Episode Transcript
[00:00:00] Speaker A: Well, hello and welcome to Localization Today. And no, I'm not playing the piano right now. My name is Eddie Arrrieta, I'm the CEO here at Multilingual Media and today in this show where we bring people that are driving innovation in language and localization industry to explore how to harness emerging technologies. We are going to be joined by Veronika Ilak, industry founder and AI vlogger and Bridget Elack, international speaker, author and consultant on language Tech and Idea Idea.
They are the authors of the 10 Best Language Technology products of the year and why they might break the current pricing model. Veronica brings over a decade of experience developing AI driven solutions for Fortune 500 companies, government agencies and startups while Bridgit and advises organizations on integrating equitable people centered strategies with cutting edge tools. In their article they propose a three tier classification, raw AI output, creative and highly technical to align pricing with purpose and context. Veronica and Bridget, welcome and thank you for being here today.
[00:01:18] Speaker B: Thank you so much for having us. It's a pleasure.
[00:01:21] Speaker A: Feels like we've been talking for weeks.
[00:01:24] Speaker B: We have been months really.
[00:01:29] Speaker A: That's what this is all about. Thank you so much for joining us at Localization Today. I know the audience is really going to appreciate this conversation. Talking about technology, talking about what we believe is going to be the future or what the future looks like. We've prepared some amazing questions for you and you've helped us with that, of course.
And let's start with the big one.
It is a huge process to identify 10 different solutions, let alone five or just one powerful solution to even recommend and put your name behind it.
So how was that process? Can you walk us through your overarching methodology for selecting those top 10 products? How did you balance tools focus on automation versus those driven by human center, nuance or input?
[00:02:24] Speaker C: Well, sure thing Eddie. First, thank you so much for having both of us today. And I think for context, it's important to point out that Veronica and I come from very different ends of the language tech spectrum and really even different industry experiences.
Same last name but different opinions, different sensibilities. But in collaboration I think that becomes very complementary and rich.
We both have had different experience optimizing tech stacks for LOC workflows. I've spent most of my career in language services and global marketing working with thousands of linguists globally managing government and corporate contracts and consulting. While Veronica on the other hand comes from the tech and AI world encode platforms, privacy, copyright and startup velocity at very high levels. So not only was it really hard like you Said to put all of these technologies, all doing different things in one list.
It was kind of like a zoo of animals. You know, you're trying to compare and contrast and rank all of them. But we also had to bridge different areas of expertise and different instincts.
So that's setting up the kind of the background of us before we actually entered this article. So through this sort of pressure and compatibility and incompatibility, what emerged were a few arguments. We had different opinions, but one very clear question that linked everything together and that was what was the purpose of the linguistic output for these tools? So what content output problem were they trying to solve? And that was really where some clarity started to emerge for us. And when it became clear that we could map the goal of the output, the linguistic output for all of these tools and see their strengths in each area and try to use that as a lens or a way to evaluate them, we wanted to do it fresh, take that fresh look at it. And so that was, you know, what we did. So this led us to look at the problem differently and through three tiers of output data, depending on that purpose, we divided them into three categories.
First was tier one, which we call raw AI output, which examples are fine tuned linguistic models, QA supported pipelines, things built for scale and speed and high speed delivery.
And tier two is creative output, which would be marketing, media localization, anything where tone and rhythm and cultural nuance matter.
And tier three, as you might expect, really that highly technical output, the legal, the medical, the scientific, where, you know, an error can mean life or death. That's the kind of output that most of the people in the language community like to talk about when we're talking about how essential linguists are in the mix. So we resolved most of our friction by staying grounded in the function of the output because it allowed us to evaluate these tools and in a real world context, not like in a vacuum.
And that is really how we landed on such a diverse list that includes a real range of tools like Modelfront, which powers high volume AI only output, and memo QAGT which supports that. Tier 2 T3 translator led technical translation. I think the best part, miraculously is that we still ended up friends, mother and daughter even. So it sort of worked its way out, but it definitely was a process to get there.
