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It's about time Fin - Intercom's agent was given a face. I was demoing Anam during New York Tech Week at an Intercom event. So decided not just to give Fin a face, but have him join a Zoom call as a participant and let him take the questions himself, live. He explained what he does, and why agents need a face. It's pretty cool to watch. Think about every business that runs on live conversation - sales or support calls, onboarding, coaching, tutoring. Now their users are in a video call with an avatar that interacts with them like a colleague. And it's finally really easy to build an interactive avatar who can join Zoom calls, interact with your users and become the face of your product - you don't need to be a developer!

NVIDIA released Nemotron 3.5 Super VL today. As NVIDIA's post-training ecosystem partner, AgileRL got early access, and I'm proud of what it shows for any enterprise deciding whether to rent a frontier model or train its own. We trained it to use a web browser, reading what's on screen and clicking through an online shop, a classifieds site, a forum, and Wikipedia. On 114 tasks it had never seen, it went from: - 30.7% before training - to 90.2% after training - ahead of Claude Opus 5.5 at 63.2% That increase in accuracy came from training on the task itself, and for teams in regulated industries like banking and insurance, that's the part worth paying attention to. Your claims process, your admin tools, and the way your team handles a case aren't in any model's pretraining data, so a prompt can describe them, but training is what actually puts them into the model. Owning the model also changes who's in control. When an agent reads screenshots of your internal systems, a model you train and host yourself means those screenshots never leave your network, and you're no longer relying on a provider to keep the model available and behaving the same way between releases. And an agent like this reads a screenshot on every turn, often for many turns per task, so per-token pricing compounds faster than most teams budget for, whereas a model you host is a fixed cost on hardware you own. Nemotron 3.5 Super VL is on Arena from day 0, so if you have a task you'd want an agent for, send me a message and we'll train and deploy it with you, on your infrastructure, with the weights yours to keep. How we trained it, and the full results, are on our blog:

Today, AgileRL was featured on Jensen Huang's "Building Europe's AI Economy" slide at the NVIDIA GTC keynote! It is wonderful to be featured, and I’m proud of the work our amazing team has done so far. For teams deciding how to train and own their models, this is worth a closer look. The slide set out the companies building Europe's AI economy row by row, from applications and models, through AI factories and compute to energy. We sit in the Applications row, alongside companies such as DeepL, ElevenLabs and Wayve. AgileRL is NVIDIA's post-training partner. Our post-training platform, Arena, lets you train specialised agents for specific use cases that can match or outperform frontier APIs. We support NVIDIA's Nemotron models, and we've published two results with them that show what's possible. The first came in August. We trained Nemotron Lightning 3.5 to solve customer support queries. On 2,728 held-out messages, it went from 71.1% correct out of the box to 99.2%, against Claude Sonnet 5's 90.3%. The second case-study trained Nemotron 3.5 Super VL, which NVIDIA released on 15 October. We trained it on Arena for web-browsing tasks, and it went from 30.7% out of the box to 90.2% on 114 unseen VisualWebArena tasks. For reference, Claude Opus 5.5 scored 63.2% on the same tasks. Both results point the same way. On your own task, an open-weight model trained for it can outperform the closed API models you call today, and the weights stay with you. You also benefit from increased security, data sovereignty, and significantly cheaper inference. If you want to learn more, send me a DM.

We're giving all of our customers an AI engineer: Meet Val, Galtea's new AI agent, who works alongside your team like an extra AI engineer. Val knows your product, understands your evals, and helps you deploy reliable AI in production. What Val can do: → Turn production traces into actionable insights without going through thousands of them manually. → Understand what's wrong in your AI product before your users find out in production. → Generate and run evaluations without starting from scratch. What Val can't do (and where humans are still fundamental): → Decide what "good" means for your product. → Provide domain expertise. → Own accountability when quality drops. → Make high-level architectural decisions. Val is live for all Galtea customers today. Not a customer yet? You can book a demo and meet Val: https://lnkd.in/eV5wftf6

