Customer Experience Superheroes - Series 13 Episode 4 -AI Powered Customer Support - Devashish Mamgain

The topic of AI in business, is ringing across all functions. Customer experience is no different. But just what value can it bring, and what governance is needed to ensure gains?

To find out how AI is making improvements in customer service CX Superheroes host, Christopher Brooks meets with Devashish Mamgain, CEO and Founder of Kommunicate. Devanshish shares his of wealth of knowledge on AI and the journey he has taken with Kommunicate to arrive at creating better outcomes for clients and their customers with AI in customer support. 

Hear how what matters most to customers is the priority of AI too. And also appreciate from Devanshish how to govern AI within an organisation to ensure its potential is achieved. 

We hear how AI is making a meaningful difference in customer support. Devanshish generously shares the learning from his journey, on this topic. If you enjoy this subject and want to know more, contact Devanshish on https://www.linkedin.com/in/devashish-mamgain-1a639320/. 

Transcript

SPEAKER_01 0:08

Hello and welcome to the latest episode of the Customer Experience Superheroes Podcast Series. In this series, I Christopher Brooks am your host, as well as being the MD for Lexin CX. We share with you the ideas, the inspiration, and the insights from the many individuals that cover the gambit of dynamism, which is the world of customer experience. In this episode, we will introduce you to Deva Shish, who, as the lead at Communicate, is really pioneering some of the areas of AI in customer service. He'll share with us some of the technological developments and the improvements he's making that are helping those companies in the world of customer experience. We'll also talk about some of the considerations, concerns, and better practices that need to be employed if you're embarking upon engaging with the AI to improve the efficiency and the effectiveness of your customer service operations. And we pause to reflect on what the future for AI may well bring. So without further ado, let's go over and carry on the conversation with Devishish. So I'm here uh today with Deva Shish. Hi, Devishish, how are you doing?

SPEAKER_00 1:22

Yeah, I'm doing good, Christopher. Excellent.

SPEAKER_01 1:26

We're really excited. I mean, obviously uh AI is a topic on everyone's lips, but you know, you guys are communicating are looking at it in in quite an extraordinary way. So um I want to really understand a few key areas I think we're going to get into. There's I know something you're very passionate about is the responsible use of AI, but also some of the innovations you guys are getting up to is quite incredible. So I look forward to talking about those with you. But before we do, I think it's always helpful for our listening audience to get some appreciation of where you've come on your journey, how you've arrived where you are now, and and perhaps where you started. You know, where where where was the flame ignited uh for customer experience? So if you could give us your plotted path, we really appreciate it.

SPEAKER_00 2:07

Yeah, sure, Christopher. So my name is Evasis. So I am a computer science graduate. So I'm a techie. Uh, and uh so I have worked with a couple of startups in the past, and then you know, I also always wanted to build something of on my own. So we uh you know built a uh texting app uh you know back in 2011. It was a you can text from your computer screen, you can use your Chrome and then send a SMS. So that's how the journey started, where I was doing something around the communication tools, and then in 2015 I started a company, Apologic, which was a chat API-based company, and that time everyone was uh integrating chat into their products. Um, so that's how I got into the chat space, where we were enabling businesses to MB chat product within their application, like buyer-seller chat, teacher student chat, and a lot of other use cases. Um, so while doing that, I realized that the major use case of chat is actually happening on the customer service space. And that's how I got to the customer experience state where there is a user interaction happening, which is ultimately leading to the improvement of the customer experience, right? Because ultimately the customer service angle comes from there. So that's how I got into the customer experience space. And by building App Logic, I have worked myself as a support agent for a long time because we were a small team and App Logic was more of a developer product. So I had to get into the customer support and answer the customer queries daily. Um so that's when I the aligned lot of prostation as well of answering the same questions again and again, and you know, at the same time be helpful to our customers. So that's how after AppLogic got acquired, then I went and started in the company with my co-founder and others, communicate. And there's the goal was to enable the customer experience uh and bring the AI chatbot into company's product so that they can have the automation in place so that the support agents workload is reduced. Uh the hand users are happy because they're getting instant help, and that's how overall the customer experience is improving. So that's how I got into this space.

SPEAKER_01 4:31

Excellent. And and just give us some flavors in terms of the clientele you have that are finding this as an opportunity, they're they're exploring. So which sort of clients do you have?

