AIエージェントを作った後、どう統制するか:Lyzr AI共同創業者Ani Narayan氏に聞く

All right, Annie, welcome to the show. I appreciate you spending some time with me. Yeah, Cain, thank you for having me. And I love the branding. You've got BUX on the back. You've got it on the mic. That's awesome. Absolutely. And normally, the mic would have a, if I'm sitting at my desk, I've got a little yellow thing on the mic. But in the absence of that, and in the absence of my neon sign being broke, I have spotted a slightly yellow-tinged hoodie. So there's got to be a little bit of yellow around somewhere. Nice. Nice. So, yeah, cheers for that, Annie. I appreciate it. I'm curious, I mean, there might be a lot of people listening to this who are familiar with Liza. There might be some that are not. So maybe it would be a good place to start for you just to kind of give us the whistle stop to what is Liza, you know, where does it originate from, and what is it that you guys do? Sweet. So where it originates from, actually, Siva, who is Sivitri co-founders. Siva started this company in August 2023. We were initially a data analyser product, hence the word Liza. Right. And the way it was is you had an SDK.

You could just essentially, with one line of code, you could be like, hey, I'm going to upload my Excel files or any data and then ask questions. And you had like an LLM at the kind of powering this underneath it all, right? And then I think when Shiva was building this, you realized, man, it's really hard to build this using existing platforms out there. So why not build a full stack agent builder at that point in time from scratch? so I think 2023 and 2024 is when we were like hey we were a low code agent builder but what's happened over the last couple of years is today there's a big need for hey I don't want to use 20 different platforms to build an agent and take it live into production and I have multiple use cases where data is getting in one place I have I want to use one model or I want hosting of AWS versus let's say Google. There are a lot of these nitty gritties that an enterprise cares about.

So you want a highly modular platform where you can plug and play some of these things. So we decided to kind of build a full stack agent infrastructure platform. And what does a full stack agent infrastructure platform mean? When you want to deploy AI, you essentially need LLMs, you need data, you most probably want a deployment layer, which is essentially what we started with. You want a layer to observe what's happening with all these agents. So all of that is essentially contained within MySpace. So that was 2024, 2025. So really good for, let's say, CTOs or CIOs are like, hey, I want to build these agents internally. Then over a period of time, I would say over the last year, year and a half, what's become really hot is I need to manage all these agents that are being built. We've hit like, it's like, you know, like Instagram came out like, hey, people had like one or two like posts they're putting on. Now it's like, I have all this content in the world. How do I manage it?

So it's become like, hey, agents have become easy to build. Now it's like, how do I manage all these agents? So we have something called OpenController, which is also like a platform where I can manage all these agents. But again, great for companies where like, hey, I'm building agents on cloud. I'm building agents on, let's say, Gemini or like platforms like Lionchain, Thurya, Lysa. I also have coding agents like, let's say, Cursor. How do I make sure all that agents can all be maintained in one place that we built? And I can see the ROI, I can see the agents have gone rogue. I can be like, hey, there's maybe an agent that's running in loop, which is burning a lot of tokens. So I need an observability central control. That's become our latest proposition. That makes sense. I'm definitely interested in exploring more about the uh the observability side of things and what makes that successful but i'll also be interested in you know where do you tend to focus or do you notice

any trends if you like in terms of where your clients are focusing as far as what type of agents are they building because i had a quick look on your website it doesn't seem as though you have any discrimination between various types of agents in various types of industries, whether it's customer service or back office or whether it's whatever, whatever, whatever. So I wonder if you could share with me a little bit in terms of like, as far as your customers are concerned, where is it more common to be deploying agents and what are the sort of typical use cases that you're seeing at the moment? Great question. So let's break that down depending on the customer, right? The most common use cases we've had to give you a TLDR version is you have sales, marketing, HR, customer support, IT ops, procurement, CFO, all functional stuff, right? So in marketing, it started a lot around content generation, but today the most common use cases, I want a set of agents that everyone in my organization can use. So if Kane's running VUX, let's say there are 100 employees,