On 2 August, three transparency obligations under the EU AI Act became applicable across the EU market: → If your AI interacts with a person, it has to disclose that it is AI. → If it generates synthetic images, audio, or video, that content has to be labeled. → If it runs emotion recognition or biometric categorization, the people exposed to it have to be informed. For most teams, disclosure was a design decision made once, at launch. Under the AI Act, it is now a behavior you are accountable for, on every release and in front of every user. And you have to verify it the same way you verify anything else your system does. Three questions worth putting to your AI team: → Does our assistant reliably disclose that it is AI across the real range of conversations users actually have, not just the scripted happy path? → Does every piece of synthetic content we generate carry the label, across every format it ships in: image, audio, and video? → When we run emotion recognition or biometric flows, does the required notice actually reach the person in the interaction? And the one that matters most under the new regime: → Can we produce the evidence to prove all of this, on demand, if a regulator or an auditor asks? None of this shows up in a standard accuracy or latency eval. Disclosure has to be tested against realistic scenarios, re-run on every new model version and every prompt change, and captured as evidence, not checked once at launch and assumed stable. Manual spot-checks do not scale to that. And "we designed it to disclose" is not the same as "we can show that it still does." P.S. I'm running a free workshop on building evals for behaviors like this, which an off-the-shelf metric can't measure. If your team is building AI in a regulated industry, join me: https://lnkd.in/et-RWCjm

Symbiosis 5. I still can’t quite believe we’re at number five. Each edition talks a little less about what AI might do, and a little more about what it’s actually doing. Last year, I talked about how voice was finally catching up to the promise of AI. One deployment was handling 2,000–3,000 calls a day, with only 6% transferred to a human. This year, we’re going further. 1. Druid AI Voice. Agents that talk back in real time. Voice in, voice out. 2. Governance. Evaluation built into the platform. An agent doesn’t stop drifting after go-live, so it shouldn’t stop being reviewed either. 3. Experience. A new look for the people talking to our agents, and for the teams building them. It’s about agents you can actually depend on. 20th October. London and online. Hope to see as many of you there: https://hubs.ly/Q04wgpsr0 #Symbiosis5 #AgenticAI #EnterpriseAI #VoiceAI #Governance

Some partnerships start with a contract. This one started with TRUST. I was at Banca Transilvania when Druid AI was a small team working out of an apartment, with a platform that was only half-built. On paper, it wasn't the obvious bet. A large bank. A young startup. A technology most people still called "chatbots". What we saw was a team that listened. That built what we actually needed. That stayed at the table when something didn't work the first time. In 2023, I joined Druid AI. It didn't feel like joining a vendor. It felt like joining a team I had already been building with for years. Today, Chat BT handles 1.8 million AI conversations a year. This number didn't come from a demo, it came from years of iterating together. It's about people willing to build it together, for the long run. On October 20th, Ana Georgia Farcas from Banca Transilvania will be with us at Symbiosis 5. If you're at the start of that journey, come hear how it really happens. In London or online, link in the comments 👇 #Symbiosis5 #AIinBanking #AgenticAI #BancaTransilvania #DruidAI

A year ago, we shared our experiences and learnings in our first 𝐰𝐡𝐢𝐭𝐞𝐩𝐚𝐩𝐞𝐫 𝐨𝐧 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞, given a practical guideline to implement AI Governance. A first set of partners joined us, and shared their experiences with us. A lot can happen in 1 year. And when I say a lot, I mean A LOT. Many of our customers embraced Agentic AI, deploying 100s or 1000s of agents (and hence use cases) to production. AI which you need to govern, you need to monitor, you need to control. But also systems that change daily, and are super hard to control in practice. Governing agentic AI requires automated discovery, automation and enforcement of controls. We all learned quickly, and are proud to share our 𝐭𝐡𝐢𝐫𝐝 𝐞𝐝𝐢𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐚 𝐝𝐞𝐝𝐢𝐜𝐚𝐭𝐞𝐝 𝐜𝐡𝐚𝐩𝐭𝐞𝐫 𝐨𝐧 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐨𝐟 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈. Curious to hear your thoughts! Feel free to reach out to me, always happy to share learnings and discuss challenges! Thanks again to our partner network, happy to do this together, sharing a strong voice on implementing AI responsibly! Want to be part of our network? Don't hesitate, reach out to Maria Saldias Olsen Deloitte, BearingPoint, Conclusion Intelligence , Full Orbit, HU University of Applied Sciences Utrecht (Hogeschool Utrecht), Datashift, Clever Republic, Nemko Digital, Carve Consulting, PA Consulting, DPO Consultancy, Zanders, Digital Power, Considerati, Enjins, DataCatalyst, and 4flow. Among the contributors: Bas Overtoom, Seppe Housen, Henning von Hauen, Koen Balm, Friso Spinhoven, Jeroen Tegelaar, Aura Orval, Floris de Lange, Ton Wagemans, Stefan Leijnen, Anita Boerema, Sharad Patel, Pepijn van der Laan, Jelmer Pieters, Emine Bilsin, Chris Lemmens, Bart Maassen, Dr. Laura Gellert, Scott Rompala, Chris Roters Kolja Verhage Sjoerd Kampen and others! Link: https://lnkd.in/eCxNjSQc

