Inside Responsible Annotation: Neurodiversity, Quality, And Ethics In AI

Want AI that works the first time instead of the tenth? We sit down with Andreas Schachl, co-founder of Responsible Annotation Services, to unpack the quiet truth behind reliable models: ethical, high-quality training data produced by people who take clarity and precision seriously. Andreas shares how a single internship sparked a company built around neurodivergent talent, turning data labeling from a churn task into a strategic advantage.

We walk through why annotation isn’t going anywhere, even with foundation models and smarter tools. When you’re training on private, business-owned data across text, images, audio, video, and LiDAR, you need a human in the loop and documentation you can defend. Andreas explains how his team co-authors rigorous annotation handbooks with clients, translating fuzzy goals into exact rules, edge cases, and review procedures. The payoff is real: higher consistency, fewer iterations, and a clear compliance trail for regulators and auditors.

Bias mitigation becomes a practice, not a promise. A neurodivergent lens exposes hidden assumptions and pushes for instructions that are unambiguous and testable. We explore practical systems—daily stand-ups, structured chat, and even “coffee calls” with agendas—that help people do their best focused work. We also confront the ethics of the global annotation supply chain and outline a different path: EU contracts, fair wages, social worker support, and leadership that values diligence over hype. From 2D images to complex 3D point clouds, we show how modern tooling plus human judgment builds AI you can trust.

If you care about responsible AI, data quality, and making models perform sooner with less guesswork, this conversation is your blueprint. Subscribe, share with a colleague wrestling with training data, and leave a review with your biggest annotation challenge—we’ll tackle it in a future episode.

Chapters

• 0:00 Origin Story Of Responsible Annotation
• 3:40 Why Annotation Still Matters
• 6:00 Private Multimodal Data And Quality
• 9:15 Building A Neurodivergent Team
• 12:25 Structure, Handbooks, And Coffee Calls
• 15:30 Bias Mitigation Through Clear Instructions
• 18:20 Tools, Human In The Loop, And 3D Data
• 21:05 Responsible Vs Exploitative Supply Chains
• 24:00 Ethics Over Hype And Choosing Projects
• 27:30 Europe’s Diligence And Sustainable Growth
• 30:05 Consumer Choice And Ethical Vendors
• 31:30 How To Support And Get In Touch

Transcript

Origin Story Of Responsible Annotation

Debra Ruh 0:00

Hi everyone, welcome to AXS Chat. Neil is not joining us today because he's a lucky dog and he got to go to Austria to the Xero Project Conference. And our guest is from Austria today. So they're two different things, but we have Andreas here today, and he has a company that's called Responsible Annotation Services. And Andreas, we love what you're doing. We love what you're doing. And you're not a disability inclusion company or any you are actually focused on this and it's a very powerful, but also at the same time, do have a diverse workforce, which we appreciate. So let me turn it over to you so you can introduce yourself.

Andreas Schachl 0:37

First of all, thanks for having me. Really more than happy to have this conversation with you and also let your audience know about what we're doing. So my name is Andreas Schachl. I'm one of the two founders of Responsible Annotation Services. And typically when I introduce my company, I'd like to tell a very short story because I think this pretty much nails down what we are, why we are, what we are, and what we do. So a close friend of mine eight years ago had the task to introduce machine learning to a medium-sized Austrian company. And he quickly realized that for machine learning to work, he needed training data, annotated training data. And as it's not uncommon, I would say, the first kind of people that had to work on annotating the data were the interns, were people that didn't have anything else to do, and so on. And they did not really like that work too much because annotation is, you perhaps know, the work to kind of add information to, for example, picture, so that the machine can learn from it and then learn whatever the target of the project, for example, is. And while he was busy looking for new for for people helping him with the annotation, he had a talk with his neighbor, and the neighbor had a son that just became 18, and her neighbor was a bit in despair already because it was really hard for the son to find a job because he was on the autism spectrum. And my friend explained to her that he had some sorrows at work and he was looking for people that would be able to do the manual task of annotating data, and it was bounding boxes, so that's rectangles that you draw in a picture to mark the area of interest, and how hard it was fine to somebody who likes to do that. And the neighbor told her that the son liked to really to focus on things on nitty-gritty details of things that others did not really find interesting, and suddenly it made click in his head, and he said, Hey, would your son like to do an internship in my company? And I'll show him what annotation is and he could play around and try that out. And long story short, they tried it out, it worked perfect. The son of the neighbor loved what he did. He produced results that were just great. He did that in a quality and over a long time that there was exactly the quality that they were looking for to train the model, and it was a big success. Out of that, an NGO was created, that is Responsible Services. And out of the NGO, a couple of years later, my business partner, close friend, and I have founded Responsible Annotation Services, and that's what we are doing now.