somebody might want to do content generation, somebody might want to be like, hey, I want to create emails, I want to do videos. So there's a generation side. There may be also a lead nurturing side and event side. So all of that, you have individual agents, right? So let's call it work benches. That's really hot. And people want that for sales, marketing, HR, all of that. So people usually start off with that use case, and then it becomes about moving to critical workflows. So the other use cases that you're seeing today is a lot of my information on legacy systems. a lot of information, let's say, was in the physical, non-digital space. How do I convert that into the digital world? So I have agents that can, let's call it the OCR agent, where you can look at all that data, convert it, and then it's like, hey, give me one place, which is, let's say, call it the central knowledge brain, where I can access all the information that's within the company, and then I can search. I can do a layer of analysis over and above. the third observation that we're seeing is take any of these tools

take a Claude Cowork take Higgsfield take any of these lovable, take any of these popular products enterprises are like, hey I want a private version of this which is air gapped which is, I don't want the frontier models to be touching it I want to use maybe an open mid models or I want to use all of this should kind of be within my environment. And what is the main reason for that? Because I've spoke to a couple of companies who have essentially, you know, using the same APIs, using the, for example, the OpenAI's APIs, just essentially built their own version of ChatGPT internally. And that was largely a cost kind of thing. Like it was cheaper for them to put some upfront cost into the development and pay for the API than it was to pay for a license to use ChatGPT teams for the whole organization type of thing. What are some of the reasons that people have for doing that over and above, just aside from cost, which is fair enough. What are some of the reasons people might have for doing that type of thing

rather than just using Cloud or OpenAI or whatever? Great question. I think, I mean, cost is primary, but I'll tell you what's happening. Let's say you have 100 people in your organization and Chad GPD makes me pay, let's say, $20 to $40 per person, right? Not everyone's usage. You have like 5% of the users that are, let's say, power users. Not everyone is using it at the same amount. So why can't I figure a way out where I don't have to pay that flat fee per month and I pay by usage? So there is a costing angle to this. Second is the frontier models are actually damn good, obviously, right? But all the open-weight models have actually caught on in terms of performance and output at one-tenth the cost. So cost may be like 70-80% of the motivation. We have customers which are like, man, we're spending, I kid you not, $250,000 on Claude every month. There's one customer who's in the FMCG space, they're spending $3 million a month on Claude.

Now it's like, this is not sustainable because we're still in the phase of, I'm building all these agents, where's my ROI? So while that's happening, can I start switching to Frontier Model? So there's different ways of doing this. Concepts like Shadow LM, there's obviously fine-tuning. Shadow LM is like, I take repetitive tasks and I slowly move these frontier models to open-made models. So cost is one, definitely. Second is also flexibility, Kane. Remember when MyThurst came out with Fable 5 and then the government shut it down because they're like this whole cybersecurity issue? They were like, man, what if I was running on Fable 5, man? And it's like a, let's say, critical workflow. I shouldn't have to shut down my business or shut down that aspect of my business. So they want like almost a router, which can switch between any model. Now, if I have to get chat GPD, open AI subscription, then you ask me to get a cloud subscription, then a Gemini subscription. It's just like, it's an overkill, right? So,

uh, give me one, give me all these models. It should route. If one falls, the other one should be a backup. And then the final one is privacy. everyone's getting paranoid that OpenAI and AnthropicNet are using my data to train. I don't want that to happen. So they started with virtual private deployments where you have this API key that's connected to these models, but all the data sets in my environment doesn't leave. Or it's like F that. I'm going to go full bare metal and have a fully enclosed all my models, all my data. It's almost like me connecting my laptop to a CPU and using it. So I'd say those are three main reasons. Yeah, interesting. And in that situation, where does Lyza fit there? Then is it that, you know, these things can be built in Lyza, these kind of like you know flexible kind of model access all that kind of stuff Or is it that you moving more into you know you go and build that with like Langchain or whatever

and Liza's going to sit in the middle and perform that kind of monitoring observability type of layer? Or is it both? It's both and it's all. We are, the way I want people, I'm hoping people think of us as, when I think about agents, I should think about Liza. or when I think about agents and enterprises, I think what lies? So we actually today have seven offerings, but we don't want to complicate it to the world and be like, hey, these are the seven things we offer. If I had to simplify it, we do agent-dig workflow automation with an FTE model, right? So that's our number one swim lane. We do that a lot. That's our bread and butter. So for that, it's a model where, hey, we'll build for you or take the platform and you build on your own. But we also do fine-tuning. We also do, let's say, we have a product just for banking, which we call Madison. Then we have a physical product called Optimus, which is AI and hardware, right? Then we have a control-paying product.

