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Florian and Esther discuss the language industry news of the past few weeks and Slator’s newly launched website, which reflects a clearer positioning around research, advisory, consulting, events, and market intelligence.
The duo breaks down the 2026 Slator Index, highlighting that while revenues appear to have grown, this does not signal real market expansion. Instead, growth is concentrated among a few large players, often driven by acquisitions, while many companies report declining revenues.
Florian touches on the RWS–Cohere strategic partnership, with RWS strengthening its technology stack by integrating advanced AI translation, while Cohere gains enterprise distribution. The move reflects a broader trend of companies recognizing they cannot build everything in-house.
Off the back of Slator’s Data-for-AI Market Report, Florian sees AI data services as a major growth opportunity. He explains that the industry’s bottleneck has shifted from building models to making them usable in real-world settings. Esther notes growing interest from companies exploring acquisitions and investments in this space.
Esther wraps things up by talking through recent M&A and funding deals, including Star7’s private equity buyout, GlobalComix’s expansion into manga localization with the acquisition of INKR, and VoiceLine’s EUR 10m funding round in voice AI.
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Paul Carr, CEO of Welo Global, joins SlatorPod to talk about the company’s strategic repositioning, continued AI investment, and evolving demand in the language solutions industry.
Paul notes that the company has narrowed its focus to a few core areas and reorganized around client segments. He adds that client centricity and specialization have been central themes, alongside increased investment in AI and data engineering.
The CEO highlights that two-thirds of Welo Global’s revenue now comes from outside of traditional localization departments. He says the business increasingly serves content owners such as legal teams, clinical managers, and AI labs.
Paul describes the launch of Welo Global as a branding shift to reflect this broader scope. He explains that the new structure includes five client-facing brands tailored to specific industries and use cases, including Welocalize, Welo Data, Welo Life Sciences, Park IP, and Adapt.
The CEO emphasizes that AI has driven major change, particularly through the development of the company’s Opal platform. He says the system delivers significantly higher-quality output than traditional machine translation by using agentic workflows and enterprise-specific data.
Paul argues that localization ROI is difficult to isolate because it is usually part of broader investments like sales and marketing. He suggests simplistic ROI models risk undermining credibility.
He concludes that demand remains strong and success will depend on adapting quickly, building new capabilities, and maintaining a culture that embraces continuous change.
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Ben Faes, CEO of RWS, joins SlatorPod to talk about the markets’ perceptions of LSIs, the company’s AI strategy, and how RWS is repositioning itself for long-term growth.
Ben positions RWS as a technology-led partner helping enterprises operate globally, from enabling multilingual communication to protecting intellectual property and improving market understanding.
The CEO highlights the rapid acceleration of innovation and the democratization of AI, where individuals and companies can now build and deploy solutions at unprecedented speed. He argues that the real opportunity lies in using these capabilities more effectively, rather than applying them to low-value tasks.
He describes the partnership with Cohere as a fundamental shift, with RWS integrating Cohere’s models into its Language Weaver Pro platform, moving beyond traditional, segment-based translation toward context-aware, LLM-driven solutions.
Beyond translation, Ben sees strong growth in AI data services, especially in areas like cultural intelligence and multimodal training, where human expertise remains critical.
Internally, RWS has reorganized into three divisions — Generate, Transform, and Protect — to better align with customer needs, buyer personas, and evolving use cases.
Despite short-term uncertainty, Ben remains optimistic, noting that new AI-driven services and products account for a growing share of revenue and signal how quickly the market is evolving.
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Jessica Powell, CEO of AudioShake, joins SlatorPod to talk about how AI-powered audio separation is making audio more usable for both human and machine workflows, and enabling new use cases across localization, broadcasting, and media production.
Jessica emphasizes that early traction came from the music industry, particularly in areas like sync licensing and remixing. However, the company’s expansion into film and television happened organically as new use cases emerged.
The CEO explains that AudioShake’s core technology uses source separation to break complex audio into individual components such as dialogue, music, and sound effects. She describes how this allows users to gain precise control over audio for tasks like editing, transcription, and multilingual dubbing.
In localization, Jessica highlights how separating dialogue from music-and-effects (M&E) tracks enables both traditional dubbing and AI-assisted workflows, particularly for legacy content where original stems are unavailable.
