B (3:15)

Cool. So, and Dee, among other things, you drive product management. So could you share a little bit about who is the user and how do they experience the SALO and how does that make their social good mission, their work more effective?

C (3:30)

Yeah, definitely. So a SALO is being used by helpline staff. So that could be a counselor and a counselor could be a social worker, a psychologist, someone who's providing that service to the help seeker that's reaching out to the helpline, or that could also be a helpline manager or supervisor, someone who's overseeing the program and needs to do reporting or quality assurance on the level of service that's being provided. And so, you know, maybe more non technical folks. Right. Again, they're really trained in and are experts in how to provide that human to human care element that people are seeking when they reach out to helplines. Keeping the user in mind and those users in mind when we think about developing the platform is really, really important. And we try to make every feature that we build into a salo, a direct request or something that is directly in response to feedback that we've heard from our users.

B (4:25)

So tell me a little bit about what did they experience? What does a SALO look like?

C (4:30)

So a SALO is a web based, cloud based platform that helpline staff can access just via a Google Chrome browser. They don't have to download anything, they just need a computer connection and they're able to access kind of what they need to do their jobs on their shift. So when they log into the platform they just get one kind of simplified interface. And the idea is that they can handle all of the inbound conversations that are being sent into the helpline. So they can handle all the inbound conversations and then they can also do all of the data entry and all of the potential admin that comes alongside doing those counseling conversations. So it's one interface where they don't have to be switching to different tabs or opening different systems to do their jobs. They can kind of do everything from one place and for the channels that are supported. I think this is a, this is a good example of maybe how we have tried to purpose build again to the, to the groups we work with. We support voice calls and texts and chat, which, which tend to be, you know, somewhat typical now for call center solutions. But you know, we started working with child helplines and Child helplines are concerned about where children are and what are children using. They're using platforms like Instagram, they're using platforms like WhatsApp and Telegram. And so part of building ASE was also building in some of those social media channels so we can meet help seekers where they are. And so in the Salo, counselors can take voice, text, Instagram, Facebook, WhatsApp, Telegram, potentially more that we'll add in the future from the same interface. It looks the same regardless of what channel that conversation comes in on.

B (6:08)

And of course, one of the things that the helplines really needed assistance with was moving from being 95% telephone oriented to adding these text capabilities because the average young person is texting a lot more. Let's hear a little bit more about your backstory. You guys are both out of the tech industry. You could be working at big companies or maybe or have worked for big companies. So I want to hear more about your journey to working in technology for social good, for choosing to come to work at a nonprofit. Sure.

D (6:46)

Well, my background in college was in computer science, and I also had a desire to get involved in social impact and do things that were helping the world to be a better place. My initial job out of college was at Amazon, which was in many ways a great experience. But there was that sort of sense of what is the social impact. I want to be able to do more. And I ultimately thought, I think I need to actually leave the tech industry to be able to do things like this. And Jim, you were doing Finitech and other stuff was going on. I didn't know that these existed. And so I went in a completely different direction. And I worked in sort of international conflict relief. I was in Asia, I was in Africa over the course of about six years and involved in things that weren't really specifically technical, but were working with internally displaced people. And some of it involved data and recording human rights violations and things like that. But eventually working within international NGOs and seeing that the ways that they were handling technology were doing things like emailing, spreadsheets around. I sort of had this revelation that maybe there are actually ways that I can contribute, given the technology background and abilities that I have in a different way. I then moved back to the United States. I worked at a machine learning startup for a while, and then through a mutual connection, ended up meeting Jim. And after a few conversations, as Jim, you were starting to get the program with child helplines off the ground. We ended up connecting and things went from there.

B (8:48)

Well, that's a great story. And of course, Nick doesn't always tell all of the story. I mean, Nick's original job at Amazon included being on the original recommendation team, which was one of the early AI sort of applications where Amazon used it to figure out if you like this book, you might like this other kind of book. And of course, Nick, you came in essentially as the first engineer working on the salo, and then you became the engineering director, and now you're the executive director. And this story of people starting at an entry level position and then quickly moving up into senior management is not Nick Alone's story. It's also Dee's story. So, Dee, why don't you share a little bit about what brought you to Tech Matters and Aseelo.