Now, they all hit different personas, but if I'm trying to move from, let's say, a 20 million ARR to a 200 million ARR business, you can have one flagship that really blows it out of the park, or it's like, hey, I need to figure out multiple streams of revenue. All of these streams of revenue are actually fundamental business problems that people are like, it's a pull right now. We just built like a nice brand around it and then we're going daily. Interesting. I appreciate I kind of interjected when you were in the process of sharing kind of some use case examples. I don't know if there's any more you wanted to share on that or whether we covered kind of most of it. We could do more for sure. So I'll maybe give you an example. So one more interesting thing right now that you're seeing with system integrators, which is Accenture, Deloitte, TCS, I don't know if you've seen the stock price. Everybody's stock prices are down. As the news came out, like, oh, Claude is getting into service.

Claude is going to have its own FD program. I remember the stock price of Accenture came down. OpenAI as well, to be fair, have got the same sort of offering now, haven't they? OpenAI have got the same offering now in terms of their pro services acquisitions and all that kind of stuff. They both have that idea. And their goal is obviously going to be around, like, build on my ecosystem, use my models, right? I think the use case that you're seeing with big, large consulting firms is, we work with a lot of large consulting firms. We work with Accenture. Accenture is one of investors. We work with KPMG, Deloitte, Capgemini, the smaller ones like EXL and RGP. we do three things for them which is really hot right now. One is we build internal solutions for them. So they all want to productize. They want to move from services to products that they can sell to their customers. So we build internal solutions for them. We do join go-to-market for their customers in terms of agent workflow automation. And then the third one is we white label our platform.

So there's sell-to, sell-to. So those are three motions. So we end up having some really cool use cases. One use case that I can tell you for a massive automobile manufacturer was that like we spent $30,000 on doing art direction and cinematography on a single day. Is there a way that we could upload a lot of our car pictures and create like 20, 30 really good photo shoot images that we could use for marketing? So that's one use case. There's another use case that we did for a really large makeup brand where they're like, hey, let's say Kim Kardashian came out with, I don't know, like an eyebrow cream. And they're like, hey, can I take something that's trending there and create a marketing campaign that I can push with my products or create a new product quickly and push it in emerging markets. So you're seeing that as a marketing use case. Sales use case, which is really hot right now, is obviously like automate my outbound,

which we do. but then a big aspect of it is also ABM which is hey I want to land and expand I want to go off these 50 accounts do the research send the email map the org how do I go about that so that's one more use case then what's the other use cases I can tell you we're seeing a lot in customer support and I think from customer support it is hey are there tickets let's say, which is very voice enabled, especially front facing. For them, it's a big success. From 100% human led, it can move to even 40% AI led, which is always human in the loop. We've seen some sports use cases as well. What, like sports teams, clubs, things like that? Yeah, we were, I mean, we sponsored the San Francisco Unicorns earlier this year. We met the folks from Miami Orcas. We spoke

to folks from McLaren earlier this year. I mean, you're I'm assuming in the UK, so maybe I'll tell you more about McLaren. Yeah, yeah. So their use case, what they were seeing is which is interesting is there's not much flexibility on the engineering side because it's only like in the winter and summer, they get like four weeks and six weeks to make those changes and the agenting budget is actually part of the engineering budget. So it's harder to do things there, whereas on the operational side, you want to do things on the marketing, you want to automate my HR workflow, all of that you could do. So they're more flexible to work on that. I think engineering will take some time, unless they want to build something on their own. How do you kind of, how do you approach, I suppose, like, the, what am I trying to say? how do you approach like either demand generation marketing that type of thing when you know the

all of these use cases are like quite vast aren't they it's almost like the the your imagination is you can build whatever you can imagine you know which is fantastic and if you've got clients who are saying look i've got all this data of all of the images of all of our sports cards and we want to be able to use that to then create marketing campaigns if they come to you with the idea that's brilliant um and so how much of it is the clients and prospects come to you with these ideas of stuff that they want to do and they just want to see if it's possible to do with ai versus how much do you have to try and like inspire them to think broader and to imagine differently because i can imagine that you know as you said as we've said your imagination is the biggest limitation here. So how much of that is them coming to you? Like we want to do this brilliant idea versus you having to go to them and like look we could do things a lot differently. Yeah. So it comes down to their know-how about agents

