SlatorPod

Florian and Esther return with a packed roundup of key stories in language solutions and AI. They cover AI voice controversies, strategic acquisitions, financial results, and the growing importance of sovereign AI.The duo starts with a German voice actor's planned lawsuit against Netflix over alleged unauthorized AI training, alongside new SAG-AFTRA protections requiring consent for digital voice replicas. They also look at YouTube’s plans to introduce AI dubbing for live streams and ElevenLabs’ push into real-time speech translation. Meanwhile, Esther explains why Reddit’s AI-translated content, initially a promising growth strategy, may now be hurting its Google search visibility.Turning to M&A, Esther breaks down Deepgram’s acquisition of Daily, which brings voice-agent infrastructure closer to its speech technology, and TransPerfect’s purchase of clinical and regulatory writing consultancy Synterex, extending its reach into regulated content creation.Florian highlights RWS’s completed Acolad acquisition and return to modest organic growth, even as its localization business remains under pressure. Esther reviews Appen’s strong third-quarter performance, driven in part by China and generative AI projects, while AI-Media forecasts increasing recurring revenue despite modest overall growth expectations.They then explore Cohere's latest funding round and multibillion-dollar valuation, considering the strategic importance of sovereign AI infrastructure and investment from Canadian and European backers.Finally, they discuss California's decision to cancel an AI translation procurement process for court services, highlighting concerns over accuracy, previous technology deployments, and the tension between automated translation and human language access.

Gabriel Fairman, CEO of language technology platform (LTP) wxrks, joins SlatorPod to discuss advances in AI translation quality, the changing role of human expertise, and how LTPs must adapt to an increasingly AI-driven industry.Fairman explains how wxrks has evolved beyond the conventional translation management system (TMS) model, incorporating AI capabilities directly into its workflows. He argues that as AI makes software development easier, enterprise software's value increasingly lies in its security, support, maintenance, and ability to handle complex business requirements.Discussing wxrks’ latest translation quality research, Fairman points to significant improvements in context-sensitive AI translation, with the LTP's 2026 study showing lower average edit distances than traditional neural machine translation. However, he stresses that achieving high-quality results still requires careful configuration, continuous testing, and human oversight.As fewer edits become necessary, Fairman sees growing value in capturing the reasoning behind human corrections and using that feedback to improve AI translation workflows. He introduces wxrks' translation reasoning feature, which provides greater transparency into AI-generated translation suggestions, and Free Flow, a segmentless editing environment designed to support context-aware rewriting, subtitling, and multimedia workflows.The CEO remains skeptical that agentic workflows will replace traditional enterprise software or that MCP integrations will become the primary way users interact with it. However, he sees significant potential in using MCP to connect and analyze data across systems.Turning to pricing, he predicts that traditional per-word pricing will become increasingly difficult to justify as AI reduces the amount of text requiring human intervention. Instead, he sees value shifting toward human judgment, quality validation, and frameworks that identify where expert intervention is needed.Looking ahead, Fairman identifies organizational transformation as a major challenge. As AI reshapes established roles and workflows, he believes companies that can navigate structural change, build trust, and help employees redefine where their expertise adds value will gain a competitive advantage.

Gato Plugins Founder Leonardo Losoviz joins SlatorPod to discuss how advances in AI translation are making multilingual websites more accessible, more affordable, and easier to manage.Leonardo explains how his background in software development and experience with multilingual Asian markets inspired him to create Gato AI Translations, a WordPress plugin that automatically translates website content upon publication, using AI models such as ChatGPT, Claude, and Gemini. He highlights the growing opportunity for website owners to reach international audiences as AI significantly reduces the cost and complexity of translation. While he has yet to observe a major shift toward multilingual websites in Southeast Asia, he expects adoption to increase.He also discusses Agent Bridge, another extension that allows users to translate content using existing AI subscription quotas rather than paying separately for API usage, offering cost savings in exchange for longer processing times.On website visibility, Leonardo discusses declining Google traffic and questions the conversion potential of generative search, emphasizing that traditional SEO remains essential.Leonardo also emphasizes the importance of contextual prompting, translation memory, and human review for high-stakes content. He explains how customizable prompts can function as glossaries, helping prevent recurring translation errors.Looking ahead, Losoviz is developing one-click translation for entire websites and exploring additional integrations. His broader ambition is to make AI-powered translation work across the WordPress ecosystem, regardless of how a website is built.