Why Annotation Still Matters

Debra Ruh 3:19

That's the very short version, but I'm sure we will be getting into the details in so Andreas with the the NGO, it started with the NGO, but then y'all realize this this is really something that needed to be a business. And that happens. We you see often things being born out of university or NGOs and stuff. That's very cool. That's very cool. I was looking it up before you came on air, and I was just because I thought it was so interesting, reminding myself just about annotation, because I think a lot of people think things like that are just going to go away because of AI when actually this is the we cannot have annotation go away. But one thing that I I really liked about what I my investigation was this question, what makes annotation responsible? And I know we talked a little bit about that, but why does it matter? I mean, it is so critical why it does matter, but why don't you tell us? Because why does it matter?

Andreas Schachl 4:18

Exactly. I mean, perhaps first to address what you just said, I also don't think that at least in the nearer future, meaning the next couple of years, at least, annotation won't be going away. But I think this is one of the most common misunderstandings about AI that many people out there still think that I is something that is bootstrapping or pulling it out of with the on their like on their own hair. I don't know what the English saying is for this one, but in German we have something like that. But in reality, for an AI model to really properly work, you always need human workforce preparing data and therefore training then the AI on that one. There are, in the meantime, there are tools that can assist humans in preparing the data for the training, but these are only tools to assist. But in the foreseeable future, I don't see that this is something that machines will or that AI will be able to do on their own, as in you will just have an AI say, please learn from me what is a cat, and I'll show you some pictures and you tell me where the cat on the picture is. It won't be working like that anytime soon.

Antonio Vieira Santos 5:27

Why it's I think it's important. I think it's important to highlight a difference is that we know Chat GPT and other tools like that. We're being trained on public data. I think it's important to clarify what type of data is being trained here because we are talking about business data, that data that business owns, and that data is private, it relates with the business, and that data will never be made public. I think it's important to highlight that somehow that's what your company aims, right?

Private Multimodal Data And Quality

Building A Neurodivergent Team

Andreas Schachl 5:57

That that's a very good point, actually. So, yes, one of the reasons why LLMs like ChatGPT are so good is because they have more or less unlimited sources of data to be trained on. But in our projects, we are typically working on restricted data that the company would never make publicly available on the internet, or so. That's the one thing. The other thing is we are also talking about multimodal training data, meaning we are not only talking about text, we are not only talking about images, about audio, about video, about LIDAR data, which are the point clouds. So you have many different aspects that could be used to train a model. And of course, the more different aspects or the more different types of data you have for training, if you want to build something that should be universally usable, then the more you have, the better. But not only the more you have, the higher the quality the data it is. Because I jokingly said from time to time, I wonder how many AI projects in the meantime have been marked as failed, where not the AI was the problem, but the training data was the problem. Nobody took the time to think about what should the AI be doing, what do I need to prepare in terms of data so that the AI can learn from it? They only just saw then I'll I'll provide that input, the output is bad, probably the AI is bad, let's scrap the project, let's start all over again, whatsoever. But the crucial part of the data preparation, the data selection, the data annotation that is often overlooked, and that's why we can excel at the level that is just breathtaking. Simply didn't have one project yet where the customer did not tell me at one point we're just so excited by the quality that you can provide. We never thought that this would be possible because we are putting the level of detail into it. People are totally standing behind what they are doing because they see this is finally something that they can get self-esteem from. It's not that they are kind of in a charity anymore, they are just working in a business. It's a business. We are not a charity as an organization, we're a business company, we are providing our service like many other businesses provide their services on the free market. We charge the same. It's it's we're not the cheapest ones, to be honest, but we are definitely the best ones. That that's the point, and the quality that you can get from us in the annotated data is more than worth the effort because you have to then not have three iterations, five iterations, ten iterations. It just works. And if it doesn't work at the first time, uh at the like iteration zero, it works in iteration one already, and and that's what our customers totally like about us and why they are super happy. Plus, the other thing is so my team, we currently have a team of four people who are all neurodivergent, mostly in the autism spectrum, and the input and the feedback that they can provide with their kind of different angle on instructions, how to annotate, what to annotate, also with their different angle on what the data is, what is missing in the data. That's something that our customers also value a lot. Because it's it's not like, yeah, um, I mean, in in software engineering you have this kind of throwing things over the fence because somebody else will collect it and will try to make something out of it. That's definitely not what we are doing. We are always having a close look at what the ask is, what we need to do, and we provide that as a feedback, and that is also seen as very valuable.