C (9:43)

Sure. Yeah. So I have been working for a couple years at a tech company based out of New York. It's called yext. It was a digital marketing software as a service platform. And I was working there as a product manager and had kind of moved my way around the organization, started operations, and really got to learn so much about how software as a service businesses run operationally and all the different sales, marketing, engineering, implementation, professional services, like how all those things kind of work together to deliver a product. Also have a background in engineering. My college degree was also in engineering. So very much a problem solving skill set. I had always been interested in the nonprofit space. I think something I always thought I would get involved in kind of growing up and going through college, but then getting an engineering degree and I think looking at job options really felt like I had a mismatched skill set maybe to go into nonprofit. I had this idea that to work in nonprofit, I had to have experience with grant writing and maybe policy. That that was not really where a lot of my training had come. And so I think the first, one of the first kind of aha moments I had around this is when I was just looking for volunteer experience with nonprofits. I had found Crisis Text Line, who is one of our peers, and signed up to be a crisis counselor for them, which is just, you know, a remote kind of texting volunteer position. And I remember being really floored by this, this kind of intersection between tech and nonprofit. That was my first, I think, click to be like, oh, this exists out there somewhere in the world. And so in looking for jobs and kind of putting myself through kind of more professional development and programming, I started to focus more on trying to find, like, where can I find this intersection where I, you know, I can bring my skills and I can bring my tech background and still Apply that in this nonprofit world that I want to get more involved in and be a part of. And. And so I was just on job boards like Idealist and Fast Forward. Fast Forward has a great job board for tech nonprofits. And that's where I found the job posting for salo. And I applied to be, I guess, the first official product manager after Joan and didn't really. Wasn't really quite sure what it would look like, but it ended up being really this perfect kind of intersection of me being able to bring my product manager skills for this, for this really important cause.

B (12:16)

Well, and one of the things that I explain to people who want to do tech for good is your two key hires is basically someone to lead engineering and someone to lead product. And that's obviously why you guys are on this podcast, because the two of you really have built this social enterprise from scratch, right? I mean, Joan and I were there to help with fundraising and making some of the connections, but really you guys built a SALO into what it is today. So obviously we started in Africa and I think the technology we had back when we launched it was a big improvement for our partners. But I don't think we were salesforce quality as a start. But now, I mean, now we're serving countries at every level, from rich countries to more developing countries. And so, Nick, I think it would be great if you could expand on a couple of things. One is open source. So for example, when our partner in Canada Kids Help Phone helps support us to build a new feature. How do other people access that? And then also the importance of data. And why does it matter? Because when we're talking about empowering humans to help other humans, where does the data come in to make basically that empathetic human process actually work better? How do we do that?

D (13:42)

Sure. So Accelo is open source and all of our sources out there to look at. And not necessarily because we imagine that there's going to be a bunch of developers who are going to jump into helping to build Asielo and maybe some. But part of that is just the transparency and being open about what we're doing. And then part of it is that any new development, everyone gets the benefit of those. So it may be that a particular helpline or another organization wants to fund a particular feature, everyone then gets the benefit of that, both because it's available like it's out there to see, and because it's built into the platform in such a way that it's useful for everyone. So it ends up being sort of a community project around contributing things in. And another aspect of that is, I think Dee and other people in our product team have done a tremendous job of taking the needs of what specific helplines want and thinking about how can this be done in such a way that it's useful for everyone. Most of the things that we built now are used not just in one place, but in multiple different helplines around the world. Data has always been one of the central aspects, I think, Jen, in some of the conversations with Giroux and in some of the conversations with helplines, one of the pain points that surfaced was that they would provide access to aggregated, anonymized data to Child Helpline International. And that was a very painful process for everyone involved because they had to provide a standard format. Helplines often had data in multiple different systems that didn't match that format. And so there's just always kind of a real pain to do that. And it's not just the data for CHI that they're providing. They have data for their national governments or for donors or for internal use, just to try to understand what's happening. And so there's kind of both the value of data for operational usage, as the helpline is trying to plot out what are both like the staffing levels and the training as well as advocacy purposes. How can they get the message out about what's going on with kids or with other people, mental health challenges? How can they sort of create sort of a systems change or a policy change around helping to prevent some of these issues and other things that are coming up? And so with the salo, we are doing good practices around structured data. First of all, making sure the data is secure, making sure that we're complying with appropriate privacy standards and regulations, and then also having that data in a state where it can be shared in different formats after being anonymized, aggregated, and being able to provide that back in a way that's valuable, and that helplines have access to it and the helplines own their data, so they get to choose what they want to do with it.

B (16:59)

Well, that's really important part. I think, that upgrading the data capabilities of helplines by a shared tech platform that kind of works on their common issues has been a huge benefit that I've heard a ton about from leaders of helplines. So no conversation about data is complete without a conversation about AI, which is a constant issue. I'll note that a lot of our job is to tell people, as the date of this recording in 2025, it's not ready to fire your counseling staff and replace it with a robot, you know, chatbot.