Artem Grigorev, CTO of AI data company Toloka, joins SlatorPod to discuss the growing importance of human expertise in AI development, the shift toward smaller specialized models, and the challenges of evaluating AI performance.Grigorev explains that while computing power and model capabilities attract significant attention, high-quality training data remains fundamental to AI progress. As models become more sophisticated, data creation has shifted from simple annotation tasks to complex, expert-driven workflows. He highlights growing demand for domain specialists, including PhD-level experts, as large language models increasingly automate simpler annotation tasks.Grigorev also discusses Toloka's work on post-training smaller models for specific enterprise applications. By fine-tuning models on targeted datasets, businesses can potentially achieve better performance, lower costs, and faster response times than they would relying on large frontier models.Turning to multilingual AI, he emphasizes the need to evaluate model performance across languages, rather than relying predominantly on English-language benchmarks.Toloka Arena, launched earlier this year, is the company's private benchmarking initiative designed to address concerns around contaminated public benchmarks and assess AI agents against realistic business workflows. Grigorev explains how Arena tests agents across multiple attempts to assess consistency rather than a single successful run.Grigorev also discusses the growing demand for robotics training data, including first-person videos of humans performing physical tasks, and the challenges of collecting such data at scale.Looking ahead, he argues that human judgment will remain essential, even as AI becomes increasingly capable, particularly in determining whether AI-generated outputs meet human expectations and preferences.

Adam Bittlingmayer, CEO and co-founder of ModelFront, joins SlatorPod to discuss the state of the art in enterprise AI translation and the language technology platform’s (LTP) launch of an AI-native open translation management system (TMS).Bittlingmayer says translation can appear solved when an AI model is given enough context and the right prompt, but providing that context consistently within complex enterprise localization workflows remains difficult. Enterprise teams must account for different products, content types, terminology, quality requirements, languages, and existing systems.The CEO revisits ModelFront’s core value proposition of using AI to check and fix AI, allowing enterprises to automatically approve translations that would otherwise require human review. Focused on high-stakes localization workflows, the LTP takes responsibility for translation quality and the engineering complexity behind it. He also highlights its outcome-based pricing model, under which customers pay only for translations that the LTP successfully approves.Bittlingmayer also talks about why neural machine translation models remain widely used despite advances in LLMs. He says newer models can benefit from much richer context, but existing enterprise workflows and software often make providing that context difficult. He sees verification as increasingly important as AI-generated content and translation volumes continue to expand.Bittlingmayer then introduces ModelFront’s AI-native open TMS, OpenLoc, which the company is making available for free, with hosting offered at cost. He argues that traditional TMSs were not designed for today’s AI workflows, creating friction that can encourage teams to bypass centralized localization processes altogether.He says the new platform is designed around openness, flexible AI use, prompt building, and MCP connectivity, allowing users to interact with localization workflows through enterprise AI assistants as well as the traditional interface. Bittlingmayer sees this as part of a broader goal to make AI more accessible while improving enterprise AI outcomes.ModelFront is launching OpenLoc through an early access program, with general availability planned to follow.

On SlatorPod, AudioStack Co-Founder and CTO Peadar Coyle discusses what it takes to deploy voice AI at enterprise scale, from localization and domain expertise to human oversight.Peadar explains how AudioStack enables brands, governments, and media companies to produce localized audio across accents, regions, languages, and individual products. Rather than betting on a single voice model, the company works with multiple providers, treating models as building blocks and selecting them according to language, accent, quality, and use case.The CTO shares how AudioStack moved away from developing its own text-to-speech models after deciding its competitive advantage lay in orchestrating technologies, delivering high-quality audio, incorporating brand guidelines, and applying domain-specific knowledge.Peadar also argues that humans will remain part of AI audio workflows as automated systems can increasingly identify potential quality problems, but brands still want editorial control over pronunciation, tone, and reputation-sensitive content.On synthetic voices, he highlights consent, vendor due diligence, transparency, and regulation, with AudioStack introducing metadata identifying AI-generated content.Looking ahead, AudioStack is investing in agentic capabilities, faster and more intelligent workflows, and a combination of proprietary technology and third-party models.