Quite a process.
[00:06:11] Speaker A: Nika, anything to add to that?
[00:06:13] Speaker B: I mean, it was extremely difficult. We were looking at anything from tools that look at automation to orchestration, to traditional translation workflows, like to tools that are going away from that. So to try to put them all on one list is pretty a very hard task. And it took us many months of research and collaboration trying to get data and going through their features and going through their market traction and seeing who was adopting. What was the industry buzz about them? Were people migrating? All of these things were really taken into consideration, but as Bridget mentioned, the content tiers really linked them all together. What were they trying to solve?
It didn't matter that one was a QA API, it didn't matter that one was something that would be for a more tier 3 type technical translation. They're all trying to solve a problem that are somewhat related.
[00:07:14] Speaker A: Of course.
Now we can or have an opportunity in this conversation to go deeper into the specifics if we want to think about what criteria did you use to narrow down, you know, the 10 products, given the diverse use cases that you encounter? Can you talk to us about those tiers that we alluded to in the description?
[00:07:36] Speaker B: Yeah, absolutely.
So we have three tiers of content, but we had a 30 point scoring system across five weighted categories. And it wasn't about what was super flashy or did really well on marketing. We were looking for big shifts and real world impact specifically, specifically for this year, even if it involved fine tuning a product that existed before, but it just took leaps in stability and performance for the user.
First graded category was, as you would probably expect, product innovation. So we were looking for originality and products that looked at problems differently, not just incremental updates to the code base, which is a routine that many of us in SaaS products tend to do.
The second category was workflow impact and market contribution. So did this product fundamentally change how language teams were working? If that's automation, if that's augmentation, orchestration like Blackbird, who was on this list?
The third category was adoption. So it wasn't just about the enterprises that were on their roster. Of course that helped, but we looked at the overall market reach, things like MAUs, monthly active users, overall temperature buzz in the industry. Were people switching away from the product for certain reasons? All of that was considered.
Fourth was specifically 2024, 2025 activity. Big feature rollouts, partnerships such as DeepL with Synthesia series, fundraising rounds such as SmartCat with their series C ecosystem growth.
We really just wanted to make sure that what we featured wasn't a great product from three to five years ago that's kind of still coasting, but something that's really actively shaping this current moment in language tech. And the last one was innovative maturity. So really that was Our way of asking in AI, really important was this a shiny new toy that somebody launched and is great in theory, or has it reached a level of maturity in terms of its output? We wanted to really see that real world impact. And yeah, and I think that last.
[00:09:41] Speaker C: Criterion is very interesting because AI is, you know, changing so rapidly and it seems like new things are old in a week. So developing that category was, I thought, an interesting take just to see what innovation has stood this short test of time so far. Right. Because we don't have a long decade to look at, but what's sort of holding its ground.
[00:10:07] Speaker A: And I think, I think this gives us an opportunity to call to talk about the elephant in the room.
You are Veronica's mother, right?
[00:10:18] Speaker C: Last time I checked.
[00:10:20] Speaker A: How did you. How did you both end up in the same industry? And also in the same conversation from very different points of view, but also in the same industry, which is rare to see.
[00:10:35] Speaker B: I'll take this a little bit. I do want to first say that we are not the first example of a parent child relationship. Yap Van Meeren and Maya Van Meer of Taos are a powerhouse.
I came into the localization industry very organically building a piece of software that Bridget's company and some of her colleagues needed. And I really fell in love with it. But really completely different sides. I came from Sony Music, I came from the US Government Leidos working on AI drone ships. Bridget has been a lifelong linguist and dedicated her entire life to, to this on different sides. So ultimately, I feel like the fact that we are related actually cut through all the BS in the terms of what we felt should actually be on that list. There was no sugarcoating.
