SlatorPod

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.