Antonio Vieira Santos 9:50

So you started the podcast with the story, how everything happened with with with uh with the young autistic intern. But today, how do you integrate new neurodivergent employees, collaborators within the organization to make sure that they are productive and they do what they are supposed to do, like anyone else that gets into a new job?

Structure, Handbooks, And Coffee Calls

Andreas Schachl 10:14

That's a very good question. And to be honest, that was a really big learning for me also when preparing for the launch of the company and everything. Of course, I I have, or we have also from the NGO part, we have a big network of specialists, clinical specialists, but also from social work aside and so on. And I think I did my homework, or at least I tried my best to do my homework before launching it. But of course, theory is one thing, and then yeah, really living it is something different. And I was also thinking about that a lot in terms of would it be really a big problem to add new people or to ramp up the company as such. What I learned, and I'm I'm more than thankful for many things that I learned from my team. But this one is one that really stands out is the team can handle that perfectly on their own. They are empathic, they are open to new things. We, or I when I say we, I mean, of course, my partner and I, but also the team. We try our best to provide an environment for also for new hires so that they can join, they can take their time. We have probably some things that a typical company would not have, like we have a company handbook where in detail we describe when we meet, what we discuss, sometimes also what is perhaps appropriate and not so much appropriate. We are having a clear structure when it comes to our meetings. We have a daily stand-up meeting in the morning, we have some really basic rules of how to talk to each other and things like that. We communicate a lot via text chat because people just prefer text chat because they can really think more about how to formulate things and make sure that they are precise in what they ask or what they they want to know, and that worked out really well. That works out really well, and yeah, it was it was somewhat a learning curve for me, to be honest, and my personal kind of favorite story from that end perhaps is we have introduced something that I call the coffee call. So I was managing lots of teams in in my career before I did this, and I had remote teams also in my past, and what I saw back then already was that sometimes it's just nice to jump on a call just for a coffee. So, like you would meet at the coffee machine in the office if there was a physical office. And I remember the first time when I asked my neurodivergent team now and said, Hey, in the past I did this thing like a coffee call, and perhaps this could be nice for you. I was really just asking if somebody would like to know more about it, or perhaps if they would have said, no, don't care, don't want, that's also perfectly fine. But no, they were just curious. I mean, they were I think a bit afraid, or at least unsure, uncertain about what this kind of coffee conversation would be because of course typically it would be very informal without big uh structure or so. But we ended up now with having a modus operandi for that. We have a clear appointment for the coffee call, we even have a backlog of topics to be discussed in the coffee call. We before the coffee call starts, we discuss what the topic for the coffee call today will be, and then we will be moving along that. And yeah, I think people are really, or I'm pretty sure people are in the meantime really enjoying this because they keep asking for the next coffee call, and typically we do it bi-weekly, but yeah, sometimes I have to skip because it's just so many things going on. Yeah, from that perspective, I think we're definitely on the right track to providing a work environment to neurodivergent individuals so that they can show that being neurodivergent is not a weakness, not at all. It's a very big strength, and in the area in which we are working, it's nearly a superpower of some sort.

