AI

Today I’m proud to announce that Language IO now integrates Large Language Models (LLMs) from Google Gemini and DeepL NextGen directly into our translation engine. For our customers, this means a level of translation quality that traditional models simply cannot match. For prospects evaluating enterprise translation for the first time, it means the bar just moved.

Cost-first evaluations consistently favor options that are easy to start but difficult to sustain. The tradeoffs don’t become visible until later, when the system is already in use and the organization is committed. By then, changing direction is far more expensive than making a better decision upfront.

Your customers need support in their native language. Your budget should not be the reason they do not get it.

Most translation POCs end the same way: inconclusive results, stakeholders with different takeaways, and no clear path forward. Here’s how to run one that actually produces a decision.

Every enterprise exploring AI for customer support eventually arrives at the same fork in the road. One path leads toward building something internally with a model like Gemini or ChatGPT. The other relies on whatever translation capability is already bundled inside the CRM or CCaaS platform. Engineering teams assume the problem is mostly API calls and prompts. Platform buyers assume the built-in feature will be “good enough.”

You have done the right things. You built the training programs. You created escalation paths. You brought in consultants and rolled out resilience curricula and made sure every agent knew what to do when a customer crossed the line. The intentions were good. The investment was real. And your burnout rate is still 59 percent.

When a global brand starts looking closely at its customer conversations, the first surprise is usually not what customers are saying, but how inconsistent the company sounds in response. This becomes exponentially more difficult to control at the scale of Vista, a global leader in design and marketing services for small businesses, where millions of customer interactions span countries, languages, and support channels.

Agent burnout isn’t a people problem. It’s a P&L problem and it’s hiding in plain-sight across your turnover costs, satisfaction scores, and the calls that close clean but shouldn’t have. This is what it actually costs when the infrastructure protecting your agents hasn’t kept pace with what they are being asked to absorb.

For years, enterprises have treated language support as something that happens after the fact. Documents get translated later. Tickets are reviewed and localized in batches. Voice calls are routed to native speakers or external interpreters when available. This model made sense when customer engagement moved slowly and interactions were easy to pause. That world no longer exists. Today, most meaningful customer conversations happen live, under time pressure, and with very little tolerance for friction.