SPEAKER_00 4:42

Um, so we have clients in different industries and different geographies, majorly in the America region. Um, and we cater to healthcare companies, e-commerce companies, education, universities are there, uh, fintech companies. So some of the clients are like MGen, which is a biotech company in the US. Then we have insurance companies like Egypt Life from India. So these companies, majorly where there's a high volume, so those are our ideal customers. Whilst where there's a high volume, that is the place where they want automation to come into picture so that the their team workload is less and their customers are able to get instant help.

SPEAKER_01 5:20

Okay, so that's interesting. So so actually it's that high volume that's kind of the common denominator. Geography and industry parallels uh are are not so important, I guess.

SPEAKER_00 5:32

Um, yeah, for us, the you know, US geography is working really good because I think there's a lot more acceptance in terms of trying out the new technology like AI. Um, so that's what we are seeing, although other geographies like India are are also getting into that. Um we have uh you know many customers from India as well, but the adoption rate we are seeing very high in US as compared to other geography.

SPEAKER_01 5:57

Okay, that's that's interesting. Yeah, excellent. And and you know, how many of you are there there now? I mean, it's it's started as you know, uh you you're a co-founder, um, where you you say, you know, you're answering the phones and and making the lunches and uh you know everything else. What what sort of science is it it needed now to run the operation?

SPEAKER_00 6:17

Uh yeah, so right now we are you know you know try to be very keen. Um and uh so uh you know we are like two two co-founders, and then then the majority of the team is on the tech side, building the product, uh, you know, creating AI models and all. Is that uh you know what the question was?

SPEAKER_01 6:35

Yeah, absolutely. And and how did happen? I mean, obviously, when it's the two of you, you're just answerable to each other. You've now got a large team that you're you're you're responsible for. Have you embraced that change as well?

SPEAKER_00 6:48

Um, yeah, so you know, there now there is a you know set of responsibilities that we have to pass on to the team team as well. Because initially, when we were only a small team, there were a lot of things we were doing, and now we have a set of uh customer support team that takes care of the customer queries. Of course, we also get involved there as and when required, but then there's a lot of work delegation, you can say, uh roles and responsibility delegation that we had to do when the team you know scaled.

SPEAKER_01 7:18

Yeah, which is which is I mean, it's it's something very interesting, I always think for entrepreneurs when they they start to scale up and you know, they've got to not not dull their entrepreneurship, but they've got to uh embrace a managerial responsibilities as well. I guess it's like it's uh you perhaps you never think about when you're starting up that you know I will have to become a manager at some point. I need to people and develop capabilities and give them the opportunity to shine as well.

SPEAKER_00 7:45

Yeah, right, definitely. And then you know, there's a lot of responsibilities and ownership now with this path on the control team, and they take care of interacting with the customer, you know, solving a lot of problems. And in some cases, we also get involved. Um, right. So there's like a lot of learnings we had on you know how to do that transition. Yeah, because because it's really important to take care of uh, you know, the customer requirements, customer needs, as well as there are like a lot of guidelines as well, uh, you know, especially around AI. Um, there are like a lot of guidelines that we have to write down, uh create SOPs, and then hand it over to the team.

SPEAKER_01 8:25

Excellent. So what we'll come on to those um those guidelines in a little bit. But I guess what's very interesting is obviously you're in the business of helping to improve outcomes for customers, make um the engagement between the customer and the organization more effective, and you're using a a technology to do that. But your you know, your value must be judged on how good the experiences they have these clients have with you. You know, if you if you don't deliver a good experience, how can they trust that your solution will deliver a good experience? So do you have to work on that? Do you have to be conscious of the fact that the experience you provide to clients is is an important indicator of how good you are at customer experience?

SPEAKER_00 9:08

Yes, and definitely. So uh everything uh you know in the product goes back to the customer experience. Because if uh see, because it's not only about how fast the bot can answer uh to the user, it's also about whether the answer was really good and the end user is happy, right? Um it's not only about the speed, but it's also about the accuracy and the way the answer is given. Uh, so there's like a lot of factors there. Uh so we actively track uh the CSAT, um, the kind that the end users give the CSAT after their chat conversation. Uh, and that is the indicator for our customers as well, on whether their CSAT is improving with time or not. Like we have some customers where the CSAT improved by 40% after they enable the AI-based platform into their customer service. So that's uh your indicator for us.