We argued about it quite heavily in certain ways and there was nothing to kind of fluff because we were looking at it completely different way. That I'm very realistic. I'm like, the tech is growing by this exponential amount, by this, this is going to be relevant. Brigid has, you know, different experience on the ground and all those conversations taught us both a lot too.
[00:11:51] Speaker C: Yeah, I think listening to, you know, again, still my, my encounters with, you know, hundreds of linguists on a weekly basis and then working on, on national and state committees that are dedicated to language access and, and technology.
Just our ears are. Are tuned to different audiences. And so in some ways merging, that was, that was a bit of a challenge. And we both, I would say, had credible opinions, but it really, you know, it made it more flavorful, I would.
[00:12:25] Speaker A: Just say, and I think so too, it's probably also related to the different ecosystems that you are part of and I love to dedicate some time to talk about that. And Bridget, we talked a lot about ata. Now I'm collaborating with ata, which is amazing.
I attended last year.
Veronica working now with a think tank and then we all are in contact with many different companies. Right. So how easy was it to navigate some of these realities for you?
[00:13:00] Speaker B: It was quite difficult. I mean as you kind of mentioned that think tank, we have 20 members, 18 different companies being represented. Only two actually made it to this list.
We were of course worried that we were going to hurt people's feelings. But I think the mindset that the think tank has given us, I mean the whole point why it came to be was to operate free of any logos.
To be a group of executives who traditionally probably wouldn't really be working together to really talk about things in a safe space. Safe space. That's really, really the best way to put it.
So it's completely free of any sort of corporate interest. Is a nonprofit, you know, for goodness sakes. It's, I think it's actually really helped us not to pick sides in any sort of way. Those kinds of conversations have come out of that think tank and so grateful to be part of it, to be honest.
[00:13:59] Speaker C: And I actually like to think of it as a really, you know, sort of non denominational space. And even after this article was published, some of the, some of the reactions of members of our think tank were very similar to the public. There was a lot of support, a lot of compliments, some other thoughts and why doesn't. Maybe this one would have been a good one or that one. So it was a very similar to the, to the general public's response and again very, very fair and very unbiased.
[00:14:29] Speaker B: Yeah, they were, we were, I mean everything that is published in the lock industry is, is, is evaluated by the think tank. We were no exception to their grilling. No exception. We had, you know, some members are like, you know, we think Frey should have been on this. We should have think we thought Crowdin should be on this. You know, we all had a.
About it. But yeah, I was, you know, we were no exception to the grilling of content that was, was published in the.
[00:14:54] Speaker C: I think, I think one really good reply is there were people who said, you know, Y wasn't X or Y on this list. And my best answer to that question is always okay if you put X or Y on. And I wouldn't argue that they don't deserve to be there because like I said, we did argue 10 was a hard cut, but if you did put X or Y on, who would you then remove from the current 10? Because most certainly all of those products deserve to be there as well. So it really, it's, it's, you know, please give me a pass. You know, for my generation, I grew up with the Miss America pageant. It is, how do you select from 50 beautiful women?
10 and, you know, everyone in your living room has a different opinion. And then, God forbid, one. I'm so glad you didn't. We didn't write an article on the best technology.
That would have been impossible. So it is really, you know, I stand by the 10 that are on that list and just say, you know, how do we others? Sure, there are other great contenders that probably could have been there, should have been there, and could have been there as well. But 10 is 10.
[00:16:05] Speaker A: Thanks for giving that context. I think it gives some idea.
I love to talk a little bit more in just a few minutes about future content that we could work on and that we will work on because this opens up opportunities through different tiers. We could dig deeper into each of the tiers and the use case categories and whatnot.
But before we do that, I love to talk a little high and low level as well in terms of the current state of the technologies in the industry. And we were talking off the record about kind of like some perceptions about the level of innovation or the speed at which companies are moving or the speed at which the markets are growing related to our industry. Of course, you've seen the list, you've seen the technologies.