and where they are in their kind of knowledge cycle of bringing agents into the organization. I was talking to one of the founders of a massive healthcare company. Like we're talking about they do more than two billion dollars here and they're like hey we're not tech savvy we sell like pharma products but he was quite clear about hey maybe there's one new skill that we can start which is here we're seven companies that keep making these decks and i want to figure a way out where i should be able to be like hey i'm meeting this person this is all the information pick and choose and figure out what deck to make but while making sure it's private that's the thing And the privacy aspect is so big because they're highly regulated, they're public. They will not do this on a cloud. So he was clear about a use case. But then when I spoke to him and said, okay, these are all the use cases that are possible. Hey, we've done this in HR. We've done this in sales.

They go, oh, my God, that is cool. Can we do a session where we educate the next layer of leaders on what is possible? and then you can either so there are two ways you can help them prioritize one is you map out and say you know what this will have the maximum ROI and the other one is you actually crowdsource all the ideas and then you use a tool like what we have called architect where you can put all these ideas in and because we've done a lot of these implementations on the back end it will show you hey this is a high medium low priority because this will have high business impact but oh maybe it's easier to implement It's not that expensive. Or there's a massive tech debt. So we were talking to a huge, I can actually tell you this. We're talking to old mutual from South Africa. They came to the office a couple of weeks back. Big, large, 200-year-old insurance company. They realized that they said 5% of the GDP is spent on funeral services.

and they wanted to make sure the agentic side was not going to be on hey as soon as someone passes away it's like they should speak to an agent it should be they speak to a human but everything behind the scenes behind the concierge person can we make that agent become where getting this information filling that out getting a loan if needed so all of that so I think there's always a layer of education which they're happy to we're still in that era you can say then you can have multiple use cases which can go on and parallel as well and what are your thoughts on I think that the example you gave there of the insurance company wants to keep the human agent front and centre but wants to automate the back office side of things I can imagine that being a type of use case where very specific very dependent on their data and their systems and their process and therefore some kind of custom agent would be absolutely required I curious to sort of get your

thoughts on where you think businesses sort of will go for certain types of use cases. So I'll give you an example. ChatGPT, this week, I literally, we'd just come back from CCW last week, and as a bit of an experiment, I thought, I want to just give ChatGPT all of the videos that we took. We took about five different interviews and some sort of like medium form interviews, like 10 minutes each. And I thought, I'm not going to put that, I'm not going to get the team to do this. I'm not going to do it. I'm not going to get it into Premiere Pro and start chopping it up. I'm going to just put it in a folder, give it a ChatGPT. chatgbt tell it what what we did what we want and all this kind of stuff and i thought i don't think it's going to be able to do it you know i thought there's no way that chatgbt is going to be able to just edit all these videos anyway it did it took about 15 videos top and tailed them so top top took the beginning off took the end off made it tight so that that's just the content put lower thirds on it and then translated it into clips so i was like right give me like you know three clips

from each from each interview portrait captions burnt on and if anyone's moving like this i want you to track them you know what i mean and make it seem as though the camera's following them and i thought there's no way it's going to be able to do that and sooner or later you know literally five minutes later i've got like 32 different clips from these interviews and i've got captions for TikTok and Instagram and LinkedIn and I've got thumbnails for YouTube and I've got everything. And it literally took me, I think, probably maybe a lapped time, half an hour. So it's like for something like that, that's a perfect use case, brilliantly executed. Obviously, the question is, how do you make that repeatable and templatable so that anyone else can sort of do it? But the reason why I'm sharing that story is I kind of want to get your perspectives on for something like that, I didn't need to go and build my own custom agent. ChatGPT has that capability natively. But for this insurance company, which is very specifically tied to their data

and their systems and what triggers the agent is going to be unique to them as well. So that seems to be obviously a custom build. I just wonder if you have any thoughts in terms of where businesses can land, do land, or should land as it relates to these different types of use cases. Like what's worth building custom versus when might you may as well just use the frontier models that are out there already? Yeah. I mean, it's fascinating to hear what you said. I'm almost like I need to go to my video team. Like, dude, let's try this now. Oh, 100%. You need to get them on it because it is honestly. We've got more content from last week than we can do anything with. It's crazy. Yeah. Nice. Because they're like, oh, it's going to take two weeks. We have a dedicated video editor. I'm just going to go and be like, guys, I heard from Kane that this is possible in 32 minutes. I'll tell you also what is even better than that. I didn't ask for this. It just did it as part of what it did. It built me a HTML page in our branding, yellow banners, all of our fonts.

