Bryan Forrester Podcast Transcript
Bryan Forrester joins host Brian Thomas on The Digital Executive Podcast.
Welcome to Coruzant Technologies, home of The Digital Executive podcast.
Brian Thomas: Welcome to the Digital Executive. Today’s guest is Brian Forrester. Brian Forrester is the CEO and Co-founder of BoostLingo, a language technology company based in Austin, Texas. He co-founded the company in 2016 with Brian Dino and Dita Rouge to modernize and scale global language access using flexible software driven solutions.
Prior to Boost Lingo, Brian founded and successfully exited Anchor Software and IT services firm with a background in technology sales and a personal connection to language equity. He launched BoostLingo to build tools that serve both interpreters and the organizations that depend on them.
Well, good afternoon, Brian. Welcome to the show.
Bryan Forrester: Hey Brian. Thanks for having me. Good to be here.
Brian Thomas: Absolutely, my friend. I appreciate it. And we’re in the same time zone today. You’re in Austin, Texas. I’m in Kansas City and that’s always nice, sometimes because there are days I literally get up at 4:00 AM to do a podcast maybe in India or or Taiwan or wherever I’ve been.
So I, I appreciate you making the time. Brian, let’s jump into your first question. Boost Lingo set out to make requesting an interpreter as easy as requesting a ride. How did that analogy shape your early product development and user experience?
Bryan Forrester: That’s a great question. I’ve started a lot of technology companies and when we founded Boost Lingo in 2016, the language interpreting market was still relying on outdated technology.
Many competitors at the time had built very strong large businesses, right? It was a very large market. But their systems were, you know, running on antiquated frameworks and old infrastructure. There were no APIs there. None of these platforms were cloud native or born in the cloud, as they say. And at that time, in 2016, which seems like a universe ago at this time.
Getting an interpreter, a remote interpreter was not necessarily easy. Oftentimes, it required a landline phone where you’d have to go through a lengthy menu system, and you’d go through operators who asked multiple questions before finally routing a call to a language interpreter. So it wasn’t a great, what I would describe user experience.
We saw an opportunity really to transform that process. What once took several minutes. We believed we could reduce that down to several seconds. So at the fingertips, right? Just get an interpreter on demand. That was sort of the vision early on. We thought we could build a better mousetrap, and we were convinced that there was a better way to do it.
And so from the beginning we, we set out to build a user-friendly platform agnostic solution, right? Leveraging mobile apps or web-based calling. We knew customers wanted that flexibility to connect with interpreters from any device. Whether it be mobile, web, landline, or even onsite scheduling, right? That used to be a very antiquated process to have to figure out how to dispatch an interpreter to come to the doctor’s appointment, for example.
So our technology made that seamless and efficient, and that’s really, I think one of the things that made us successful was kind of focusing on that user experience early.
Brian Thomas: Thank you and I appreciate that. You know, you started from the ground up and just rethought the whole process. And again, having that innovative mindset certainly helps.
But yeah, 20 16, 10 years ago, just about it does seem like a, a decade or a universe ago, as you said. I really like talking to entrepreneurs, founders that really rule up their sleeves, jump in and say there is got to be a better way. Better, faster, smarter, cheaper is the way they say it, but I appreciate the backstory on that.
Brian Boost lingo reportedly grew by 50% or more each year, and now supports over 17,000 interpreters and 160 employees. What strategies, culture or innovations fueled that sustainable hypergrowth?
Bryan Forrester: That’s a really good question, and I could probably talk for 20 minutes on this topic, but I won’t do that. I mean, I think I kind of narrow it down to some of the, I guess the most important lessons I’ve learned as an entrepreneur early on about running a successful technology startup.
You know, I think number one. You’ve gotta have a clear mission and well-defined quarterly objectives. I would argue even in the pre-revenue stages of a startup, you know, most people think about like early, early stage startups is just total chaos, right? People wearing multiple hats and there is no structure, there’s no design.
Everybody’s trying to be all things to all people. But I think. Focus, even in those early days is really critical because it’s so easy to get distracted by, I guess, enticing opportunities that appear. Along the way. You know, you think about when you’re an early stage startup and you see large customer opportunities, you’re really asking them, you know, how high do I jump?
Like what? What can I do to please this potential prospective customer? You end up going down rabbit holes chasing these large customer requirements. In the hopes of building some features that might generate quick revenue. And I think we were really disciplined early on. Of course, we made some mistakes.
There were definitely some, uh, punch holes in the wall moments, uh, at least figuratively. But the way to avoid that trap, I think, again, is staying grounded in your mission and really aligning around a product roadmap that everyone, all stakeholders commit to. So for us, for example, in bus lingo, we could have built a bunch of translation features, which is very different.
