SlatorPod

• #272 Spatial Audio, IMDb Honors Dubs, Kindle AI Translations, Startup Rounds

  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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• # 271 How aiOla Turns Natural, Multilingual Speech into Workflow-Ready Data

  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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• #270 AI Translation State of the Art with Tom Kocmi and Alon Lavie

  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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• #269 Milestone Localization Founder on Automated Glossaries, LSI Leadership, AI Fatigue

  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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• #268 Thordur Arnason on Why Capgemini Is Building an AI Speech Translator

  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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