If you could think about the specifics in what we had prepared, looking at the solutions that perhaps didn't make to the list and didn't get to the top 10, what would be the things that could be enhanced? For me at the high level is what are the things that we still need to see in the technologies that run our industry? That, to me is the major question there.
[00:17:24] Speaker B: I think a lot of the biggest ones, everyone knows technology is moving at a crazy speed. Everyone feels like their information is outdated every single hour. With that in mind, your product and your interface has to feel extremely light to the user to try to attract them.
A lot of systems are older and they're not paying as much attention to that user interface. And my argument to you now, I mean, we see it with Apple and everything else. Your user needs to understand when they get into your system how to perform 85% of the functions. And then of course there are trainings to get you all the way.
But making it feel like paying more attention to the user experience is a lot more important than just focusing on functionality. I mean, I love the human interface guidelines published by Apple in 1984 and they were talking about some really simple concept that the user needs to feel in control of the computer, not the other way around. I feel like the last 20 years we really kind of lost that. We were always focused on performing, building this functionality in to complete this task. No matter if that took two months of training to get into the system, that's not a reality anymore. Because of how quickly this tech is changing, you really need to find a way to alleviate that friction point. And the second is, you know, doing the same amount and the least amount of clicks, like people don't like clicking anymore. They I, you know, with my last startup, it was a, it started out as only a client portal and I got, oh, there's three clicks to submit this form. That's too many and it's ridiculous. It's open form, type in submit, but that was still too many for some people. And yeah, it's. The clicks are very, very, very important. So kind of just looking, you might have solved a problem, but how can you solve it further for your end user to understand intuitively what they should do without formal training and in the least amount of clicks possible is kind of the angle that I feel like everybody should be looking at.
[00:19:33] Speaker C: I would totally agree. And that's actually how Veronica and I started. You asked initially how did we start collaborating? It was really, because I was really exhausted with certain technologies in the same, in our industry, they just really had old UI or they were just upgrading on top of old infrastructures or had what I call government friendly user interfaces. You know, the ones that are like 1990s and they think they're fine. It's. The majority of us don't want to deal with multiple clicks.
Clicks represent money, right? But in certain sectors of government, it seems like we can encounter people with tenured salaries. This is my personal experience.
There may be less tech literate. Definitely not as keen on advancing as fast as the corporate sector. They have a little bit less urgency. They have their hands tied with bureaucracy. They're trying to get budgets to make things get upgraded, but these budgets don't come through until a year and a half later. So, you know, maybe some people just give up.
But that ui, those clicks are so important. I'm actually working on something pretty groundbreaking right now in the Commonwealth of Pennsylvania court system in this regard and language Space And I'll just say that there are a lot more hoops to go through and a lot more rules to make or change.
People you have to kind of shock out of complacency. It's an entirely different ball game than corporate audience. But again, like Brock said, I think that UI and those clicks are where there's, there's room for everyone to improve.
[00:21:11] Speaker B: It's interesting you even like even that we have the same goal but completely different reasons because for me the clicks are the conversions. If there's any barrier to entry, no one's going to adopt, no one's going to switch to your technology, you know, but they, because of how quickly things are moving, you know, they, you need to have that friction removed and you had a completely different reason. But same, same, same point. And that's just. The competition is so stiff right now. We're a lot of organizations like for example Trados has been around for a while but this year had a tremendous push towards focusing on that user experience. They launched a complete NLP driven reporting interface. No other company has done that. Which means you can generate your reports with typing in. I want to see X, Y and Z. And that's just absolutely incredible trying to alleviate that friction for the end user. So yeah, that was kind of the biggest.
[00:22:08] Speaker A: Thank you. Thank you. And this is a perfect segue to what I was referring to earlier about the future and things that we could cover. It looks to us at least from the multilingual perspective that agentic is going to be a huge conversation whether you applied it wrong or did. Because I think one of the biggest things that I started to see is everything is an agent and not everything is an agent. So now that everything is an agent, it's very easy to be tempted into, you know, having agents for pretty much everything.