Translation technology is very different than interpreting technology, for example. But we were disciplined not to go do that, even though several of our customers were asking us to do that. We knew that we had to stay focused on the interpreting workflow early on. Otherwise that distraction, you try to do too many things and you don’t, won’t do any of them well.
And so I think if anything surfaced, you know, outside the scope of these goals that we were focused on, the answer had to be no. And, and we got better at saying no as time went by. But I think a lot of startups don’t do that, and then they really struggle. And then I think another principle that served us well.
If I think about it was really cultivating a, what I call a bottoms up culture. We really focused on hiring people who could teach us not, you know, hiring people to just tell them what to do. Rather hire people that would tell us what to do and challenge us and really guide the direction of the company rather than simply waiting for us to tell them what to do.
And so by creating this sort of inclusive environment where trust is given, not earned, right? We hire people ’cause we trust them to do their job. They don’t have to prove it to us. I think that we built the conditions for our employees to really thrive and innovate. And I think one thing that we keep track of is A KPI called ENPS, right?
An employee net promoter score. How happy are your employees? Um, are they learning? Are they developing, are they thriving? And we have always been sort of off the charts on that metric. And that’s a really key metric because you don’t have employees leaving voluntarily, right? They, they really love working at Boingo.
And if you hire the right people and they love working for you, you know things are gonna go well.
Brian Thomas: That’s awesome and I appreciate that. And I highlight just a few things here. Some of the most important lessons you learned as a tech entrepreneur is having that clear focused, well-defined goals, that mission, right.
Especially early stage, making sure everybody’s aligned, committed to the same vision. You know, having discipline to stay focused on your core product, right? Your core vision, and then that bottoms up culture, I think is important. Hiring the people, as Steve Jobs said, you know, hire really good people and get outta their way.
Let them do what they do best. So I appreciate that, Brian. The next question I have for you, you’ve commented on comment, misconceptions about ai. Telling us AI won’t replace interpreters, but instead expand their capacity for complex work. How are you integrating AI thoughtfully into your product suite without undermining professional interpreters?
Bryan Forrester: Well, this is such an important question and it’s something we think about a lot, right? You should take a step back. I mean, this is an exciting moment of rapid technological change, right? I think you and I can agree on that. Things are changing rapidly and. When you think about Bingo’s mission, which is really to expand language access through technology innovation, these remarkable advancements in AI really feel like, at least to me, a dream come true.
I mean, they open up new possibilities for breaking down language barriers and making language services more accessible than ever before. You know, just recently I was reading in the United States alone, more than 60% of limited English proficient individuals, what we call in the industry, LEP. In the United States, they lack access to professional interpreters during critical and regulated conversations.
So those are conversations with their attorney, their banker, their doctor, right? They don’t have access to an interpreter in 60% of those interactions. So it’s better from our perspective to have AI access than no access. And if you consider sort of the patient journey in a large healthcare system, right?
In the United States, it’s a complex healthcare system that we have, but a patient might have access to an interpreter when they’re meeting with their doctor right at point of care. But what about when they’re getting their intake done in the reception or they’re calling in to schedule their appointment?
We’ve all gone through those iv, those sort of landline menu systems, right? Press one for Spanish, but what about Haitian Creole, or what about Arabic or all these other languages, right? They don’t have access to any help in setting up their appointment and going through those intake forms. So there’s really a language desert and AI can fill those gaps.
And so that’s a big win for us because again, for us it’s all about breaking down language barriers and technology innovation is going to help us break down more language barriers than ever before. But that doesn’t mean that AI is going to replace human interpreting. There’s something that we talk about a lot in at Blingo called, uh, the decision tree that we help kind of walk our customers through and, you know, when should you use AI and when should you use humans?
And you know, it’s kind of on this scale of low complexity to high complexity and low risk to high risk, right? If you’re in a high risk, high complex conversation, right? You’re talking to your doctor about cancer treatment. And it’s a highly complex conversation. You’re not gonna want to use AI for that.
First of all, it’s a very high risk, right? And even to this day, with all the advancements in ai, for most languages, AI is only as about 95% as accurate as a professional human interpreter, but for low risk, low complexity conversation. It makes a lot of sense to use ai. So again, a lot of our customers are cautious about relying on speech to speech AI in high stakes critical conversations, and I think they should be.
But you know, those same customers are ready to embrace AI when it. Enhances rather than sort of replaces interpreting. For example, a lot of our customers are asking for things to enhance their interpreting experience. They still want a human interpreter, but they want a meeting transcript and a summary that will feed into their medical records system.
They want real time captioning, you know, to tell them on the screen what is being conversed or they may want like language detection tools because. It’s an emergency room and the patient just came on a gurney and they don’t even know what language they speak, right? AI could do language detection, so there’s lots of really cool things that AI can do to enhance that.