A while back, I wrote about the emergence of the 𝗖𝗵𝗶𝗲𝗳 𝗔𝗜 𝗢𝗳𝗳𝗶𝗰𝗲𝗿 and the open questions that come with being the first to fill a role that doesn't have a playbook yet. We're now seeing the exact same pattern play out one level deeper: the rise of the AI Governance Officer and AI Compliance Officer. With the EU AI Act and other regulatory frameworks entering enforcement and regulations tightening across industries, organizations are scrambling to hire people who can "own" AI governance. And just like with the CAIO, the role sounds obvious in theory, but gets complicated fast in practice. Here's what I keep hearing from people stepping into these roles: 𝗬𝗼𝘂 𝗶𝗻𝗵𝗲𝗿𝗶𝘁 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆. You're accountable for AI risk, but the models are built by engineering teams, the data is owned by another department, and procurement just signed an AI vendor without looping you in. Sound familiar? 𝗡𝗼𝗯𝗼𝗱𝘆 𝘀𝗽𝗲𝗮𝗸𝘀 𝘆𝗼𝘂𝗿 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲. I recently wrote about this exact challenge. Governance and compliance professionals talk about proportionality, accountability, and legitimate interest. Engineers talk about model drift, feature stores, and inference pipelines. Both are describing the same system but the translation layer between them is often missing entirely. 𝗧𝗵𝗲 𝘀𝗰𝗼𝗽𝗲 𝗶𝘀 𝘂𝗻𝗱𝗲𝗳𝗶𝗻𝗲𝗱. Does AI governance cover only high-risk systems? What about that internal chatbot someone spun up over the weekend? Do you own the policy, the process, or the tooling? Where does your mandate end and the DPO's or CTO's begin? 𝗧𝗵𝗲𝗿𝗲'𝘀 𝗻𝗼 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸. How do you measure success in AI governance? Fewer incidents? Faster compliance cycles? Board confidence? The KPIs don't exist yet, which makes it hard to prove value, secure budget, and build a team. These are the same structural questions I raised about the CAIO: differentiation, scope, performance, and organizational dynamics, just applied to a role that sits even more at the intersection of legal, technical, and operational domains. If you're an AI Governance Officer, AI Compliance Officer, or the person who just got "AI risk" added to your job description, I'd love to hear from you. What's working? What's broken? What do you wish your organization understood? P.S. If you're stepping into an AI Governance or Compliance role, Deeploy gives you the infrastructure to back up your mandate: model-level controls, audit trails, and governance evidence your whole organization can work with. Learn more at https://lnkd.in/e_an5nCg

Today, H Company is releasing Holo4, our new family of generalist computer-use models. Holo4 can click and type through graphical interfaces, write and execute code, call MCP and API tools, and combine these modes within the same workflow. It runs across desktop, web, Android, code sandboxes, and business APIs. The model stays the same, and so does the way you call it. Holo4 27B is already setting the pace across desktop, mobile, API, and coding tasks, leading on the main benchmarks. On long-horizon desktop workflows, it reaches 61.7% on OSWorld 2.0, just ahead of GPT-6 Sol at 60.5%, at roughly half the estimated cost. Our bet: agents start doing real work when they can move between screens, code, and tools like people do, without being told when to switch. Holo4 is available today on the H Models API, with weights on Hugging Face. Links in the comments 👇

