot of times that's what's killing your time when you're just getting these random calls or you're working with potential customers or customers themselves. And the questions that they have are fairly trivial.

Len Landale:

Yep, absolutely.

Demetrios:

So let's change gears a bit and talk about the consumer side of things, because I know you all have Interesting product that you were just telling me about. Maybe you can set the scene about what you're doing with Kokoro.

Scott McIsaac:

So Kokoro. Let me give you a little background of what it means. So it's a Japanese word for mind, body and soul. And this kind of came out of a little bit of a strange space. We were not really planning on doing anything like this, just kind of as the voice agent kind of came.

Scott McIsaac:

We built the voice agent for a couple different clients, and we found that our voice agent is actually really good. We had a client come to us about potentially bringing back someone that isn't here anymore, and they wanted to kind of recreate their voice and the memories. He was a very public figure, so it was easy to find a lot of the background and stories about it. But we created a version of him that actually called the owner of this business. And he was, how did you guys do that? Like, it's crazy. And we did it on a couple minutes of voice.

Scott McIsaac:

We trained it and made it sound like him and whatever. So that kind of led us into a space where we were thinking about how do we help people with AI and there's a lot of data out there right now that people are using AI for companionship. they're talking to it. It's becoming part of their daily routine. It's becoming part of their daily life. it's just something that they use.

Scott McIsaac:

I find myself talking to AI a lot and brainstorming and things like that. I don't use it for companionship necessarily, but it led us down a path of saying, we can help people in the grieving process potentially. What if we could, someone could buy a service that stored Memories that stored stories that stored the essence of a person, the Persona of a person. And then when they've passed away or they're not here anymore, they can't have those conversations that you can reach out and still talk to that person. Sure, it's AI right. It's never going to be real.

Scott McIsaac:

But the idea is that it touches a lot of that emotional side where, my mom passed away in 2021.

Scott McIsaac:

What if I could just hear her voice again? What if I could just, ask her a question or hear a story that she had? So we used, we can use this voice agent in our ability of tool calling and in storing of memories and all these things that really create a Persona of a person. And so that's kind of how we got into the name of Kokoro. Mind, body, essence. And we're going to launch this in the next couple weeks here because we think we can help solve problems for people.

Len Landale:

It's a really interesting space because as we've kind of floated the idea with friends and brought this up to a few people, there's mostly positive response that everyone's that's really powerful. That could help people. And you start to get these stories and relations that this isn't new, this desire is not new. my kids have a recording of my mom reading the bedtime stories from years and years ago, And she's since passed on. So there's not much they have of her anymore they've got some memories. They probably have photos or 8 millimeter film Like it fades with time. But technology's gotten to a point where it doesn't have to, I would have called my dad's voicemail after he passed to hear his voice I said that to people and they're like, everybody does that. I'm like this is common, So imagine if you could have the voice, some of the memories, the advanced technology to kind of craft a conversation with that individual again.

Len Landale:

We want to make sure that people aren't thinking this is real. This is just a new way of using technology that people have used old technology to do for years, Calling voicemails, recording their voices on cell phones, whatever the case may be, taking memories and jotting them down in a book so that you have them, they're recorded. Well, what if all of those things could be combined into one? That what we think we've created, which is pretty interesting. And we're excited about launching it.

Demetrios:

I think it's such a cool use of technology, especially if you can A, help folks get closure or B, just connect people a little bit more. So if you have families that are living on different sides of the planet and I can't be there to read a bedtime story to my daughter because I'm traveling, she can still have me read a bedtime story and I don't have to go and pre record every single bedtime story that is out there.

Len Landale:

Or do made up stories. I mean, I used to spend hours making up stories for my kids when they were little.

Len Landale:

To your point, I traveled a lot globally in my career. There was many days I weren't there. Even if this was something that they had, when I wasn't able to do it, they knew it wasn't me, but they had the opportunity to hear, that connection was there and fostered. I think there's a lot of potential. it's interesting times for sure.

Scott McIsaac:

It's the technology that's allowing it too. just how emotive these voices can be now. I mean, before, you could always have some sort of text to speech solution that was just kind of read something back in a voice. But now you can it can be a motive. you can feel it, you can hear its, excitement in the word. You can hear its sadness in something.

Scott McIsaac:

It's crazy. And I mean some of the technology is letting us do that.