Beyond localization, Jessica underscores the importance of clean audio inputs for speech recognition systems. In noisy environments like sports broadcasts or unscripted content, separating dialogue before transcription significantly improves accuracy.
Jessica also reflects on the broader AI landscape, noting that the rise of generative AI has increased awareness of audio as a critical modality. However, she distinguishes AudioShake’s work as non-generative, focused on extracting structure rather than creating new content.
The CEO discusses the current funding environment in the Bay Area and how the investor narrative has evolved leading up to AudioShake’s late 2025 Series A.
Looking ahead, Jessica points to real-time processing and copyright-compliant audio editing as key areas of innovation, as the company continues to expand its role in media and AI ecosystems.
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Daniel Sebesta joins Florian and Esther on the pod to talk about the latest language industry news, AI translation developments, and key insights from the Slator Pro Guide: Growth Hacks for Language Technology Platforms (LTPs).
The trio begin with TransPerfect’s latest financial results, which reported USD 1.32 billion in revenue, up 7% year on year. They also discuss leadership changes at Straker, where founder Grant Straker stepped down as CEO after more than 25 years.
Florian shares new AI-powered contextual features in Google Translate that allow users to refine translations and adjust tone or phrasing. Daniel believes these interactive capabilities aim to improve trust in AI systems by giving users more visibility and control over translation outputs.
The discussion also turns to ElevenLabs and its partnership with Deutsche Telekom to embed live translation into phone calls. The integration could enable real-time multilingual conversations, summaries, and contextual assistance for telecom customers.
The trio then cover Walmart’s internal AI localization initiative, where the system now translates millions of catalog items across 22 languages while reducing translation costs by about 99%.
Daniel concludes by outlining the Growth Hacks Pro Guide, which explores strategies for scaling LTPs. He highlights areas such as go-to-market strategy, partnerships with language solutions integrators, enterprise sales execution, and security readiness as key drivers of scalable growth.
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Georg Ell, CEO of Phrase, returns to SlatorPod for round 3 to talk about how the language technology platform (LTP) is evolving amid the AI boom and the shifting dynamics in enterprise SaaS.
Georg shares how Phrase has doubled down on a platform and ecosystem strategy that encourages customers to build solutions on top of the LTP’s system rather than forcing them into a closed system.
The CEO addresses the broader AI narrative affecting SaaS companies and explains that investor uncertainty about long-term software value has created anxiety across the sector.
Georg argues that the AI boom has triggered a “build vs buy” debate inside many enterprises, with engineering teams experimenting with internal solutions. He explains how the gap between building a demo versus running a reliable, scalable system is where most internal projects fail.
Georg notes that core AI translation quality improvements seem to be plateauing, but AI continues to significantly enhance the layers surrounding translation. He highlights improvements in context handling, evaluation, automated post-editing, and orchestration that allow companies to translate more content at lower human review rates.
The CEO says localization must move beyond cost reduction narratives and instead focus on business outcomes such as hiring efficiency, support performance, and revenue metrics.
Georg predicts 2026 will bring more production-grade AI applications, including personalization, multimodal content, and automation across the enterprise. He believes language technology will be framed as content adaptation and delivery rather than simply translation.
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Daphne Tay, Founder and CEO of Bluente, joins SlatorPod to talk about building an AI-powered document translation platform that goes beyond text and tackles the complexities of formatting at scale.
Daphne explains that formatting challenges vary significantly across file types, from scanned PDFs to multi-column layouts and complex graphics, requiring deep technical handling of document structures.
The CEO points to legal and financial services as core verticals, citing the example of investment banking teams uploading hundreds of pages overnight to meet tight deal deadlines.
Daphne discusses how large language models have accelerated translation quality and increased market openness to AI adoption, especially among legal professionals who want to reduce time spent on non-billable translation tasks.
She highlights that human reviewers still remain essential for court filings, arbitration, and high-stakes documents requiring certification or final sign-off.
Daphne shares that Bluente raised funding to expand internationally, increase brand visibility, and partner with investors experienced in scaling B2B SaaS and AI businesses.
The pod wraps with Daphne outlining a forthcoming feature that enables temporary translation memory, allowing only recently edited sections of a document to be retranslated while preserving previously approved text.
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Slator’s Head of Research Anna Wyndham joins Florian on the pod to discuss Slator’s new Pro Guide: Growth Hacks for Language Technology Platforms, describing it as a practical playbook for turning strong AI products into scalable revenue.
