Global enterprises of all types have long struggled to provide language services quickly and accurately enough to those who need them, without overstretching tight budgets.
Now, artificial intelligence is bringing smarter, more adaptable solutions for these problems within reach. But what does that actually look like in practice? Can AI reduce wait times for interpreters, improve translation consistency, and handle high volumes—without compromising quality or security? We spoke with Dan Nelson, our Chief Technology Officer, to break down how AI is reshaping the industry and to find out what BIG Language Solutions is up to.
The Shift from Manual Processes to Technology-Driven Solutions
Q: Dan, tell us about your background. How did you get into language technology?
I started out in electrical engineering, focusing on digital and processor design, but I quickly found myself drawn to software and automation. I worked at a startup building warehouse management and time-tracking systems, which gave me hands-on experience developing technology that makes complex processes run more smoothly.
In 2006, I joined Language Link, a growing language services company. At the time, over-the-phone interpreting (OPI) was still a manual process—clients would call in, a switchboard operator would answer, and then they’d have to track down an available interpreter. It was slow, frustrating, and inefficient. There was a clear need for a faster, more reliable way to connect clients with interpreters.
To fill that need, we built an easy-to-use OPI platform, replacing switchboards with an automated system that could route calls, assign interpreters, and manage scheduling without human intervention.
Laying the Groundwork for AI with Automation
Q: An evolution of that platform is still in use at BIG Language Solutions today, right? Can you tell us more about it?
Yes, the foundation of what we built is still in use, but it’s grown into something much more advanced over the years. Today, it supports over-the-phone (OPI), video remote (VRI), and on-site interpreting, allowing organizations to manage all their language services in one place.
One of the biggest challenges in interpreting is getting the right interpreter on the line quickly—especially when handling hundreds of languages and specialized industries.
Our skills-based routing system solves this by analyzing multiple factors in real time—language, subject matter expertise, certifications, past performance, and client preferences—to match each request with the best available interpreter. While a typical call center might have four or five queues, our system might manage 10,000 queues, each one requiring highly specific combinations of languages, skills, and other interpreter characteristics.
I’ll give an example: if a hospital needs a Spanish-speaking interpreter with medical training for an OB/GYN appointment, the system will route to the lowest-cost available interpreter who speaks Spanish, is female to make sure the patient feels comfortable, and understands medical terminology.
Our platform offers other improvements, too, like:
● Faster connections, 24/7, which are critical for emergency services, hospitals, and government agencies where delays aren’t an option.
● Smarter interpreter management through our InterpVault™ system, which tracks interpreter skills, certifications, and availability in real time so clients always have qualified support.
● Easy workflow integration, with access to OPI, VRI, billing, analytics and support all in one place.
● Actionable reporting to help clients can track interpreter usage and monitor call performance to optimize resources.
AI in Action: How BIG Language Uses AI in Interpretation and Translation
Q: So automation has already changed the way language services operate. How is AI building on those advancements today?
Automation was the first leap forward. Now, AI is helping us extend those improvements even further, improving quality control, interpreter workflows, and translation efficiency.
AI in Translation: Accuracy, Speed, and Security
AI is making the biggest impact in handling high-volume, repetitive content—things like standardized forms, medical instructions, legal templates, and business documentation. These are areas where speed and consistency matter most, and AI helps process large amounts of text efficiently while keeping terminology accurate. We translate tens of thousands of these types of documents each month for some of our larger clients.
As you can imagine, data security is a major concern in these industries. Many providers use Neural Machine Translation (NMT) engines hosted on the NMT providers’ cloud servers, which adds an extra element of risk.
We host NMT engines on our own in-house, secure servers, so sensitive client data stays fully protected within our system. And we never use client data to train public AI models, so private information remains private.
We use three types of AI-powered translation engines, depending on the content:
● General-purpose AI models are best for high-volume, low-risk translations like FAQs and internal communications. We have models that operate securely within our infrastructure, keeping client data private when necessary.
● Industry-specific AI engines are designed for highly regulated fields like healthcare, legal, and finance, where compliance and terminology precision are critical. These models are trained on carefully curated, secure datasets for improved accuracy.
● Client-trained AI models are customized using a company’s own translation memory and terminology, so translations match brand voice, internal language, and industry-specific requirements. With these models, we typically see quality scores that are 30 to 40 points higher compared to off-the-shelf engines. Clients have full control over their training data and can request updates or removal at any time.
To improve accuracy, we also use AI-powered quality estimation tools that flag potential errors before human reviewers step in. This allows linguists to focus on areas that need attention rather than reviewing content that’s already correct, improving both efficiency and consistency.
AI in Interpretation: Quality Assurance and Performance Optimization
Real-time interpretation is far more complex than translation—there’s no time for corrections, so AI plays a different role. Right now, we use it to improve interpreter training, optimize performance, and maintain service quality.
One of the most valuable applications is AI-driven call analysis. Instead of manually reviewing a small sample of interpreter calls each month, we can assess more interactions, giving us a clearer picture of service quality, response times, and accuracy.
AI also helps with interpreter training and feedback. Trainers can review AI-generated transcripts, spot areas for improvement, and provide more targeted coaching. This means interpreters receive feedback based on real data, not just occasional call reviews.
Also, using AI for automated QA in interpretation is in our 12-month roadmap.
What’s Next for AI in Language Services?
Q: What advancements do you see coming next?
When it comes to translation, AI just keeps getting better.
AI engines are becoming more responsive, learning from human feedback instead of relying on periodic retraining. Automated post-editing is also advancing, allowing AI to fix mistakes before
human reviewers step in. As quality continues to improve, we’ll be able to translate increasing amounts of content without any human post-editing at all, though it will remain important for critical documents.
But even as off-the-shelf engines improve, customization will continue to be key for industries with complex terminology and strict compliance requirements.
For real-time interpretation, AI is advancing, but accuracy is still a challenge. As we touched on earlier, it’s especially tricky because there’s no time for a human to review the output. There are three potential points of failure: transcription, translation, and then turning that translation into spoken words. Because of this, while AI is already assisting with lower-risk, high-volume interpreting tasks, its role in critical conversations—like legal, medical, and emergency settings—will take longer to develop.
The next big developments will likely include better speech-to-speech AI, smarter multilingual captioning, and AI models that refine their output based on real-world interactions.
Humans will still be needed, but often in different capacities—many translators are adding AI prompt engineering to their skillsets, for example.
Moving Towards a World Without Language Barriers
Q: What excites you most about where AI is headed?
The progress we’ve seen in just the last few years is incredible. AI is already making language services faster, smarter, and more scalable, and as models improve, the quality will keep getting better. What excites me most is the potential to remove language barriers in ways that weren’t possible before—whether that’s faster access to interpreters, real-time AI translation, or hybrid systems that combine AI and human expertise.
Q: What advice would you give to global companies looking to incorporate AI into their workflows?
AI isn’t something you just switch on and expect perfect results. It’s no silver bullet. It works best when customized, integrated into existing processes, and backed by human expertise. The most effective AI strategies focus on security, accuracy, and adaptability—choosing AI solutions designed to work alongside human teams.
Looking to make AI part of your language strategy? Let’s talk about how AI-powered solutions can improve efficiency without compromising quality. Contact us to get started.