SPEAKER_01 10:02

Yeah, very, very impressive. So you mentioned about guidelines there. So the first area I'd like to get to, we we we are very um conscious when we're talking to um clients and to organizations, that there is a hunger and an appetite to consume AI and deploy it at real speed, you know, kind of start you know, using our half-built learning platforms and let's start optimizing AI and let's start gaining some efficiency. And I was reading, um, as I mentioned to you just this morning about some law cases coming forward where you know the data sets haven't actually there's no authority to use the data sets and it's kind of privileged information. How do you go about that in the first? Because I'd imagine you must have clients who are very eager, very keen to optimize the benefits of it. But you may just mention yourself kind of guidelines, the governance, the the responsibility. What is your take on it and and how can organizations be more responsible when they're thinking about AI?

SPEAKER_00 11:08

Yeah, right. So you do whenever implementing AI, the main thing uh in AI is what is the data that you're using to train it. Right? If the data is very less or if the data is biased, uh, you know, no amount of training is going to help, right? So whenever we are you know working, working with the customer, the first thing that we check is you know whether that data that they are training it, whether it belongs to them or not. Because that's the first criteria, right? And then whether the data is sufficient for the training purpose or not. Right? If the data is not you know sufficient, then there are other ways to go about it using um you know some other form of bot creation. Um, so you know that's what we we go through the customer, understand the type of data they have, then we manually review the data as well uh to to some extent so that we can identify whether there are biases in the data or not. Um so to give you an example, let's say there's a data of uh you know certain diseases in in you know different countries. Okay. Now, let's say for one country the data is very small, and for another country the data is huge. Right? So, and if a user from the country where the data is very small comes and asks a question, it might not be that good as compared to where the huge set of data are. Right? So, so that's the data, you know, discrepancy. Uh, because there has to be a minimum threshold, minimum set of data for each type of segment. So those things we then inform the customers on what are the gaps in their data, and then we ask them to collect those those set of data before it gets to the training uh feed. Um, so these are some of the things that we take care, and then we also take care of the personalization because many times what happens is a user asking a question, the answer might be different than the answer which is for some other user. Uh, to give you an example, we are we are working with a very big company in India where they are also using it for their HR portal. Now, if a user asks a question about uh, let's say reimbursement limits of different employees, now different employees are working on different designations, so each one has a different limit. Now, if a user is asking a question about what is my reimbursement limit, right, for for certain benefits, different employees will have different answers. Now, if the if the bot is not aware of the user, then the bot answer will not be correct, right? So the the AI system is needs to be aware of who that user is they are talking with, and all their attributes which are finally linked with the training data that is provided. So these are some of the things that that we you know take care uh while working with our customers.

SPEAKER_01 14:13

I mean uh talking to somebody the other day and saying that sometimes it gets oversimplified, you know, there's a sense of we've got data, we've got thing called AI, and that would enter it. You know, and it may sound very good at kind of a board level, but when you get into that, you're actually it's a lot to do with you know, kind of the data selection, the quality of it, that period that period of kind of engineering to make sure it's cleaned and it's in good shape. Then the the the modeling that has to take place before you then start operating. What where do you see in that process as the you know the the areas that perhaps are um undervalued and the areas where you can really fall short in terms of the capability of what you're gonna get from your AI?

SPEAKER_00 15:03

Um yeah, so in terms of you know, uh like undervalued, uh in terms of that, like many times uh you know, people don't pay much attention to gathering that much of data because gathering the data is a time consuming to us, and then people have to, you know, get the data maybe from different departments, different sources, and all. Um, so I think I think that's where uh you know one place where people undervalue it. And nowadays, after looking at Chat GPT, uh the expectations are very high. And many times, you know, people think that okay, uh Chat GPT is able to answer everything. Why do we have to provide people that look data? Right. So I think I think that that aspect uh you know gets gets undervalued a lot. Uh yeah, in terms of you know, uh things that companies can get uh most, um, you know, like there are different areas beyond uh beyond simple interaction. Um so companies can get done a lot other than simple question-answer thing. They can get certain work done by the um and if they are able to clean their internal services, uh, right, so they can achieve like one is where you are using AI to get the information, another is you are using AI to do certain actions. So I think that part is right now, you know, where there is some work happening. Um, and you know, in the coming years, there will be a lot more things happening. You will see a lot of co-pilot, uh, you know, where you can hire a sales agent, hire a customer support agent. So I think that part is, you know, uh yet to be seen. Um it's it's an ongoing thing. Uh you know, a lot of companies are working on it, but the true value will come when actions will happen and not just information retrieval. Sure, sure.