But the bottom line is we, we really don’t see AI replacing human interpreters anytime in, in the the near term future because. And people that are familiar with, with the job that a human interpreter does, understands the nuance, the emotions, you know, things like sarcasm, those emotional inflections in language can be very hard for AI to pick up and get right.
So our belief is really simple. AI, where it fits, and human when it counts.
Brian Thomas: Thank you, Brian. I appreciate that. I agree with you right now. AI can only make this human process, human interpreters better at this point, obviously, AI can fill in the gaps. Like you had mentioned, manage some basic intake forms, fill in those little gaps, I believe and, and what I heard is this AI, human partnership is helping to really break down language barriers.
And you did highlight high risk versus low risk, high complexity versus low complexity, and that’s where you kind of say, okay, a human is needed for this versus a machine and vice versa. So I appreciate that. I really do. Brian, the last question of the day is we look ahead, how do you see language access evolving, especially in areas like intelligent routing, AI captioning, or enterprise expansion?
Bryan Forrester: It’s for me, AI is here to stay, right? This is not like a, a short term fa I think we could all see that it’s having a lasting impact. And as somebody who works in a technology company, I don’t just use AI as a, a Google search bar. I use it as a mentor almost. And there’s a saying, AI as your os, it becomes sort of integrated into your everyday work.
And I think as that continues to happen throughout society. It’s going to rapidly evolve and change industries completely, and we’re no different, right? The only thing I know for sure is that if Boost Lingo doesn’t change rapidly, we won’t continue to grow rapidly, and we’re constantly reminding our team of this.
I think you’re gonna see. Things like, for example, we have a call routing algorithm right now that we could put AI in the call routing algorithm to make more intelligent decisions around how we, it routes calls to which professional interpreter. It could help us reduce response times. You know, we already have really fast response times, but it could make it even better.
It could enhance quality. Right now we have thousands and thousands of interpreting calls. That go through our platform and we have to have humans review the quality of those calls. Every single, you know, call. We have to review to make sure the interpreter did a good job. AI can help us streamline that.
AI can actually test quality and allow us to quickly figure out which interpreters may need to be retrained. So that’s just one example. You know, AI captioning is gonna become more important. You know, one of the, the languages that we support is American Sign Language, right? For the deaf and heart of hearing community.
But think about all the old, older Americans who, you know, they’ve lost their hearing and they don’t know American sign language, right? So when they go to the doctor, they just can’t hear. And so imagine how powerful real-time captioning can be, right Where when the doctor talks, they can at least read what the doctor’s saying in real time.
And you don’t need a professional human transcriber out there typing in the captions. That’s why you actually see a lot of these AI captioning startups raising a lot of money. And I think the winners here, there’s a lot of AI companies, right? Trillions of dollars invested in venture capital into AI startups.
So who wins in the long run? And one of the parts of your question was enterprise expansion, right? We believe the winners will be the ones that have the best user experience. And that create competitive moat. In other words, it’s a workflow, not a point solution, right? If you think about Google Translate, right?
That’s more of a point solution. I’m at the restaurant trying to order sushi. I can use Google Translate to to help me translate a single sentence. But if I’m in a hospital and I’m a patient, I don’t just need to have AI interpret the conversation. I need that meeting summary. I need that transcript. I need it fed into the EHR system that the hospital uses.
So there’s all of these additional workflow aspects that make the software more sticky, and that workspace is extremely important. So again, I think that AI is going to increase GDP dramatically, and it’s gonna create, uh, thousands of successful startups. It’s gonna make a lot of people a lot of money, and it’s really exciting to work kind of on the forefront of this.
In our industry. I, you know, I feel like it’s almost like we’re a startup again, even though we’re continuing to grow, we’re adding more employees. You know it, it’s exciting to work on this new technology that we know will add value to our customers.
Brian Thomas: Thank you. I appreciate that and sharing your insights of where AI is going and how it’s being used today and highlighting a couple things.
I think AI is certainly there as every entrepreneur and founder knows that you’ve gotta continue to improve your platform and service to stay in business, and AI can certainly help augment that. I liked how you said AI can do a lot of the QA now. I think that’s important. It takes humans outta the mix to do higher level tasks.
The one thing I definitely took away on this AI captioning is we do have a large population of elderly that don’t know sign language and they’re hard of hearing. And this realtime AI captioning is, is certainly a great use case. So I appreciate you sharing that with our audience today. Brian, it was certainly a pleasure having you on today and I look forward to speaking with you real soon.
Bryan Forrester: Thanks, Brian, it was a lot of fun. Thanks for having me on.
Brian Thomas: Bye for now.
Bryan Forrester Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