So that seems to be, that's probably for us what the end of the year is going to bring in as a conclusion in terms of like the developments that are more salient.
What can we expect from the ELAC duo or individually in terms of coverage or you know, content that you think we could work together in multilingual.
[00:23:10] Speaker C: Just want to start by prefacing this. That just shows how very different we are. Veronica hates the word AI agent. I love the word A. I love it. And it's just different opinions from different experiences.
[00:23:24] Speaker B: So yeah, I hate AI agent.
[00:23:27] Speaker A: What did you go with? You don't say AI agent. What'd you say?
[00:23:31] Speaker B: I, I don't, I don't know what I would replace it with. But, but, but I don't know. To me, it's like a step up of people were saying AI assistants for a while and now they're saying AI agents. And I think they're just going to be another one. And I understand it's like a very good visual representation of what it's doing and agentic frameworks and yada, yada, yada, but I, I think it's like still a buzzword of a lot of things that are just going to be a toy. And that next level elevation is going.
[00:23:56] Speaker A: To be a new term, is the new buzzword.
[00:23:59] Speaker B: Oh, it totally is.
[00:24:02] Speaker A: Good, good. Yeah. So we'll call it the AI of Agent. The. The year of Agentic is what I'm gonna go with.
But we cut you off.
[00:24:11] Speaker C: No, I, I was just, it was funny because as you were talking about AI agents being this next time, just sitting there wondering how Veronica is going to answer this, because she says, I hate that word.
And I've already, like last year gave a presentation on AI agents on, you know, again, on a government level platform. But, but it is, it's, it's. I would say as far as collaboration, you know, we have ongoing projects that we do work on in sync, sort of as a consultants on each other's projects. I mean, she works in a consultant capacity for several of our corporate clients that, you know, we have a consulting company then areas if anything's related to language or language technology. And I can support her efforts. I do that as well with others.
So as far as writing, I, you know, I have written for multilingual for a couple years.
And funny enough, like the very beginning of this, this interview today, the whole crux of when I started writing for you guys was about this, you know, tech versus language debacle which has just erupted in this industry. Like, are you tech? Are you language? It's just so, so silly to me because it's, it's, it's really one industry. And I think if at least it's been kind of my mantra and message to my language industry colleagues, that is not a separate industry. And if you think of it as that, then maybe we do have to worry about our jobs.
We have to get fluent in all aspects of language localization.
[00:25:45] Speaker A: Right, right, right. I'd say before we go, something that I like to do is to put some color into this conversation by going over some of the names that are on your top 10. I think we have also discussed if we can look into some of those.
How do we call them? Notable mentions.
It'd be great to understand, kind of like what were some of the things that put them up there for you and then give the audience an idea of how that actually turned out to be.
[00:26:19] Speaker B: Yeah, well, I guess to start with, the ones that made it was model front Unbabel.
They both are great examples of strong performers in category 2 content.
Their entire focus is on translation verification at scale, great quality APIs. And they both have clients from luxury fashion platforms to heavily scientific biotech companies to ebay, trusting their tech to make that kind of decision and publish automatically.
Tratos AI Most people kind of know about their other product, but most people don't understand that AI is in Tratos. And we really, really liked their focus on the user experience. Of course they have a lot of AI that other people do, but their focus on user experience was really big for us.
DeepL naturally there, their laser focus on solving linguistic pain points with precision. They're not just going for flashy stuff. They're doing what they do well, continuously. They had a new great partnership with Synthesia. They launched a product called Clarify.
Bridget, I know you had a few more too.
[00:27:29] Speaker C: Like memoq agt was really a standout for tier 2 and 3 because it was the first few shot LLM translator built specifically for the localization industry.
And again, it's very popular on the language industry side.
Instead of replacing human translators, it helps them work faster with a lot more precision. And the buzz that I hear from my colleagues and LSPs of all sizes that it's really impressive in practice.