SPEAKER_01 16:57

I mean for me it it there is, and I think it's probably the same in you know in a lot of area businesses. But I was um I'm quite a bit older than you, Devishish. So I remember kind of the original arrival of websites and um when they they there was an explosion, and it was almost like people were building these really tall towers, but hadn't really thought about the foundations, and then it was too late, so they just crumbled. But then they fought the next time they realized we need strong foundations and they stuck. And I wonder what's your your your observation. Do you think AI is on a bit of a roller coaster, or are we going to see it fail before it picks itself up and succeeds?

SPEAKER_00 17:37

I don't think AI is going to fail. Every year you can see it's going to unlack the and now we can see the AI is able to do voice conversation as well, and very seamless, very past, very uh, you know, real. Um, so I don't think it's going to fail, but yeah, there is one thing um that is very much required, and I'm sure it's going to happen the coming years, is right now the AI is relying more on large language models where you need a huge set of data. And so I'm sure something will come in the future where we would probably not require that huge set of data for training or creating the LNM models and all. This is like a huge requirement, right? You need so many GPUs, uh, right? You need that first, you need that much data, then you need that many machines. Yeah, move with your own model, right? So I think it's a high time the industry will definitely work on um so that we can have a small language models, but in terms of effectiveness of the same level, maybe it might come for specific use cases, specific domain areas, but I think that uh will happen.

SPEAKER_01 18:49

Okay, so okay, that that makes perfect sense. So it'll become much easier to work with a smaller or more specific data set. Um, one of the things that um I'm conscious in in the UK, we're going through um a general election at the moment. And I know uh similar things have happened in in France, um not too long ago, Argentina, South Africa. One of the things that doesn't seem to be on the political agenda is is the governance uh around AI. And I just wondered what your view is. Is it something that as a society we're still trying to figure out, do you think?

SPEAKER_00 19:25

Um yeah, it is very much required. And uh, I know some of the countries have already started planning on this. Um, because see if the AI is not taken into consideration all the regulations and the compliances, uh, then it can go out of hand. Right? Uh a good person will think from all the positive use cases perspective, but we don't know what all things can happen with AI. And then there are cases where there's a data privacy concerns we are already saying, because if the AI is getting trained on some data which is copyright data, that's again a problem. And if the data is biased, um, that's again a problem, especially in healthcare. Uh, if if the AI gives a wrong position to a patient, it it could be life-threatening, right? So there is definitely uh governance required on that. Um, the proper compliance framework needs to be placed. And it's a good thing we can see that some companies like Google, Microsoft, they have uh created certain set of responsible AI guidelines, and and that's a very good initiative from these companies. And I think all the big giant will have to work closely with the government and get the guidelines and everything in place so that there is not any negative impact of this.

SPEAKER_01 20:47

Yeah, I've seen in other sectors things like I think it was in Portugal, they uh adopted visas um governance for for um uh automatic payments because you it didn't have anything. And I and I wonder if governments will use some of the you know the superpowers in this space as kind of their starting point. And and it's encouraging to hear, as you say, you know, people are you know thinking about it, considering it and and and developing it as they go forward. Um I mean obviously so we've spoken about the responsibility, we've spoken about kind of the the practical practical nature of it. Um one thing you've just well you've you've constantly given us during this podcast is an appreciation of the importance of um expertise. And when I say expertise, it's kind of broad expertise. And I say this with you know, we we can be uh honest about this. I think in some organizations there is such an excitement around this topic that you almost you know you wrap four walls around it and say we're gonna build our own and we're gonna just do it ourselves. Obviously, that means that you keep all the intelligence inside and you kind of don't show the world what you're doing as you go. But from my perspective, it feels like There's an inherent danger in there. It feels like this is something we should share and develop together. I don't see it necessarily being a differentiator, significant differentiator. I think it gives you an advantage, obviously. But it feels to me that organizations would benefit from being in partnership with organizations like Communicate so that you can bring your world knowledge as you're working with different partners and different clients into every single project. Rather than, you know, working within a company, putting your head down and saying, this is where we're going. And regardless of how things change, it's too late because you know our trains left the station. I mean, it's easy for you to say because you do sit on the outside, but is there is there ever an argument to say let's just keep it in-house? Or do you think there's always a value of having some external partner to help, you know, keep how how rapidly this thing's moving, keep an appreciation of what's going on out there and bringing best practice in?