Florian highlights ElevenLabs’ USD 500m raise at an USD 11bn valuation and Synthesia’s USD 200m round as evidence that investor appetite for voice AI is accelerating rapidly.
Florian connects that funding momentum to product launches, including ElevenLab’s Expressive Mode and YouTube’s expanding AI dubbing push.
The duo then reviews YouTube’s AI dubbing in German and Spanish, finding the intelligibility and naturalness impressive, but rhythm and intonation still mirroring the English source language too closely.
Anna turns to new academic research arguing that current text-to-speech evaluation methods under-test real-world deployment factors such as long-form consistency, punctuation handling, and robustness across messy inputs.
Anna reports that Appen delivered double-digit revenue growth and an EBITDA turnaround in Q4 FY25, driven by a higher share of generative AI projects and strong momentum in China.
Florian closes by touching on prompt injection issues in AI translation tools, RWS’s return to growth, and Lionbridge’s ownership transition.
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Florian and Esther discuss the language industry news of the past few weeks, starting with senior hires in revenue and operations at DeepL and what this signals about the LTP’s next phase.
The duo then turns to new data from AI labs and hyperscalers, where Florian highlights findings from Anthropic’s research showing AI is settling into a support role rather than full automation, with usage concentrated around review and validation, and humans remaining firmly in the loop.
On the consumer side, Esther points to Microsoft Copilot data showing translation and language learning as one of the most common everyday AI use cases. Florian flags Adobe’s new “Translate this PDF” feature, where formatting was the main issue rather than translation accuracy.
The conversation then shifts to infrastructure, where Florian emphasizes how NVIDIA is positioning itself at the center of real-time multilingual voice ecosystems by open-sourcing models while driving demand for its hardware.
The duo unpacks OpenAI’s quiet launch of ChatGPT Translate. Esther notes that reactions have been mixed, with many seeing the interface as basic, while Florian stresses the strategic importance of the move. Then the two disagree on whether or not the AI’s default prompt to make the translation sound “more fluent” makes any sense.
Esther walks through recent M&A activity and funding rounds, highlighting acquisitions in Europe and the US alongside major raises by Synthesia, Deepgram, and reportedly ElevenLabs.
Florian concludes with a look at an S-1 filing by a tiny company, using it as an example of how the US capital markets accommodate everything from billion-dollar AI firms to survival-stage experiments.
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JC Penet, Reader in Translation Industry Studies at Newcastle University, and Joss Moorkens, Associate Professor at DCU, join SlatorPod to talk about the new open-access book Teaching translation in the age of generative AI: New paradigm, new learning?
The duo explains how large language models (LLMs) have a different impact than earlier machine translation breakthroughs as they generate human-like text, respond to prompts, and adapt output to context.
Public hype around LLMs has affected demand for some translators and fueled misconceptions around the value of studying translation. Although, JC and Joss stress that translation education must adapt.
JC outlines how students need to assess whether output is appropriate for purpose, audience, risk, and context. This places greater importance on skills such as selection, evaluation, and effective prompting, while still relying on core linguistic and cultural competence.
Joss adds that this shift reflects real industry practice, where different content types already receive different levels of automation and human involvement. Drawing on healthcare research, he highlights how AI can outperform traditional workflows in some contexts but fail badly in others, especially across languages with uneven data coverage.
Joss also highlights ethical blind spots that arise when performance metrics dominate decision-making. He describes a “triple bottom line” approach that weighs people, planet, and performance equally.
On fears of de-skilling, JC argues that excluding AI from classrooms poses a greater risk. Without guided engagement, students may use tools uncritically or fail to develop AI literacy altogether. Joss points to initiatives such as LT-LiDER, an Erasmus+ project designed to build AI literacy among educators.
Looking ahead, the duo contends that studying languages and translation remains valuable because it develops deep reading, critical thinking, intercultural awareness, and adaptability.
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In the 2025 year-end episode of SlatorPod, hosts Florian Faes and Esther Bond reflect on a year defined by rapid AI investment, shifting policy, and structural change across the language industry.
Esther opens the year-in-review by highlighting January’s twin funding milestones in the language AI and product space. Florian follows with February, which saw hyperscalers and AI labs release data highly relevant to the way AI translation is being used.
March, April, and May saw major developments both on the regulatory side and in terms of bolt-on acquisition deals.