Florian and Esther discuss what mattered in language solutions and AI over the past few weeks, including key takeaways from SlatorCon San Francisco 2026, where AI ran through discussions across enterprise localization, voice AI, interpreting, data-for-AI, media localization, and venture capital.Florian covers Smartling’s majority investment from Vitruvian Partners as a positive development after a quieter period for language-related deals. He notes that Smartling plans to use the backing to expand AI translation and agentic localization, accelerate global growth, and pursue acquisitions.Florian examines early translation benchmarks for OpenAI’s latest model, GPT-6 Astra. He cites sources that find Astra performing well in multilingual performance but still shows some weaknesses on difficult translation tasks. He argues that expert oversight remains important for critical use cases.Esther breaks down Appen’s first-half results, highlighting strong growth in China driven partly by generative AI projects alongside continued pressure in its global business. She also looks at AI-Media’s continued shift from captioning services toward recurring technology and SaaS revenue.Florian turns to AMN Healthcare’s language services business, where interpreting volumes were flat, and pricing pressure weighed on revenue. He also addresses Straker’s financial difficulties following the discovery of roughly USD 5m in misappropriated funds.

Dmitrii Sandzhiev and Iurii Agafonov, two of the Co-Founders of NCSpeech, join SlatorPod to talk about building speech AI datasets in emerging markets, scaling data collection through superapps, and addressing the quality and infrastructure challenges behind voice AI.Dmitrii explains that NCSpeech turns idle time in superapps into AI training data by rewarding drivers, riders, and passengers for completing data collection tasks. The company connects AI labs, enterprise R&D teams, and sovereign AI initiatives with real-world audio, image, and video data collected through the platform’s partners.Dmitrii sees particularly strong demand for speech datasets in emerging markets, where languages, dialects, and code-switching remain underrepresented in training data. In Malaysia, for example, speakers frequently switch between Malay, English, Mandarin, and local dialects.Iurii highlights the technical challenges behind producing training-ready datasets, including controlling recording conditions, verifying speaker consistency, detecting synthetic submissions, managing local data storage requirements, and processing large volumes of audio.Alongside collecting data on demand, NCSpeech is building reusable dataset libraries and developing its own models. Dmitrii shares how its Kazakh speech recognition model now outperforms available open solutions, demonstrating its technical capabilities and attracting potential customers.Looking ahead, Dmitrii and Iurii discuss NCSpeech plans to expand into more countries and apps, secure longer-term data customers, strengthen their US presence, and raise a seed round.

Florian and Esther discuss the language industry news of the past few weeks, starting with RWS’s proposed acquisition of Acolad and what the deal means for consolidation among the largest language solutions integrators (LSIs). Esther rounds up further M&A activity, including t’works acquiring SwissGlobal, Alfatrad buying Lexic Language Solutions, Alpha CRC acquiring PureFluent, Magna Legal Services merging with Naegeli Deposition and Trial, R&A Translators buying Viva Translations, and Contents acquiring Balio.Esther and Florian examine Anthropic’s introduction of invisible watermarks for Claude-generated text in response to the EU AI Act. Florian questions how meaningful AI-content labeling will remain as AI becomes embedded in content creation and translation, while Esther points to transparency as the regulation’s underlying objective.Florian reviews ZOO Digital’s declining revenue, improving profitability, growing use of AI, and shift toward faster localization services and more fulfillment in India.The duo talks about how AI skills are increasingly appearing in language roles at organizations including NATO, the ICC, WIPO, Interpol, FIFA, and the IMF. Esther contrasts this with a more traditional language-access role in New York and OpenAI’s continued hiring of localization specialists to oversee AI-assisted workflows.Finally, Florian highlights continued investment in voice AI, with funding rounds for Fish Audio, Smallest AI, Omilia, and Gradium underscoring how crowded the speech technology market has become.

Artur Nowakowski, Co-founder and CTO of Laniqo, joins SlatorPod to talk about the language technology platform’s (LTP) origins, business model, and research-driven approach to AI translation.Artur shares that Laniqo emerged from machine translation research at Adam Mickiewicz University after his team won a WMT 2022 shared task, attracting PONS Langenscheidt, which became the company’s main investor and helped commercialize the university’s technology.Laniqo initially developed its own neural machine translation models but shifted toward open-source large language models as their translation capabilities improved. According to Artur, controlling and adapting these models remains essential for domain-specific use cases and lower-resource language pairs.He highlights Laniqo’s work with Central and Eastern European ecommerce platform Allegro, where the LTP supports the translation of hundreds of millions of product offers. Key challenges include scalability, cost control, terminology, limited source context, and detecting critical errors across volumes that human linguists cannot review manually.Laniqo is also developing quality estimation tools that identify error spans, assign MQM categories, and suggest corrections. Its recently published ForMaT dataset supports research into PDF translation that preserves document layouts without relying on conversion to Microsoft Word.Looking ahead, Artur outlines how Laniqo plans to expand beyond text translation into voice, images, and broader language AI applications, while continuing to prioritize privacy and deeper domain adaptation.