And on that page, it embedded all of the videos that it made. So it had the first interview. I just press play. I can watch the whole interview. next to it I can just press the button to expand on the description for YouTube timestamps for Spotify everything and then underneath the video it's got this four portrait videos that it's cut from that with all the captions on it and stuff like that titles captions are underneath that and it just literally on its own without me asking it just built this HTML page for me to proof everything from unbelievable yeah yeah anyway yeah I think that's awesome I think your point, okay, and so you build custom tooling when you're dealing with multiple systems and you need custom code to connect some of these things, right? Like, if I have, let's say, a legacy system which is built in the 80s or 90s, let's say, especially, like, mainframes, there's some, let's say, there's Tally which is in retail. It's not easy to have, like, today when you're using Cloud Cowork, I just need to authorize

my username and password for a HubSpot or a Slack, and then I connected to that tool. Whereas, it's not easy to do that in the enterprise at all. AI is the layer that's being built on top of the legacy system. So there's also like, where is all my data? Who are all the people that I need to speak to? It's a massive ecosystem. So in that sense, it makes us do customers. I thought your example, which you did, you should, if you're an SMB startup and you want to get stuff done, I mean, master cycles of you want to save time in terms of cycles like this, you should use a tool. You should find as many agent-based SaaS tools and then not hire as many people. I think that's why you're seeing this where you have 30 people, organization which is over a billion dollars, right? It's a $19 product. It's a $99 product. Yeah. But all these players, when they go to larger legacy systems, they'll have to build customers. Yeah.

Yeah, interesting. And I suppose that, as I said, you know, like if it's just me and three other team members that are doing it, it's different to having an organization where your marketing team is, you know, 150 people and all of them need to be able to use it with the same level of consistency, same quality of output. So there's a lot of other stuff to consider there, I think, as well. actually one more thing Kane which might be interesting is see you realise this about Claude it doesn't have sustained long term memory when you cross multiple threads and what if Kane wants to use this across three other people and they all want to collaborate on the same thing that's hard I mean ChadGBT is actually a bit better than Claude it will pick up on I think it's a bit better at getting stuff into its memory but sharing that with the rest of the organisation is I think definitely a bottleneck. Like how do I know that John was working on this and that Susie was working on that and actually there's a lot of shared kind of like information

and knowledge that's being sort of done there or they're both working on the same thing or whatever. Yeah, it's interesting. I'm curious and to kind of alluded to at the beginning in terms of how you approach this kind of observability and monitoring type of work because, you know, the picture you described at the beginning where someone's using, you know, Langchain and someone's using this and that and they all got different models behind the scenes. Some of them might even have built some stuff in Lysa and stuff like that. And it's like, how do you observe what's going on? How do you spot rogue agents and this, that and the other? So what are the sort of key things to consider, you know, as you're deploying these agents, what are the key things to consider as far as observability is concerned? I'm going to do, can I share my screen and maybe show you something or is it going to be an audio predominantly uh it is audio but there there is also a video if i just uh allow that should work for you now so one is this right like

when you look at this this is what okay so one is the how right now what are we tracking when you talk about observability you're tracking you should be able to first of all figure out who of the players that you want to track, right? So there's models, there's platforms, there's frameworks, there's coding agents, and then there's agents that you've built. So Databricks, there may be data coming from there, but there's also agents which are sitting within Databricks or Salesforce SAP. So you need one kind of layer to bring all of that in. So that's the first part, right? So identifying and coding agencies, There's token consumption of Cloud or Cortex or Cursor or GitHub Copilot. How do I make sure all of that's happening? Now, we've built something called a control plane, but imagine you have a readme file that reads through all of this, reads through all of these agents. And let me show you the next one, which is this, right?