Then we had SmartCat on the list. It's a sort of quiet stealth industry player in a way. We don't always hear a lot about SmartCat, but it hit all three of our tiers in different ways.
[00:28:11] Speaker B: SmartCat would have probably been left off the list traditionally. Like, nobody really ever talks about SmartCat, but they're doing some amazing stuff.
[00:28:20] Speaker C: I mean, it did have that 43 million in series C funding this year, which was, you know, I think people who didn't know who SmartCat was turned on and said, Wait a minute, who's SmartCat?
It also made a huge leap with AI Image Translator, which is a tool that doesn't just OCR and spit out text, but it actually rebuilds localized visuals in the target language, which is such a huge pain point. And localization, it should be something interesting to localization professionals.
Veronica, you have to mention, I have.
[00:28:54] Speaker B: Others boost lingo, of course. Was there another example of why I really feel like our approach was the correct one? Because Boost Lingo would have been left off too, probably, traditionally, even though they are killing it in the interpreting space, whether it be AI leveraged or enhanced with the human element there, they're doing great in Tier one, Tier two and Tier three content.
Yeah, I guess. Who would have been on.
[00:29:21] Speaker C: I think just to just follow up quickly. I think boostlingo is a great example of a platform that's really redefining how we think about tiered language access.
So they're not just offering options, they're setting like a new delivery model for how language scales across industries. It really is interesting.
They're using hybrid, they're using AI only, they're using human only. It's very. It's really unique.
They're killing it also.
Go ahead.
[00:29:51] Speaker B: I was going to say this lingo also, like, if anyone ever goes and kind of does their research, they're doing a really good job of educating the end client. The difference, Right, because this is like we hear. We hear from linguists all the time. I mean, a lot of angry linguists. AI is going to take our job. It's not ethical to use AI. Yada, yada, yada, yada. The thing. And again, it became very clear to me as we were writing this, Tier one, Tier two, Tier three, Tier one content. You know, you're not competing with that. Tier two and Tier three, you're looking at situations that require a lot of cultural nuance, require, you know, cannot have errors. Tier 3, if you made an error on an IP patent, you would lose legal protection. Right. So it made it very clear to us, you know, the value of human translators, where they should be focusing too. And Boost Lingo already communicates that to their client. That would be making this choice. You know, AI is cheaper and it's good for, you know, some situations. If you need high precision, you need to go with a human linguist. And it's right there on their website. I feel like everybody should be doing that 100%.
[00:30:53] Speaker C: That's such a great point. Because client education, I've often said this, you know, training, corporate and linguist client education is almost half of our job in this industry.
People don't understand it. We're hidden. Nobody knows where all those subtitles come from that are on the bottom of your movies. They think there's little elves at the North Pole that are doing this. Educating our clients is seriously, like I said, about half of. I think it should be about half of our effort if we're trying to sell language services.
But other contenders on the list, Blackbird, you know, it's probably the most invisible but essential tool on the list. It's not an empty engine, it's not a cat tool. It's like the plumbing really. It connects systems that weren't built to talk to each other and makes sure that they can all work together without custom dev work. So you know, really expensive.
[00:31:49] Speaker B: It's really expensive. Integrations cost so much.
[00:31:52] Speaker C: Yeah, we can't forget Lara. Nobody would. I don't think any list this year would be complete without translated's Lara. And that impressed us because it's the first LLM built from scratch specifically for our industry, specifically for our. For translation. Not adapted, not retrofitted, but you know, designed for the job. And I've worked independently with a lot of their reps. They really know what they're doing.
Really well informed customer service reps. It's kind of refreshing working in sales calls like for example, with that level of expertise and 5 million also ramping to 200 languages this month, which is a level of product growth that hits every one of our five judging criteria.
[00:32:42] Speaker B: So I was just going to say one more thing. Translated Lara has hit 5 million users just from word of mouth. So you know, they're expecting like a significant exponential growth once they actually have like a GTM strategy too.
[00:32:55] Speaker A: So yeah, this is wonderful.