You can now use your ChatGPT subscription to hire AI staff on Hyperagent. 🚀 Was excited to attend DevDay and watch OpenAI launch Sign in with ChatGPT yesterday - with Hyperagent's logo popping up on the big screen among the first products where it's live. Our customers use Hyperagent to hire agents into real jobs all across their businesses: → A pavement company's sales agents are running outbound at 3X the response rate, adding $2M in revenue. → A glass installer's quoting agents are bidding projects 6X faster. → An AI startup's marketing agents are getting 5X the industry-average CTR on perf mktg ads. So now, with your existing ChatGPT subscription, there's no barrier to your own business's first AI employee. Sign in and start hiring. Link below 👇 https://lnkd.in/gCvTYfsZ

How will AI shape value creation strategies? Backwell will host a private presentation in Madrid and bring together a selected group of investment and industry professionals to discuss how AI can be applied to established businesses to improve operational performance, productivity, and long-term value creation. The event will include a presentation followed by informal networking and refreshments. If you want to join, please register to request a slot via the link: https://lnkd.in/dXXsChDP

How will AI shape value creation strategies? Backwell will host a private presentation in Berlin and bring together a selected group of investment and industry professionals to discuss how AI can be applied to established businesses to improve operational performance, productivity, and long-term value creation. The event will include a presentation followed by networking and refreshments. If you want to join, please register to request a slot via the link: https://lnkd.in/dTk_6j5K

KI für Normen und Regulatory Compliance: Was geht heute wirklich? Eine neue Produktvariante geht in die Entwicklung, das Kundenlastenheft liegt auf dem Tisch. Welche Normen gelten dafür, und welche Abschnitte davon betreffen uns wirklich? Die Antwort steckt in hunderten Seiten Regelwerk, und sie herauszuarbeiten ist Fleißarbeit, bei der kleine Fehler große Wirkung haben. Genau für solche Aufgaben ist KI gemacht. Aber mit einem Sprachmodell und einem Chatfenster ist es nicht getan. Unser neuer Artikel beleuchtet das Thema aus unserer praktischen Erfahrung mit Med-Tech, Maschinenbau, Chemie und Automotive: ➡️ Sechs Anwendungen, die heute in regulierten Unternehmen produktiv laufen ➡️ Drei Voraussetzungen, ohne die KI im Regulatory-Umfeld nur ein interaktives Lexikon bleibt ➡️ Wo heute die Grenzen sind und was Regulatory-Teams von der KI erwarten können ➡️ Was für die Validierung von KI im regulatorischen Umfeld gilt ➡️ Wie der Einstieg gelingt und was sich dabei messen lässt Für QM- und Regulatory-Teams, die KI nicht ausprobieren, sondern einsetzen wollen. Den Link zum Artikel gibt’s im ersten Kommentar.