We call it the control plane. But basically what we have done is you have, let's say, like a readme that follows. It discovers all the agents that are out there. And once it discovers the agents, it converts it into a specific format. Now, that's the important part, Kane. Like, imagine these are all players, right? These are all basketball players. You're measuring this. Let's say somebody's height is measured in centimeters. Somebody's height is in, like, feet and inches. The registry makes sure all of it's in one format. Everybody is in feet and inches format. Everybody's in kgs instead of pounds, right? But for agents, so what are you capturing? You're capturing role, description, soul, what are the tools it's connected to, what are its features, all of that stuff. Once it's done, then the next year kicks in in terms of your identifying what these agents do, what are their activities like,

And then there's evaluation. So you run these evaluations on these agents. And once you're, then you build the platform from there, which is how do you, how do I make, when I say running evaluations, it is, I can have test cases and I can be like, hey, this is the current output. Again, multiple test cases, is the agent still reliable? Right. And then from there, I kind of move to how do I take this into a live environment? How do I make sure this is deployed? The rest of the stuff is more once it's live, like, oh, agent gateways, not everybody should have access to the same sort of agents or there limited access LLM gateway might be is like oh make sure there remember I told you like if Anthropic fails go to OpenAI That gateway deciding you can decide for multiple tasks, you can decide, maybe there's certain reasoning tasks that need better models, maybe simple tasks in small models, so you could do that. And then it's making sure that a guardrails and observability. And this, the observability is basically telling you

what is available on the public side, internal, and private side. So some of this gets even technical for me, to be honest. But it starts telling you what's happening at the app level, at the user level, at the agent level. Okay. So that's the big observability play. I think a lot of it first is just making sure. Let me see if I can give you another good image, which could be interesting. it's all right we can put the i'll stick this link i've got actually got this web page open here actually i'll stick i'll stick this link into the uh into the show notes i think it does a good job of of kind of visualizing uh all those different all those different components um final question before we sort of wrap up what are the in your experience in terms of you know the customers and prospects you speak to what are some of the things that businesses are getting right today as far as their deployment of AI agents and agentic AI and what are some of the

things that they are getting wrong that should change Businesses like Wonderful AI Happy Robot Emma, even us I would say what we are getting right is we are making sure we're, at least us if I can speak, I don't know enough about them but everyone doing the FDE model, which is it's not like, hey, here's the platform and Lego blocks you build on here. It's not IKEA style. Bro, I'll come to your house. I'll fix all the nuts and bolts. Here's a chair. Sit down. Is it working? Players that are owning the outcomes with your customer are winning. So they're doing that, right? The ones that are doing it wrong, the ones that are doing it wrong I feel are building products where Claude can very easily start doing what you're doing

you know like especially if it's a $200 product because even if let's say I'm building an agentic workbench which is like multiple agents today it's like I find it difficult to tell people to get out of Claude if everybody has a Claude subscription if they all have Claude subscription It's multi-usage, as you know, like they're using videos, content generation, you name it. So the smart thing that you should do is actually move to like a fully, even other players, actually Harvey is doing this very well. Move to a very verticalized player, like build a brand around a specific industry, build a wedge, which is really capturing the nuance of a workflow. So if you're a legal person talking to another partner or an associate, more resonance than I want to buy from you. Like Harvey, for example, is 350 legal product managers. Right? So they're doing much better than opening eye coming out and saying,

hey, I have a legal offer. Yeah. Yeah. Yeah, absolutely, yeah. What about the end clients as well then? And so, you know, of the brands that you work with that are deploying Agentic AI in their organizations, what are they getting right and what do they need to improve on, do you think? What they're getting right is all the services, all the service players that I know, productizing some of their workflows, converting it into a brand and selling it to their existing distribution. That's super, super smart. I think everyone's doing that well. Every large consulting firm is doing that well. So I'm still surprised. I feel like some of these consulting firms are undervalued, in my opinion, as of today, as a result. What are they not doing well? I think the experience needs to be agentic end-to-end. I think some of them are making sure.