Any contenders that you thought, you know, maybe if we do tier by tier we could have a top 10 tier one list or top 50. I'm kidding.
What do you see there?
[00:33:16] Speaker B: There are three notable ones. So Frase was right there at the top of the list. They're doing a lot of really amazing things. The biggest one that really, really made it tough for us as a call was the automated asset curation, the cleaning of the translation memories, the term basis, it's the worst manual process. There are no good tools for it. And Phrase this year launched a tool that not only cleans the translation memories, just with a few clicks on their website advertise few clicks, that's what we're talking about going in that movement. So important but naturally by doing so improves the quality of any custom AI MT model by giving them cleaner, smarter training data. And we all know that's the only way to get a precision and no one's addressing that pain point really other than them. I remember me looking for this about a year ago and it was a lot of really abstract tools but nobody was focusing on it.
They also had a AI powered MT auto select. So basically Fraser's system will automatically select the optimal MT engine for each job based on domain and Language pair that was a really good one.
Another really close one was Taos Epic. Nobody really, nobody really will again put put this on a traditional list but it's combining real time quality estimation and automatic post editing into one pipeline. It's basically making the LLM corrections where needed before a linguist ever sees the file. And Taos has a lot of data to validate against. Curating this data has been a multi decade endeavor for them way before we understood the importance of data.
You know I think YAP was a visionary in that way but they have some really impressive case studies like Uber using this Taos EPIC API saved over half a million dollars in year one which is like insane to think about. So something to keep an eye on.
[00:35:06] Speaker C: Demoed and worked with it. It is pretty. It's incredible. And again it's one of those kind of maybe you could say stealth or less advertised sort of things but extremely powerful, extremely useful. So many great potential implementations and use cases and actually another example just to sort of continue the thought here of how things can change overnight. Eddie and maybe this top 10 list of the year should become top 10 of the month really.
But it's actually something that would score super high in several categories including the maturing innovation category is something by BWX called the Free Flow Translation Editor. It's a finalist in Lockworld Process Innovation Challenge. It's being presented today in Sweden. And what it is is something that a lot of us have been waiting for is a translation environment without segments.
So this Free Flow Translation editor is context first which means you know, it pays attention to the meaning of the content, not just the words. It's natively multimodal natively which is important. It's not just building on top of some old crap. Excuse me. And it's again it's designed to replace this segmented translation workflow with chunk based full context editing.
There's a lot of technical details you know regarding this. It's, it's they, they even have it built into their smells engine that automatically walks watches for any spot where the meeting could drift from the original. You can audit the, the, you know, audit keep it, keep a list of the edits, changes. It's pretty incredible. So but just shows how dynamic and fluid this whole process of evaluating is. You know that we could dethrone somebody.
[00:37:02] Speaker B: The BWX one's not going to be really released until July but, but, but you, you know just as an example, like if we had seen this a month ago that probably would have really skewed the. You Know, it would have changed our minds and it could change our mind tomorrow, you know, so it is very dynamic list.
[00:37:21] Speaker A: I would just say that's great because we want to see more of this. We want to look more into, into the tiers. It's probably going to be some opinions on the tiers and I invite those in the audience. If you have your own tiers, bring them on. I think for those of us who create content, we understand what comes after you publish something. We understand that there are others listening, there are others reading, and we expect that.
I think those of us that appreciate content creation journalism to a certain extent, we appreciate opinions, we appreciate perspective, and we're always looking to get more of that. So I'm really looking forward to more updates on this. Veronica. Last year did the top news in artificial intelligence for the end of the year issue. So maybe this year we should do something like that. Maybe we could look into a few more technologies with the help of others. Let's see if you're listening to this and you think you can do it better. Let us know. Let us know.