„KI-gestützte Suche“ und „KI-Wissensmanagement“ werden für Industrieunternehmen oft viel zu kurz gedacht. „Mal eben das Datenblatt finden“ ist ein netter Anfang. Aber es ist nicht das, was eine KI leisten kann, wenn sie an die Daten eines Unternehmens angeschlossen ist. Ein praktisches Beispiel aus unserem KI-Projekt mit einem mittelständischen Maschinenbauer im regulierten Umfeld: Im Lastenheft steht eine Anforderung, ihr Ursprung liegt in der FMEA. Ob sie erfüllt ist, belegt ein Prüfbericht im CAQ. Wie sie umgesetzt werden soll, beschreibt ein Konzept im DMS. Vier Dokumente, drei Systeme, eine Anforderung. Alles ist miteinander verknüpft. - Ändert sich die Anforderung, gilt der Prüfbericht möglicherweise nicht mehr. Dann muss der Versuch wiederholt werden. - Wird das Risiko in der FMEA neu bewertet, ist die Anforderung vielleicht obsolet. Oder zu schwach. Im regulierten Umfeld hat das einen Namen: Nachverfolgbarkeit. Wer etwas ändert, muss eine Impact-Analyse machen und klären, was sonst noch betroffen ist. In der Praxis heißt das oft: Excel-Matrizen, manuell gepflegte Links im RMS und vor allem: viel Erfahrungswissen in den Köpfen. Sind die Abhängigkeiten unklar, entsteht schnell Chaos. Im Worst Case bedeutet das eine gescheiterte Abnahme. Eine bessere Suche („Chat with your company docs“ 🥴) ändert daran nichts. Weil sie prozessual nicht greift. Was hilft, ist eine KI, die weiß, was ein Lastenheft, eine FMEA und ein Prüfbericht ist, die Verbindungen über Systemgrenzen hinweg selbst herstellt und aktuell hält und proaktiv meldet: „Achtung, hier musst du dir etwas anschauen!“ RAG, Embeddings und Vektorsuche sind dafür die Grundlage. Aber es sind generische Basistechnologien. Mehrwert entsteht da, wo KI wirklich auf die Fachwelt zugeschnitten ist. Wir tun viel zu oft so, als wäre RAG bereits ein Meilenstein für Unternehmen. Wie immer entstehen gute Lösungen aber erst dann, wenn Technologie tief mit realem Fachwissen verknüpft wird. Genau das machen wir mit KogniLink: ein intelligenter Layer für regulierte Industrien & Fertigung, der Datenwelt und Sprachmodell verbindet und Dokumente vernetzt, überwacht und prüft. Bei voller Souveränität im eigenen Rechenzentrum. Fazit: KI muss viel stärker mit der fachlichen Realität verzahnt werden, als das heute oft gelebt wird. Agreed?

Four days ago, we put Apodex 1.1 Mini on OpenRouter. Today it’s #4 on OpenRouter’s New & Trending list 🎊, with 79.6B tokens processed. That puts it next to new releases from Anthropic and OpenAI. Token volume is a simple metric, but an honest one. It shows developers choosing a model for real workloads. A quick recap of Apodex 1.1 Mini: 🔷 A reasoning-first model built for research, forecasting, and agentic workflows 🔷 262K context window 🔷 Available now on OpenRouter Thank you to everyone who’s been putting it to work. More from the Apodex model family soon. If you’re building research or reasoning workflows, give it a run and tell us what you find 👇 https://lnkd.in/g3bwJRSc #Apodex #OpenRouter #LLM #AIAgents #AIforDiscovery

We celebrate Alpic 1 year anniversary with a customer week. Day 1 - NOVASOL Travel booking is about to move to 100% agentic and offering an MCP is now a must. Find your next vacation rental house with NOVASOL (part of Awaze). Congrats Sebastian Florian and the whole Awaze team.

Well that escalated quickly 🤣🤣 ! Muse going absolutely rogue 1 min after I installed it on a new phone. AGI is here, and apparently it has its own agenda 🤣 Muse is an incredible product. Slightly unhinged. Meta, please don’t fix it too quickly.

Last Friday my daughter had music class six blocks from our apartment. A room full of babies singing the animal alphabet with a puppet named Rocco. I work from home. I didn't go. And here's the embarrassing part. It wasn't really the calendar. Yeah, I had a call about our new office, candidate outreach for the media company we're building, a YouTube cut my editor was waiting on, a partnership decision my co-founder needed my take on. But honestly? Even if I'd walked over, I know what happens 20 minutes in. I'm on my phone. Checking Slack, and nobody even needs me. I'm just busying myself. Because when there's always more to do, every hour with your kid is really a half hour. Every morning I write my to-do list in the Notes app. A few weeks ago I stared at it and realized every single thing on it is a job I'd hand to a great teammate. So I hired the teammates. None of them are people. They're agents I built on Hyperagent Francois runs my recruiting outreach. Named him Francois because I wanted Michelin-star-host energy, white glove everything. Tommy leaves first-pass notes on our YouTube cuts. Albert preps the partnership memo. And Structured Chuck, long story, posts a brief to our Slack at 8am whether I'm at my desk or not. Watch Francois for a second. I onboarded him the exact way we onboarded our human interns: here's the role, full-stack creator, 10 to 50k YouTube subs, strong on camera. He came back with 50 candidates, emails found, and three outreach drafts in my voice because he studied my sent folder. And he won't send a single one until I say yes. So this Friday, I went. Phone stayed on the counter. 45 minutes on the floor with my daughter and a puppet band manager. And when I got home: Chuck's brief was in Slack, my editor already had Tommy's notes, and Francois had three drafts waiting for a yes. Nothing on fire. Nothing waiting on me. Last Friday I couldn't find one hour, six blocks from my own apartment. This Friday, the only thing waiting on me was a yes that could wait. My agents run on Hyperagent by Airtable Sign up for a paid plan and get $500 in bonus credits. Link below: https://lnkd.in/g2e55__Z Go hire your Francois. #HyperagentPartner #Sponsored