Bias Mitigation Through Clear Instructions

Debra Ruh 14:15

Right. And it reminds me a bit of when I created Tech Access technology company, accessibility company years ago. Most of my employees, over 90%, were people with very severe disabilities, including many people that were blind. So at the time, it was this is a long time ago. People were saying, close your eyes and try to figure out what it's like to be blind to program it for accessibility. Honestly, people were saying that. And I was saying, well, my competitors close their eyes and pretend they're blind, but actually, my team are individuals with lived experiences every day of their life. And so it was a competitive advantage to me. But I love the story you're telling because I'm neurodivergent with ADHD, and what you're explaining would would sort of be very soothing and comforting to me to know what to expect. Sometimes it's nice to know what to expect. So it's powerful and beautiful for people with maybe say autism, but it actually helps all of the employees, all of the humans. And so that that I love, but I wanted to just shift. And as I shift, I'm shifting because if we don't do this, Andreas and other employers that are want to hire the best people for the team, which might be someone with autism, aren't going to be successful. And that's bias mitigation, which I know is part of annotation. And I was just wondering if we could shift into that, because if we don't get the bias mitigation right and so many things, not just about people with disabilities, I can't even imagine how many things we got to get right with bias mitigation, but I know it is critical and people are freaked out about it. And I believe humans must teach AI and continue to teach AI how to do this, but in my opinion. But what do you think, Andreas?

Andreas Schachl 16:00

I couldn't agree more, and I keep saying from time to time at least that others are talking about how they try to reduce bias or work around bias, mitigate bias, whatever. The people that are working in my team, they are brained in their head that they are biased, more or less. They are just having a look at what it is that they are looking at, and then they are working with it. But there is not like, yeah, should I interpret it in this way or in another way? And and and again, that was a big learning also for me. That I mean, I I always thought, especially being a technical guy. I mean, I'm a software engineer, or long time ago at least, I started software engineering before I went on on this journey. Now, I always thought as a technical guy, I would be kind of used to be precise in my words, be precise in instructions and all these kinds of things. Man, did I know. I remember the first version of the company handbook that I created before the first employee was even onboarded. I thought, yeah, okay, that that's pretty good. I I think I pinpointed everything down, and yeah, yeah, okay. It took exactly, I think, 10 minutes or so after the first employee was like, and by the way, what did you mean with this? Are you making this assumption here? And what what's that now? And I was like, okay, thank you for the feedback. I totally realize now I'm by far not as unbiased in a sense of unassuming things than I thought. And every day basically I see that, and that's again a thing that I learned from the team. Having different angles at the same thing can be so fruitful and and so important, especially if you are not or if if you are looking through different pairs of glasses or with different eyes on them. And being neurotypical versus neurodivergent is definitely making a big difference in the best possible way. And and that's why I say it's it's a strength that we have here.

Tools, Human In The Loop, And 3D Data

Antonio Vieira Santos 18:07

We know that uh it's fairly that many organizations are having difficulties uh onboarding new employees and particularly making sure that they they they use AI properly in the context of business. So considering the success that you are having, what are you doing further also to develop their skills in terms of developing of the development of the team?

Responsible Vs Exploitative Supply Chains

Andreas Schachl 18:30

The thing is basically you cannot develop further in AI these days because things are moving that fast. As I said before, two years ago, basically annotation was really, I would say, a percent manual task. Now we are at around 50 to 40 percent task with tools that help you. The annotation tools themselves they keep improving. Um, we have foundation models now in place that can help you with doing kind of a pre-annotation of data, but you still need to have a human in the loop to check if that's right what it's done, because we can still see sometimes even funny results that are produced because, yeah, of reflections in pictures, of echoes in audio, and so on. So basically, and I think this is really not over-exaggregated. Every day I learn something new, and my team learned something new because we are moving from one project to the next. Every project has the its own kind of challenges from a tooling perspective, from the from data that we annotate on. I remember the other day when we first really looked into the PD point clouds, the LiDAR data, and it was the the step from the 2D world, from 2D images and videos to 3D first sounded like, yeah, okay, let's do that. But when you're then really sitting in front of a LIDAR point cloud the first time, and you really try to figure out what's going on and you have to rotate things. So, yeah, it's just I don't think we had two projects that are just similar so far. Every project is something new, and the team again, they they like that. We have our standards in the meantime. We always create together with our customers extensive instructions on how the labeling shall be done. That's the so-called annotation handbook. And then we we move along, we work through the data, and at the end of the project, we can even hand over the annotation instructions to our customers, and they are always super happy because they finally have something that they can also use for compliance reasons. Often our customers are asked, How did you train your model? And so far, they had to then point at some person in the room, often the data scientists, and say, Yeah, this guy knew what we did. Now they have extensive documents, and they can also document that the annotation was done by individuals that are really working according to the documents because they're not working like they feel today and don't care about the documents. No, they look up things in the documents, and that helps our customers a lot. We we had very big Austrian company in the public sector, and they said they would have needs needed something like that for ages.