[00:38:23] Speaker B: You right now, please let us know. Because we, we even said in the article we only feel like this is a starting point to the conversation. Like we, we think there's probably, you know, there's a million other nuances that we're missing and this is only, you know, ground one. But I think the biggest thing with product releases nowadays in the age of AI is I'm really looking at them as fundamental shifts of how we work more than individual products. So it's what made this list so hard and what was different about that last article. But how things are shifting, how we work is really what we're always looking at.
[00:38:59] Speaker C: And I think the five criteria we chose, what if they were a little bit different? Right. It could have changed the whole thing. We started out talking about literally this being a zoo of technology options. So always open to feedback. It always helps. And I think that, you know, this conversation basically reflects the complexity and the diversity of the industry and the pace.
[00:39:21] Speaker A: Of current innovation and the big opportunity that we have. I think, I think this industry really well positioned to innovate in so many different fronts. We are really lucky to be where we are at. There are other industries that they've heard of. Artificial intelligence, you just heard of it like really far. And then in 10, 15 years is when they'll be like, oh, that thing that you were talking about like 15 years ago. And like the industry will be like all in already the industry. I think for the most part has been all in into the adoption of different technologies, I think probably. And if you listen to localization today, you'll realize that we're always going back to the essence of the industry, which is language and then culture. And that's why I love gaming. But that'll be another conversation. Before we go, Veronica, Bridget, is there anything you want to tell your fans? Veronica has millions of them.
You and I, we have thousands. That's good as well.
Any message to your, to your audiences, to those that are listening to localization today?
[00:40:31] Speaker B: I think the biggest takeaway and my heart, you know, I saw my mother run her language company my whole life. Like, I know I'm a little bit newer, maybe, you know, last five years, but really the takeaway I really feel like from the most from this list is aside from the technology advancements, you know, linguists, you're not competing with Tier one content. You're not like, you guys are experts. You're in Tier two, you're in Tier three. I think it makes a very clear path forward on where you need to be focusing. You know, you're not in competition with that. Translation as a feature, which was one of the ones that were on our list is. It's a standard expectation now. Instagram, YouTube, Facebook, like billions and billions of users, their first interaction with translation is now through tier zero content. Like, nobody. You know what I mean? Like, we're not. You're not competing with that. You're competing, you know, you're specializing in tier 2 or tier 3. Focus on that. You're going to be fine. I think that's kind of just, you know, that was the clearest message that I had from this article.
[00:41:31] Speaker C: And I also think just the quantity of content out there is so much more than it ever has been. And like Veronica said, this, this tier 0 stuff is stuff no one would ever have been paid to translate. So. And again, I would end with what we discussed, this education factor of educating your clients. Whoever is buying your services knows zero to nothing about, about this industry, about how language works. If they're lucky, they're a tiny bit bilingual. But education is primary and we, you know, every industry has to sell and that's something that we maybe have to get better at.
[00:42:07] Speaker B: I hate selling, but yeah, we do.
[00:42:11] Speaker A: We'll bring someone who loves selling to talk about selling, how to sell. If you don't love sales, that would be the. That'll be our next.
[00:42:18] Speaker C: I love the industry. I love selling it.
[00:42:21] Speaker A: Fantastic.
[00:42:22] Speaker B: I'm scared of selling, of course.
[00:42:26] Speaker A: Thank you. So much, both of you. This, this really wraps up our conversation. So that wraps up our conversation with Veronica and Bridget Ilak, where we unpacked the year's top 10 language technology products and some runner ups. And we dug into a three tier pricing framework that ties value to intent. If you'd like to dive deeper, head over to multilingual.com to read their full article, the 10 Best Language Technology Products of the Year and why they Might Break the Current Pricing Model. Veronica, Bridget, thank you so much.
[00:43:01] Speaker B: Thank you.
[00:43:02] Speaker A: Thanks for listening to Localization today. Be sure to subscribe and rate us on Spotify and Apple podcasts and follow us on LinkedIn and Twitter for updates. We'll be back soon with more stories from the people shaping our industry. My name is Eddie Arrieta, CEO here at Multilingual Media. Until next time. Goodbye.