D (17:41)

So typical helplines counselors will have a phone conversation or a chat conversation, and they will be recording a bunch of data about that conversation. But a lot of them spend a lot of time adding in that information, and that time could be spent taking another call, talking with another person. And so we tried to look at how are there ways that we could use AI to help facilitate that as the initial step. And then there's a whole bunch of other things that we could do. So we looked at the possibility of categorization. So generally all helplines, when a contact comes in, they will categorize it was this mental health issue, this anxiety or depression or violence or something like that. And they have to categorize these to doing that. And then also they generally have to provide summaries or notes. And so one thing that AI can do is take conversation and attempt to summarize it. So those are the things that we initially identified, and those are things then that we've been working on. And before we did any of this, starting to work on these projects, first thing that we did was talk with helplines and get their agreement to use data for that. We didn't initially have agreements that allowed us to do that with helplines. And so since they own their data, we said, here's what we're going to do. This is how we're going to use it. We want to make sure that you're okay with that. Another big part of it was scrubbing it for personal information. So removing names, places, other things like that before we start working with AI on it. And that's for a couple of different reasons. One of those is protecting people's privacy, making sure that if we're using that in any way, there isn't a risk of that leaking in some way. Another aspect is to avoid bias. So we're not training AI based on someone's first name or anything like that, where that could actually add some bias and some error. So once we had that as a data set, we started experimenting with different things and really engaging with our helplines throughout that process. And I may actually offer to dee to sort of talk through the process of sort of doing the user research and other aspects of that.

C (20:06)

Yeah, I think on the user research side, two kind of areas I think we focused on is one, again, trying to focus on what are the pain points that we can solve with technology and with AI and not starting the other way around, not saying, oh, here's a Fancy use case of AI. What do you think? Right. We tried much more to understand what are these counselors workflows, where are they maybe getting stuck, where do they have challenges? And it's not the same for a counselor working in Zambia and a counselor working in New Zealand. Each kind of counselor's experience is going to be a little bit different. Each helpline has different protocols around how they capture data, and they have different operational policies, things like that. So doing the user research was a really important step to make sure we understood what problem were we actually trying to solve, and what can we build that's going to enhance the counselor's experience and not create more friction around it and more confusion. And then I think the second element that we really wanted to understand was will these counselors trust AI in terms of seeing it in their platform and seeing the results that are generated from it? And resoundingly, the answer was yes. Obviously, there's a little bit of kind of. There can be a little bit of hesitation. But I think overall, once the counselors get to see their work reflected in the technology, I think there is a trust and there is kind of a knowing that AI is being used out in the world. And we had some counselors who had their own really great ideas about how they thought they wanted to see AI in the platform and how they wanted to engage with it. So kind of being able to hear their ideas, hear their voices, and get kind of a pulse on their comfort around AI, I think was really important.

B (21:43)

Well, Dee, I mean, bringing up trust, I mean, we're being entrusted with this super sensitive data. We're being entrusted by building the key infrastructure for these organizations. And so an awful lot of our work is about being trustworthy. And that creates the opportunity for us to make a difference in partnership, because we're the toolmakers and the helplines are the ones that are actually helping people on the ground. So I think one other point I want to make about this is that there are a lot of people working on AI in our field. We're releasing our stuff open source, not releasing the data, the sensitive data, but sharing what we are finding out as we do it. And that's really quite unusual. And I think the nonprofit sector needs more of that because, frankly, the use cases across much of the helpline field when it comes to AI are kind of similar. We should be working together as opposed to in tiny little silos. But as we get ready to kind of wrap up the story of a Salo and the story of you two leaders, I think you both have observations, learnings, challenges that you've been experiencing by doing this work because it's not 100% easy. And so, Diya, it would be great if you could highlight, you know, a couple of the key things that you would like to share with other people about, you know, what your experience, you know, building this social enterprise as the product leader and the operational leader has actually meant.

C (23:19)

Yeah, I think I would go back to what we were just talking about, which is trust. And I think that that has been a really key part to our success. And I think that is something that has really almost set us apart. And I think when we speak to helplines, whether or not they use a sale, I think something that we just consistently see is people don't trust the software they're using. And when they don't trust the software, there's almost like a hostility, there's a resistance to being able to kind of figure out how we can build something together. And I think continually making sure that we're earning that trust and keeping that trust has really been important. I think we do that with our relationships. I think we do that by really putting a lot of care and showing a lot of empathy for the missions that each of these organizations are putting forth. And so I think again, hiring people on our team who really understand the importance of that and prioritize that helps us maintain these really strong partnerships with our helpline partners. Working internationally with a fully remote team both allows us to sometimes meet our partners where they are because we have kind of more regional coverage. But it's also a challenge because we can't have people, you know, all over the world in every place that we work either. And so I think there is this balance. I think one of the challenges we have experienced is working with a small team and supporting very big range of time zones and making sure that people feel like we are there and supporting them even if it's the middle of the night for most of our working team, definitely been a challenge, but I think something that we are continuing to work towards and build around.