Like, you know, if I showed you a SaaS dashboard and I said it's agentic, You'll be like, bro, how is it different from... Like Claude experience gives... It's like it's nothing, right? It's a simple voice input. It shows me how the agents are working, then gives me an output. But if you give me a dashboard where I have to click on things, people feel like I'm regressing in some sense. So you want to make... You want to create the experience as much as what Claude is doing. One thing, though, which I'm so surprised that Claude hasn't done, Cain, is why is my output not multimodal? Why do I not get, like, if I'm learning something, it should give me a YouTube video with that specific index part that I can go watch the video directly. Yeah. I mean, OpenAI has kind of launched something a bit like, I forget what they called it now, but they sort of announced something a bit more like that, where, like, you know, if you were asking, you know, how does, what's the difference between a muscle and a tendon? Then it creates, like, an animation. It shows you how it all connects together and stuff like that. that's a that's a great kind of feature that like somebody did that sort of an astra 5.2 i think

which is all the bones and muscles in the body and then you could like kind of move it in three dimensions click on it see what your muscle stands for yeah that was like that's beautiful yeah it is yeah yeah the whole kind of concept of the generative generative interface i'm kind of really interested in um and also how that you know how do you kind of build them generative interface type applications and what type of UI components do you use at what point in the experience I think is an interesting sort of question. But yeah, no, this has been brilliant. I really appreciate it. I think it's, yeah, it's really interesting. We spend a lot of time talking about kind of customer experience and conversational based sort of AI. I think this conversation has been really interesting to get a look at the other side in terms of, you know, how are businesses approaching the harder challenge really, which is automating the back office processes, you know, because that's where a lot of the challenges are. Data, as you mentioned, legacy systems, stuff like that. And also the things that they have to do manually,

like the video editing and all that kind of stuff, how AI is being used to sort of improve. That's what's been a really interesting conversation. I appreciate it. Yeah. And since you run a podcast, right, Cain, I think there's such a good offering here, which is imagine I'm on Slack and I'm like, at Cain, it's like, hey, what all podcasts should I be, looking to get into and it like finds all that information automatically sends the email like hey this is good i want to come live schedule like what is the entire podcasting workflow productize and then you give it out to people as a as a flat fee right because it's better than i mean they might be that might be an interesting space for you to explore also yeah yeah i think that's a good idea that i know and also the the things that i'm sort to work at the moment is once this podcast is done i'd be i'd worked a lot in the last few years of getting it to a point where i can start a podcast in five minutes so for this podcast

literally it was five to four i've got the cameras are on rigs that go up to the ceiling i pull the cameras down turn them on open zoom and all the gear is ready to go i just press go and it's done so it literally takes five minutes to set up i've kind of tried to lower the barrier or the time it takes to start what i'm sort of working on now is decreasing the time it takes to finish so once this once you press stop what happens then you know get the asset get the audio edit the audio edit the videos chop up the short clips get it all distributed all that sort of backup back end stuff that i think there's there's quite a lot of merit to you know like i mean it's something that we've been trying and not not with any degree of intensity but something that's been sort of on the on the radar for a while is how to streamline that workflow and i think there's value in that i think the only thing is with stuff like that is that you know i can imagine that that's there's tools like munch and these other sort of tools that do those like identify the clips and all that kind of stuff so there's already sort of things out there that do a bit of that but when you package it i think as a podcast end-to-end workflow i think that's where it

gets quite interesting you know you could almost have it as you could potentially even host your podcast through it, you know. Yeah. The podcast also. And also if the setup, right, like what you have is a mic and this background and stuff. Like now it's almost podcast is a good way to, the way I look at it is it's high effort, but you can really squeeze the juice and repurpose thing. The way we use the podcast is we record those clips amplified using ads, right, like specific audiences. Even if you were like enterprises like us might, or not, sorry, startups like us might like dude I need someone to help me set up this space so end to end could also be like the physical experience too yeah yeah that's true good point nice one well I appreciate it it's been really good hasn't it it's been really interesting we'll stick that link to the OpenController and the Liza website in the show notes we'll put your link in there as well if people want to reach out to you and connect and yeah definitely keen to keep in touch and see where this kind of stuff goes

I think you've got a really interesting proposition there. Very diverse in terms of the kind of offering. So it's kind of interesting. You're kind of involved in all parts of AI, really, which is really interesting. So, yeah, I'd be definitely keen to see how it goes. Yeah, and thank you for having me. That was really kind of you to schedule time with us. And, yeah, happy to help, happy to push the word out whenever you need help. Nice one. Appreciate it. Thank you so much, and thank you all for tuning in. We'll see you again on the next one. Thanks now. Bye. Thank you.