SPEAKER_00 22:57

Yeah, so uh I think um you know keeping in-house is only when there is a very confidential work going on or you know, very long, you know, research work. Um, otherwise, I think it's best uh for you know customers to partner with you know someone, uh be it Communicate or you know any other player in the market, uh, to you know partner up because the people who are working on it every day, right? They they get a lot of feedback from all the different you know customers. And there's like there are various problems there, right? Because some customer gives one feedback, another will give some other feedback. So uh the companies who are working in this space they consolidate all that feedback and do that improvement, all right, as compared to you know one company who is keeping everything private and then you know doing a limited sort of you know improvement, right? Because it requires like um dedicated time. And uh, yeah, if if some company has like whole bandwidth available, great. Um, and but but what I've seen majorly is uh you know most of the companies they uh try to focus on their core business, uh, you know, rather than reinventing what um what the rest of the world is throwing. Um, so so you know that's what I've seen. Some places where I've seen customers requesting us about the data, where the customers had a concern around the data and they did not want it, the data to go out of their country. Uh, right. For example, we work with with NFC Live, uh, so it's censored company in India because it's censorance data, so they don't want the data to go out, yeah. Right? So so for those sort of uh you know cases, we actually do uh you know deployments in different regions, so their concern about data is taken care of by deploying servers in India, right? So for so similarly, we have worked with some European companies where the their uh you know yeah, models are deployed in universe. Um, so those kind of you know concerns as well come for the data, but otherwise, ideally I my recommendation would be to work with companies who are working in the customer experience AI space and partner with them and move things fast because AI space is moving really, really fast.

SPEAKER_01 25:23

Yeah. Okay. I mean, I think that's that's salient advice. I think it makes perfect sense. We find, you know, the work that we do as consultants um advising and supporting clients on their kind of customer experience strategic word work and the the development of customer experience management solutions. Um we find you can go down a rabbit hole and you know become a a little island if you say no let and just give us time just to develop some understanding and capability of this. And it feels as if that will be so much richer if there was an external partner who's able to kind of coach you and support you and develop your capability so that you don't create something only to find out it's now obsolete, you know, and it's been been. But I think I mean it's it's easy, you know, if you call it kind of a pitch for communicate, but it's not. It's actually a plea to organizations to say, as we have found with other areas of of um of rapid development, find partners you can trust and rely on who are gonna, you know, learn with you as opposed to learning on your own and then finding the leadership team say this doesn't work, this is too expensive, I can't see the return coming through. Um thank you for that honesty in that space. I really appreciate that. Um, I mean, it this is an impossible question I'm gonna ask, but uh so I'm gonna put a timestamp on it. You know, we're in July 2024, but what comes next for AI? Because I realise, you know, this will probably go out within a month, and we may be very out of date by the time we get there. But you know, really, if you think about the short term, what what are the next steps that you know AI is gonna go through? Are we gonna see it just being focused in this area of customer service? Or I was um at the privilege of being in Monaco a couple of weeks ago and was hearing how it's being used um by some military forces to kind of you know level up uh you know you know that their their abilities. So where where do you see it going?

SPEAKER_00 27:18

Yeah, so one one thing is um AI is going to go from just the information retrieval to doing certain actions, and we will see a lot of co-pilots in the market, uh, where the co-pilots will actually perform certain tasks. Okay, be it writing an email, be it downloading something, um, be it finding prospects or anything, you know, whatever it's like replacing the jobs that human beings were doing, and the AI will do that jobs uh as opposed to just finding the information. So that is definitely going to happen. And uh I think a lot of things going to get into voice and video. Um, as you know, we can see in the latest you know, OpenAI's launch as well. Um, you know, a lot of uh interest is coming on the voice. And uh yeah, and you know, one of the things that we you know discussed earlier is a model that can work even with the next set of data, but have you know greater accuracy. Um, so these are the three things that I um and that I'm sure the great market will go towards.

SPEAKER_01 28:26

Really interesting. Thank you for that. I mean, we've we've looked at AI probably from the company's perspective, but let's just dwell on the point you just made uh earlier around employees. Um, and you yourself have recognized this first hand, you know, the repetitive task being taken or what um what what do you what do you foresee employees are going to be able to do with that release of resource? I mean, there is potentially uh a regrettable kind of you know cost saving that could happen, but those who are perhaps thinking more um strategically, how do you think organizations will be able to utilize this this time from their employees that they get back?

SPEAKER_00 29:04

Yeah. So if we are talking about, let's say, in terms of customer service, uh, you know, the market that we have VD, so what we are uh you know looking at is um the support agents are going to get free with the repeat event boring work. Uh it's not going to 100% eliminated, but in 90 to 95% is what the workload will be opt in sold up, right? Uh and then these support agents will be relatively free to do the more complex or more customer personalized relationship work. So they are going to move, you can say, from a traditional customer support answering system to a more personalized customer relationship thing, right? So yeah, it's less about the cost saving, but it's more about now they have a workforce that can build better customer relations, more personalized customer relations, and grow the company further, generate more revenue. It's more of a revenue enabler as compared to you know, you know, cost.

