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New Premium Opportunities For Linguists in Model Tuning 🔥🔥🔥⬇️ One of the most interesting points in my recent conversation with Jaap Van Der Meer, owner of TAUS was his view that smaller, specialist LSPs and experienced linguists are entering a moment of real new opportunity. Not secondary work, not leftover tasks, but work that sits close to the core of how AI models learn and improve. On the agile localization podcast by Crowdin, Jaap explained that TAUS can deliver strong generic QE models across more than 100 languages, but the moment you step into highly specialized fields like pharmaceutical, legal, automotive, or finance, the general models fall short. That’s where human expertise becomes essential. “We’re going to need subject-matter experts, linguists who know that field and that language, partnering with specialists to tune models for that particular purpose,” he told me. And this is exactly where smaller LSPs stand out. As Jaap put it, “There is more value for smaller specialized LSPs than for the generic super agencies… They know the terminology and the sensitivities from a language perspective in those markets.” Instead of competing on volume or turnaround, the advantage moves to those who can refine and nurture models in a focused domain. It’s work that includes validating domain-specific outputs, identifying edge cases, providing negative examples, refining terminology and grammar rules, and shaping custom models for narrowly defined use cases. None of this is volume work; it is precision work, and it is work that AI companies actively need. In Jaap’s words, the new LSP is becoming an AI Service Integrator, the partner who ensures that powerful models from big AI labs actually perform correctly in real industry settings. For linguists and boutique LSPs who built their careers on deep domain knowledge, this represents a new important business opportunity. There's riches in the niches, agreed?

The Most Overlooked QA Step in Localization = Fixing the Source 🔥🔥🔥⬇️ In this week’s episode of the Agile Localization Podcast, Shashi Bhushan drew on lessons from implementing AI workflow systems at companies like Amazon and Google to highlight a point that is both simple and strangely overlooked: Quality Begins At The Source. Before anyone debates MT engines, review layers, LLM routing, or any attempt to fine-tune output, the first question should be whether the source text is clean enough to support quality in the first place. Shashi walked through his approach in the interview. The moment content enters the system, he triggers an AI source-text agent that reads the entire file, checks for typos, catches glossary inconsistencies, and verifies alignment with the style guide. "Once the source text is ingested, I trigger the AI source text agent. It checks typos, glossary terms, and style issues so those errors don’t cascade into 10 or 20 languages. Fixing the source is the first real quality step." It sounds obvious, yet teams often search for improvements only on the target side. One typo in the source can multiply into twenty languages. One inconsistent term can ripple through every market. You either fix the problem once at the top, or you fix it repeatedly downstream. I am curious how others handle this in practice. Do you always validate the source before localization begins? Shashi’s approach reduces rework, protects brand consistency, and gives linguists a cleaner starting point. The clip below captures how he thinks about this stage in the workflow and why it matters more than people assume. Have you tried using AI for source-text quality checks? Where do you see the biggest gains when you clean the upstream content first? Would love to hear your experience. Check out the  full episode of the agile localization Podcast by Crowdin: Shashi and I go deeper into agentic AI workflows, multi-engine MT and LLM routing, human feedback loops, on-premises models for sensitive content, and how to design localization systems that scale without losing control. Links are in the comments.