Debra Ruh 21:19

Yeah, I think a lot of people need it for ages. And I really believe this is bold, but I think annotation is the invisible backbone of AI. Only because you know, you gotta get it right. And so, you know, if you think about responsible annotation. How do you make sure you actually are doing it? It's not just being marketing language, which we're all so guilty of over the years, right? Oh, let's get it right marketing. I wrote a book on inclusion branding. It wasn't written so people could find better ways to market us but not do the work. But how do you I know that what you're doing just based on what you're saying, it's all about operational as opposed to marketing language, but at the same time. I think a lot of us are very confused about all this. So could you just explain a little bit why it's the backbone and why we're always gonna have to do this. I don't think we're ever gonna okay gone. Don't need it anymore.

Ethics Over Hype And Choosing Projects

Andreas Schachl 22:13

But yeah, I believe that's I mean ever is perhaps a very long term but at least in the foreseeable future I'm very sure that this won't be happening. Because the if if everything else would work you would still have from a compliance point of view a situation where you want to have a human in a loop. When we talk about medical appliances, when we talk about transportation safety and things we are not there that anybody including insurance companies is willing to fully trust an automated process or an AI or whatever it may be called but they always want to have a human in the loop and annotation at this point is mainly done in the global south in in India in Africa there are really big sweatshops and there are lots of reports that you can find in the internet or wherever you're looking for information on how people are exploited there. There are many cases where even children are used to do that work and used to do sometimes even disturbing work because sometimes these pictures I mean we are also talking about pictures for example for autonomous driving so there could be situations where car accidents or an accident in general is a topic and I would not like my five year old son to have to have a look at that and then have a close look especially and draw a bounding box or a segment something or label something. On the opposite side we are sitting here in Austria in the middle of the European Union all our employees have a typical Austrian work contract it's no charity they are paid in regards to the in Austria we have something that is called the collective bargaining agreement which kind of makes sure that everyone gets a fair wage that they can even make a living on and it's not like just yeah okay here you have some pocket money or so. No they're all getting properly paid we have the network of social workers so if there is some something that needs to be discussed or that needs to be sorted out to make sure that the environment is working for our team members we have a huge network that we can leverage I even have as the the CEO of the company I have a contact person that I can ask if I come across a situation where I'm unsure on how to talk or how to address something with the team so I would really 100% say that we are doing everything we can to be an ethical service provider of this ground annotation of this ground service that you need to have responsible AI later on.

Antonio Vieira Santos 25:06

Antonio you're on mute yeah well we all know that in AI everyone wants to go fast and we have news about AI almost every second everywhere but how you make sure that you move as you were saying you move in an ethical path and then you are able to assure your customers that with you they have that assurance so you probably have guessed it by now I'm not the typical startup founder as in looking to do hypergrowth and an exit of two billion in in two years or so and and the same is true for my co-founder we are both doing this because we think it's just a great idea and somebody has to do it and somebody should be doing it.

Andreas Schachl 25:51

That also and we had situations like that that sometimes we even reject the project because we say it does not fit to us. That's the one part of the answer the other part of the answer would be yes we we have breaking news and just the other day a guy from Austria Peter Steinberger with OpenClaw made big headlines again but I think it's still very important to keep in mind that we are always seeing the the frontrunner or the two three frontrunners but there is a large amount of companies enterprises organizations that perhaps have an idea where the frontrunner could be but they are not really one step behind him. So also our goal is not we don't want to be the first ones. We don't want to do everything we want to have a look at the project at the customer say hey yeah that really makes sense and if if a customer approaches me and and tells me about something and and I get the feeling it just doesn't fit then I will gladly recommend something else to him. So yeah I'm I'm not really this kind of speed kills guy.

Europe’s Diligence And Sustainable Growth

Antonio Vieira Santos 26:54

I'm really happy that you're able to be informed the case of Montcloth because it's an example of an European entrepreneur who created something very successful without almost getting no funding but at the same time suddenly he was hired to work at OpenAI in the United States so how do you make sure that we in Europe are able to keep this no keep this our entrepreneurs have them supported because we end up in this scenario that is taking 20 years that we have successful ideas being developed in Europe people don't get the support and they need to go to the other side of the Atlantic to be able to succeed and to scale their business. How can we mitigate this?