SPEAKER_01 30:04

Excellent, excellent. And then the other perspective from the customer's perspective, yeah, I've seen interesting presentations on the level of adoption from kind of digital native versus people like me who are digital immigrants. Um, but obviously it's gonna get more sophisticated. And what I did see was a really interesting piece that um humans will lie to AI more more likely than they will to humans. And you know, we we will become, as we always do, become trained in kind of spotting when we're talking to uh an AI versus talking to a human, if indeed it's us or if it's not our own AI doing it. But um, from a customer's perspective, you know, what what are the perceived benefits that the customer is going to be able to really value the AI being deployed effectively?

SPEAKER_00 30:52

Yeah, one is the time to respond. Um, so if there's a AI deployed, there's no no waiting time. Um, the customer can get quick answers, uh, you know, many times even faster. Uh, you know, even nowadays we are saying even the complex queries get you know answered faster. So that is definitely one. Um, and at the end, the customer is uh, you know, in terms of customer service, a customer experience, the customer is basically looking to get an answer, right? Uh, it doesn't matter whether the answer comes from an AI bot or a Moong Bing. So that's the biggest benefit. Plus, it's available 24 by 7. Um, because many businesses they have teams working in the day shift. So at night time, if you need help, there is no one there, and then you have to fall back to the email ticketing system, right? So that is a great um, you know, in help for the end users that they don't have to wait for the next day, right? They can get instant instant answers 24 by 7 and they can get it uh on the tap of you know uh few buttons one year on the mobile.

SPEAKER_01 32:00

So and and obviously, I mean what we've seen over the years is it gets harder and harder with digitalization for brands to get their identity to come through? So if you you know, if I'm dealing with um interacting with Ferrari AI or Disney AI or Walmart AI, are organizations starting to kind of consider the the brand language in in their large language models? Are they are they bringing the narrative and the tonality that they will use that others won't and decisions that they will make as that brand and that perhaps others wouldn't? How how are they fusing this in to make sure that the customer still receives that kind of what feels like a branded experience?

SPEAKER_00 32:45

Right. So uh, you know, uh like with tools like OpenAI and all, there is a way you can set the tone, you can configure whether you want uh you can set some prompts, you can say, okay, reply in a professional tone, reply in a friendly tone. So that options are there, and I think brands will you know utilize them. Some companies are already within them. So prompt engineering is going to play a good role where you have to write really good prompt to uh you know answer it in a way your brand will do. Uh so that's just uh you know, so that is going to happen for prompt.

SPEAKER_01 33:23

Excellent, excellent. I mean, I'm really uh interested in that space because uh, you know, I just we work across many um different uh verticals and with different types of audience, and you've got obviously, you know, in in most uh most countries now and in most verticals, that vulnerable customer who you know needs to be dealt with in a specific way so they fully comprehend the information. Um so it'd be really interesting to see that go. Well uh um Devishish, I've really enjoyed this conversation. I think you've demonstrated you know, you are the man to go to if you want to kind of consider you know what do we do next with our AI. If people want to get hold of you and communicate, uh what is the best way to do it?

SPEAKER_00 34:06

Uh yeah, so I'm available at uh devastish at the rate omnigate.io and also uh you know we can reach out to me if you click on the chat option on the OmniGet side, you can just say I want to talk with diversity and the bot will hand it over to me.

SPEAKER_01 34:22

Of course, of course, excellent. Wonderful. Well, we'll include that. Um for those of uh our our listeners who want to have a chat with you. I guess you're always open to questions that could be posed, and you must be mindful of information on what not to do as well as what to do. So we'll include your details in the description and and and they can reach out to you. Well, I I personally, this is the the the the second time we've interacted with with your your organization, and um it it's very exciting to see where you're going. But I I really respect the fact that you're doing it in a responsible way, which is most admirable. So I wish you uh and the rest of the organization uh a very kind of healthy and and progressive growth. I'm sure the next time we talk, there'll be 200 of you. And you'll be sharing with us again, kind of you know, where where AI goes next. But for now, thank you so much for your time and for sharing your knowledge and being so open with your answers and really look forward to seeing where this space goes next.

SPEAKER_00 35:18

Yeah, thank you, Christopher, for having me here. And it was great you're interacting with you.

SPEAKER_01 35:23

Brilliant. Thanks ever so much. Take care.