Past the mid-year point, OpenAI’s decision to hire a localization manager was what grabbed the industry’s collective attention. The AI lab’s decision contrasted with September’s news, which saw the closure of one of the world’s most recognized academic programs for localization.
The year closed on publicly listed LSIs releasing mixed results and major announcements in AI translation for literature and live speech translation rollouts.
The duo closes with 2026 predictions!
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Josh Miller and Christopher Antunes, Co-Founders and co-CEOs of 3Play Media, join SlatorPod to talk about the company’s trajectory as a leading language solutions integrator (LSI) in multilingual video accessibility.
The duo explains how the two met at MIT, where an early challenge from OpenCourseWare revealed that captioning thousands of technical videos was financially impossible, leading to the company’s founding, where they developed proprietary tooling, leveraged AI, and incorporated expert-in-the-loop solutions.
Josh describes how their platform evolved into a dual system supporting both customers and large-scale operations. Chris notes that the LSI now serves media and entertainment, higher education, e-learning, and corporate clients.
Chris explains that three major trends — the European Accessibility Act (EAA), advancements in voice technology, and the rise of live events — drove their expansion into global localization.
The co-CEOs detail their dubbing journey, noting rapid learning over the last 18 months and the emergence of a big mid-tier market between high-end theatrical dubbing and low-cost AI-only output.
Josh explains how the EAA is pushing companies to prepare for large columns of multilingual captioning and audio description. He notes that interpretations of the law still vary, but major media firms are already investing to avoid disruption.
The duo shares findings from their 2025 State of ASR Report, where they found that accuracy initially improved sharply with generative models but has now plateaued.
Looking to the future, the co-CEOs are working on shifting their model to incorporate AI-generated scores and analytics, allowing customers to decide on the level of expert intervention.
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Florian and Esther discuss the language industry news of the past few weeks, reflecting on SlatorCon Remote and announcing that SlatorCon London 2026 is open for registration.
The duo touch on IMDb’s decision to recognize dubbing artists as part of new professional credit categories, explaining how this expands visibility for multilingual voice talent. They then move on to Coursera’s strategy shift and outline how its new CEO is betting on AI translation and AI dubbing to revive slowing growth.
Florian and Esther talk about Amazon’s rollout of AI-translated Kindle eBooks, and question authors' willingness to rely on automated translation despite Amazon’s promise of fast turnarounds, in as little as 72 hours.
Florian highlights research on spatial audio improving AI live speech translation, and reflects on how clearer speaker differentiation could enhance comprehension. Although he stresses ongoing challenges in live settings, like latency and overlapping speech.
In Esther’s M&A and funding corner, healthcare AI technology startup No Barrier raises USD 2.7m, Cisco acquires EZ Dubs to enhance WebEx’s real-time speech translation capabilities, and audio AI startup AudioShake raises USD 14m.
Florian analyzes OneMeta’s financials and notes its rapid revenue growth despite significant ongoing and limited marketing presence. Esther details the landmark UK NHS framework agreement for language services, including scope and the number of awarded vendors.
Florian concludes with updates on interpreting performances at Teleperformance and AMN Healthcare, noting mixed results.
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Amir Haramaty, Co-Founder and President of aiOla, joins SlatorPod to talk about how spoken, multilingual data can transform enterprise workflows and unlock real ROI.
The Co-Founder introduces himself not as a serial entrepreneur but as a serial problem solver, focused on one core challenge: most enterprise data remains uncaptured, unstructured, and unused.
Amir emphasizes that traditional speech tech fails in real-world conditions, where accents, noise, and hyper-specific jargon dominate. He illustrates how he tackles this challenge by building workflow-specific language models that extract only the data relevant to a process.
Amir says aiOla converts speech not into text but into structured, schema-ready data, allowing organizations to automate workflows, improve compliance, and identify trends long before humans can. He explains that the company focuses on narrow processes rather than general conversation, enabling precision in niche environments.
Amir shares how aiOla routinely cuts multi-hour procedures down to minutes, drives efficiency across frontline roles, and creates previously unavailable datasets that feed enterprise intelligence. He highlights ROI examples from supermarkets, airlines, manufacturing, and automotive industries.
Amir explains that after proving aiOla’s value, he realized the fastest way to scale was through firms already embedded in enterprise digital transformation. He notes that aiOla now partners with UST, Accenture, Salesforce, and Nvidia, creating a distribution engine capable of replicating wins across thousands of clients.