Andreas Schachl 27:38

I would say Europe's strength in the past has always been to perhaps not be the first one because it's not always the first one that is is the one who who wins in the long run. You know we have lots of examples of of groundbreaking inventions technologies that were developed in Europe and yes I know many of those were then really made big and very successful across the Atlantic but still on the long run if if you are not only looking at the one or two frontrunners you will recognize that there is lots of good entrepreneurship many good companies in Austria that are perhaps not so much in a hype spiral but they are just doing the work and to be honest I think that that's a nice place also you know if you're the frontrunner you always have to feel like you're chased by the crowd and and you have to be way more energetic and also you have to rush things because you're the frontrunner and perhaps this is now me being old school European I don't know perhaps I'm also not too much compatible with this hype mindset but I like to do my homework diligently I want to make sure to think about the implications of my work and where we are heading in overall I I think it's just you know in in in in football and soccer you also you have the the people in the front who are shooting or who are scoring you have the goalkeepers but you have also somebody in the middle field that helps you and I personally I feel comfortable in the middle field especially with what we are doing because I just think it would not be fitting to us to do something different.

Antonio Vieira Santos 29:25

So you feel comfortable with the support that you have today would you say that?

Consumer Choice And Ethical Vendors

Andreas Schachl 29:30

I mean of course it could always be more but I think that lots of things that called as being so yeah so so slow and and so like the AI act and things like that yes perhaps they're more diligent than in some cases than they should be sometimes it takes longer but on the other side there's still we have a common ground we have a common understanding and it's not like we have one strong man standing in front and saying that that's it we are sitting together as Europe discussing things and trying to find a consensus and that is a strength in itself.

How To Support And Get In Touch

Debra Ruh 30:08

I agree I agree and Andreas you had mentioned earlier when you were and and I'm an American here so I well only I'm sorry but I just really think this is happening all over the world and we're not better in the United States. So we're working on it we're doing some yeah we're figuring it out but I would say something that I thought you said that was very important is that we as consumers, we as individuals, we as business owners need to take the time to do our homework we need to make sure we are working with the ethical people I would hope with the word responsible you know you showed us how responsible you're being but don't go to the first and the second if they are using children child labor or bad vendors. Take the time to figure out who you're working with. And so I think that says really a lot about what you're doing. And I think it's going to get to the point I believe where we're not going to be so concerned about one country over the other but who is doing the best work to make the biggest difference that this is important work to do. So I just wanted to thank you for the work that you're doing and all that stuff. I also want to first of all I want to thank Amazon for keeping us here. We are so grateful for the support they give us um to make sure the show is totally accessible and to have guests like you. But Andreas what do you need? And I know we went over a little time but what can the community do to help you? You are not a disability you know vendor you are a the work you're doing is very important for all people it's just a huge blessing that you're including our community and see the value. And so tell the audience how they can connect with you. How can we help you?

Andreas Schachl 31:42

And also thank you so much for the work you're doing and Antonio thanks for inviting Andreas to thanks a lot for for the for the kind words I I think I start blushing now. But it's not true thank you that's very kind Deborah so yeah we're a small company in a small country in Europe and I think one of the biggest challenges that we are facing is we're relatively unknown. There are big players there are big players also in the annotation market um in general they are very big players and we talked about some of them just in this conversation and therefore it's really difficult for us to be recognized I think every second call with a new potential customer I was like hey I did not even know that I can buy annotation also in in Austria or in Europe or I I can buy annotation somewhere else than in India in in the Philippines or something like that with just me getting the chance to talk here I think you have helped a lot already so really I appreciate it very much and thank you for that if somebody wants to help us even more of course we are always looking for new projects we have also a list of people that we could hire more so I have potential new employees on the same side as I said before not planning any hyper scale up or so definitely looking for the right projects also for the company that sometimes makes it a bit difficult also from a planning perspective for us or from a financial planning perspective especially as I said we are paying fair wages to anyone that is an employee of responsible annotation services. So if anyone in the audience is working at a company where training machine learning AI development is a topic then I would be glad if you could reach out you can find me on LinkedIn. You can also find all the contact data on our website www responsibleannotation services.com and yeah I'm I'm just glad if people get in touch there are sometimes ways that cooperation can work that are not so obvious in the first place but yeah I'm always open to having a chat.

Debra Ruh 33:54

Thank you for being on we hope you'll come back on again and give us an update bye everybody thanks for having me