D (25:07)

Yeah, I would certainly agree with everything that DIA said. I think when people ask me what it's like to work at a tech nonprofit versus a for profit organization, I think I'm quick to say that there is a lot more that is in common than you realize. We're organized in similar ways. We have product teams, we have engineering teams, we have sprints, we have planning, we have all of this different stuff that factors in. There's a lot that's similar I think that some of the differences, like of course, the trust in the sort of community building I think is really important. I think it's good to be in a field where the other organizations and peers in the field are more collaborators than competitors. And I would much rather work in that sort of environment than in a really tight competitive situation. One of the aspects as well is just that the way that things are funded can be very different, which creates situations. A lot of times there is restricted funding. So there's like we're being funded to do a specific thing that doesn't include these other things. And that often misses out on some of the things that are really core to having a tech organization function as someone with an engineering background. One of the things I harp on all the time is just technical infrastructure. People are really excited to find the shiny new features, but then some of the boring stuff keeps everything running, isn't great. And we are thankful that we do have some tech forward donors who have helped out with that. But I think another factor is as we are doing more in the earned revenue side and trying to build SALO as a more sustainable organization that relies on both a combination of grant funding and earned revenue, then sales and marketing ends up being a thing that we, we need to do. We need to, to put effort into that, which is also a thing that it's hard to find funding for when you're not providing any sort of a monetary return on investment. So I think those are the, some of the things that a lot of nonprofit organizations work with that, that we have to wrestle with as well.

B (27:40)

I think you described a very ironic situation. I mean, last year SALO was more than 50% of its revenue came from our customers. If a for profit had reached that point where it was generating cash flow and product market fit, they would go out and raise a boatload more capital because investors would say, oh my God, they have a product, they have distribution, you know, let's help them grow. And this is the moment for organizations like ours that we have donors going, well, you know, we funded you already, you know, we're funding somebody else. And with, you know, some, some noteworthy exceptions, which as Nick, you know, we're very appreciative of those people. Well, this has been really exciting and I think that, you know, I'm, I'm looking forward to doing, you know, my part, which is helping us raise a lot more money to fill those gaps so that a SALO can grow from 15 or 16 countries to 20 or 30 and keep seeing that the people who are helping other people are a lot more powerful in their work by having modern tech tools that actually work, safeguard the data and can be used to advocate for larger change at a systems level. So thanks for building this incredible solution and I hope it just goes from strength to strength.

C (29:04)

Yeah, thank you Jim.

A (29:10)

Thanks for listening to my conversation with D Lo and Nick Krobert from aseelo. I'd like to take a moment to highlight something Dee and Nick said about trust Helplines entrust Aseelo with their core operations. Help seekers entrust helplines with their most vulnerable moments. That chain only holds if every link behaves in a trustworthy way. In the for profit sector, we treat trust as a marketing problem, but really it's a governance problem, a power problem. Who owns the data? Who sets the policy? Who decides what AI can and can't do? And how transparent are we when things go wrong? What I love about this story, the Assailo story, is that it shows a way forward open source code, local ownership of data, and a non profit partner whose incentives are aligned with social impact

B (30:09)

rather than growth at all costs.

A (30:11)

That doesn't magically solve every issue, but it gives us a healthier starting point for the social contract around technology, especially when the needs of children are hanging in the balance. Which of course brings us to children's rights. Not nice to have rights, but the basic ones in the UN Convention on the Rights of the Child the right to be heard, the right to protection,

B (30:35)

the right to privacy.

A (30:38)

When young people contact a crisis response helpline, they're often exercising those rights in the most fragile moment of their lives. And technology can either help or get in the way. A system like a SALO is in a way infrastructure for delivering on children's rights. It makes it more likely that a child's voice is heard, documented, taken seriously, and acted upon. But that only works if the technology is designed for their safety and dignity first, not for the convenience of the software developer or the donor. So when we think about innovation, I don't get excited about AI features on their own. I get excited when I see technology reinforcing those fundamental rights or making it easier for people to protect those rights. Especially for kids who don't have any other safe adult to talk to. If you'd like to learn more about as Salo, visit aseelo.org that's a s e l o dot org to find out more about what we do at TechMatters, head to techmatters.org or look for Tech Matters on YouTube and or LinkedIn and if you have thoughts, questions or guest ideas, you could always reach us@podcastechmatters.org we'll be on a holiday break until January 7, 2026, at which point stay tuned as we come back strong with Rick and Gandhi of Digital Green, a very successful social enterprise that's using generative AI to provide over 8 million farmers around the world with real time advice and knowledge sharing. If these conversations resonate with you, I'd love for you to check out my new book, Technology for How Nonprofit Leaders Are Using Software and Data to Solve Our Most Pressing social problems from MIT Press. You can find links and more@fruchterman.org I also want to acknowledge the help of the generous donors who support us, especially Okta for Good. I'm your host, Jim Fruchterman.

B (32:51)

Thanks for listening.