He calls this channel strategy a force multiplier that shortens sales cycles and embeds aiOla inside broader modernization initiatives. Amir adds that these partners not only bring scale but also domain expertise aiOla deliberately chose not to build in-house.
Amir outlines future priorities, including product-led growth, speech-based coding, and speech-prompted AI agents. He predicts that agentic systems will rely heavily on high-quality spoken data, making aiOla’s role even more central.
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Tom Kocmi, Researcher at Cohere, and Alon Lavie, Distinguished Career Professor at Carnegie Mellon University, join Florian and Slator language AI Research Analyst, Maria Stasimioti, on SlatorPod to talk about the state-of-the-art in AI translation and what the latest WMT25 results reveal about progress and remaining challenges.
Tom outlines how the WMT conference has become a crucial annual benchmark for assessing AI translation quality and ensuring systems are tested on fresh, demanding datasets. He notes that systems now face literary text, social-media language, ASR-noisy speech transcripts, and data selected through a difficulty-sampling algorithm. He stresses that these harder inputs expose far more system weaknesses than in previous years.
He adds that human translators also struggle as they face fatigue, time pressure, and constraints such as not being allowed to post-edit. He emphasizes that human parity claims are unreliable and highlights the need for improved human evaluation design.
Alon underscores that harder test data also challenges evaluators. He explains that segment-level scoring is now more difficult, and even human evaluators miss different subsets of errors. He highlights that automated metrics built on earlier-era training data underperformed, particularly COMET, because they absorbed their own biases.
He reports that the strongest performers in the evaluation task were reasoning-capable large language models (LLMs), either lightly prompted or submitted with elaborate evaluation-specific prompting. He notes that while these LLM-as-judge setups outperformed traditional neural metrics overall, their segment-level performance varied.
Tom points out that the translation task also revealed notable progress from smaller academic models around 9B parameters, some ranking near trillion-parameter frontier models. He sees this as a sign that competitive research is still widely accessible.
The duo concludes that they must carefully choose evaluation methods, avoid assessing models with the same metric used during training, and adopt LLM-based judging for more reliable assessments.
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Nikita Agarwal, Founder of Milestone Localization, joins SlatorPod to talk about her journey founding a language solutions integrator (LSI) and launching Cavya.ai, a platform designed to streamline translation project preparation.
Nikita began Milestone Localization in 2020 after discovering the language industry while working in international sales. She was drawn to the field’s global scope and low barrier to entry. She emphasizes that sales experience played a crucial role in landing early clients and understanding the value of hiring people from within the industry.
The founder reflects on the past 16 months as a period of intense change marked by AI disruption, client pressure on pricing, and shifting expectations. She highlights how regulated sectors like life sciences have helped stabilize the company amid volatility. She details how the LSI specializes in medical device translations and regulatory submissions across Europe.
Nikita explains that her new platform, Cavya.ai, emerged from internal needs to improve project preparation. She says the tool automates glossaries, style guides, and document analysis, reducing time and boosting consistency for small and mid-sized projects.
The founder shares her observations on India’s evolving language technology landscape, noting significant progress in AI for major Indian languages. She says increased internet access and AI-driven localization are expanding education and job opportunities across the country.
Nikita concludes that she sees the future in expanding life sciences work, refining Cavya, and developing an AI-powered QA tool. She notes that some clients are showing “AI fatigue” and returning to human-led workflows.
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Thordur Arnason, Global AI GTM Lead at Capgemini Invent, joins SlatorPod to talk about how the consulting giant is embracing language AI through BabelSpeak, its new real-time AI speech translation platform.
Thordur explains that the idea emerged from Capgemini’s AI Futures Lab while researching multimodal AI. Inspired by Meta’s launch of the Seamless M4T model, the team set out to tackle the hard problem of live AI speech translation.
He notes that early pilots with DNB Bank, the Norwegian Red Cross, and the Norwegian Police tested BabelSpeak in critical situations — from refugee banking access to emergency communication.
Thordur highlights Capgemini’s partnerships with Nvidia and Telenor, saying Nvidia provides the AI hardware and models, while Telenor’s sovereign AI infrastructure ensures security, GDPR compliance, and data sovereignty.
He emphasizes that BabelSpeak’s reliability comes not just from AI models but from engineering precision, reducing latency from three seconds to under 300 milliseconds.
Thordur discusses Capgemini’s exploration of agentic AI, where autonomous systems perceive, reason, and act independently. He describes how the company built an “Agentic Workbench” to help non-technical users experiment with AI agents safely and sees BabelSpeak as a potential tool within larger agentic systems.
He concludes that Capgemini is expanding BabelSpeak into a broader suite of language tools, combining secure AI infrastructure with advanced multilingual communication for enterprise and government clients.
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Florian and Esther discuss the language industry news of the week, with congratulations to Villam Language Services on its sale to InAnyLanguage. Slator served as joint exclusive advisor with Maveria Advisory, representing Villam throughout the end-to-end M&A process.
The duo turns to Perplexity’s Localization Manager job posting, which they found almost identical to OpenAI’s earlier post, down to matching structure, order, and phrasing. They question whether copying such a specific ad shows a lack of seriousness or simply reflects practicality and efficiency.
Esther and Florian talk about RWS's new leadership hires: Stephen Lamb as Chief Financial Officer and Michael Wayne as Head of Media and Entertainment. Esther outlines how the appointments strengthen RWS’s investment strategy in media localization, dubbing, and content adaptation.
Esther next mentions that Visual Data has named Maz Al-Jumaili as SVP of Worldwide Localization, to lead subtitling and dubbing operations and strengthen client partnerships.
The duo wrap up with the UK government’s bizarre energy-efficiency study, claiming AI translation is a thousand times greener than human translation. They review the flawed logic, where the report assigns human translators the entire office energy costs while excluding AI infrastructure.
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Tom Bridges, CEO and Founder of CaptionHub, joins SlatorPod to talk about how a small in-house tool evolved into a global AI-powered multimedia localization platform.
Tom began his career in post-production and visual effects before stumbling into subtitling when a client needed to localize a video into 16 languages overnight. He reveals that the disorganized workflows relying on spreadsheets inspired him to create a more efficient, centralized solution, which became CaptionHub.
Tom explains that CaptionHub has since grown from a subtitling tool into a full multimedia localization platform integrating speech recognition, machine translation, and synthetic voice. He adds that the platform’s strength lies in being AI-agnostic and offering end-to-end workflows that balance automation with human-in-the-loop processes.
Tom describes how CaptionHub’s new product suite, Timbra, enables real-time media localization and has already supported major live events. He says live captioning is technically complex but benefits from the company’s years of research into video-on-demand subtitling quality.
Tom notes that accessibility regulations like the European Accessibility Act are driving demand, while AI and language models are opening new frontiers such as lip-sync and sign-language integration.
Tom envisions a future where speech-to-speech translation, synthetic dubbing, and real-time localization merge into seamless, scalable experiences. CaptionHub’s mission remains to make multimedia communication universally accessible and efficient through human and AI collaboration.
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Florian and Esther discuss the language industry news of the week, with breaking news that DeepL is reportedly exploring an initial public offering (IPO) in the US at a potential USD 5bn valuation. This comes as DeepL now positions itself as a “global AI product and research company”. Florian also notes the launch of DeepL Marketplace and the appointment of Gonçalo Gaiolas as Chief Product Officer.
Florian opens with the first-ever Slator Award at ZHAW Zurich University of Applied Sciences, where Guy Ratnitsky won for his thesis on data security and confidentiality in AI. The program will soon be renamed MA in Multilingual Communication Management to reflect market realities.
The duo turns to Anthropic’s new Economic Index, which shows translators and interpreters make up 0.63% of Claude AI usage, while OpenAI data previously showed translation-related conversations at 4.5%.
Florian unpacks comments from German Chancellor Friedrich Merz, who, during a visit to Spain, suggested AI could replace EU interpreters in the medium term. He explains that Spain is pushing for Catalan, Basque, and Galician to become official EU languages, but Merz cited translation workload and complexity.
Florian and Esther then run through live AI speech translation updates: Zoom’s in-house rollout, Apple’s AirPods, Google’s translation features, Microsoft’s API, and Meta’s Ray-Bans.
In Esther’s M&A corner, she reports on Bering Lab’s acquisition of Intersphere in Korea and Iyuno’s partnership with Motion Picture Solutions in the UK for a film localization pipeline. Meanwhile, Testronic secured funding to scale down in some locations while expanding in Manila as a hub for QA testing and localization.
- Visa fler