Let’s be real—most CX solutions are just meh. They get the job done, but are they actually driving new revenue? The thing is, customers don’t want “good enough.” They want frictionless, frustration-free, actually-helps-them CX, and that’s where the magic happens. At Interactions, we’ve seen firsthand how enterprises unlock new ARR streams and deepen customer relationships by combining AI-driven automation with real human intelligence. Just ask brands like SRP, Vistra Energy, GoDaddy, and a major Telco company. If your customers are looking for differentiated, revenue-driving CX solutions, let’s connect. We can create something that benefits both your business and your customers.

Breaking news: Customer service still kinda…sucks. I hit the streets to ask people if they think customer service is bad. The verdict? A resounding “YES.” Every. Single. Time. The problem isn’t that customers can’t get answers. It’s that getting them feels like pulling teeth. Clunky tech, endless phone menus, and AI that doesn’t really understand? Yeah, people are over it. That’s why we do things differently. At Interactions, we blend Conversational AI with real human intelligence to create seamless, frustration-free experiences that actually help people. And for businesses? That means happier customers, stronger relationships, and new revenue opportunities. Bad CX is costing companies more than they realize. If you’re in the business of fixing broken CX (and unlocking new revenue streams while you’re at it), let’s talk. Because bad customer service isn’t just annoying, it’s costing you.

I'm speaking at Holo AI Night - SF Edition on July 13 in San Francisco. It turns out the best way to understand agent failures is to talk about them. I'll do my best to cover: → Building computer-use agents at H Company → The gap between test-time performance and real-world deployment → How we're approaching evaluation I'm stoked to go on stage alongside Akshatha Kamath from NVIDIA, who works on multimodality, and Saksham Consul from Black Forest Labs, who focuses on large scale training! This is H Company's first technical meetup, and what better place than SF? No marketing, just builders talking about real AI work. Space is limited. If you're in SF that week building agent systems, see you there.

Some of the best convos I have are with creators who message me here about the Creator Partnership Program. I've now typed out the same answers enough times to know they belong in one place instead. I run the CPP at Higgsfield. It's how we back creators building serious work with AI. Filmmakers, animators, designers, editors, tutorial makers, social creators, you name it. Some are shipping a short film every week, some are releasing their first one, some are deep in projects big enough that they should probably be a studio by now. The idea behind it is that the tools shouldn't be the thing limiting what you make. So partners get a full Higgsfield plan renewed every month, plus extra credits when a project calls for it. Early access to new models before they go public. A direct line to someone on our team instead of a support queue. A private community of partners across 35+ countries sharing what's actually working. And your work featured across our channels. The main ask is simple enough: tag or mention Higgsfield when the tools are part of your work, engage with our launch posts so you're first on new drops, and say hi in the community when you arrive. You keep full ownership of everything you make and full creative freedom over how you make it. And when I say we, I mean it. Shoutout to Karlygash Tulenova, Mirian Palavandishvili, Ali Yermek and Dana Abdikhaliyeva, who run this with me day to day. On applications, our CPP team reads every one. So I can promise nobody is being filtered by an algorithm. And everyone gets an answer by email either way. The thing that carries the most weight is the work itself. Reach matters, but a strong body of work with a smaller audience gets read carefully. So link your best piece, not just your numbers. Apply here: https://lnkd.in/dBzPDrPG Questions welcome in the comments. If you're wondering something, someone else probably is too, they're just too polite to ask.

We groeien. Drie mensen kunnen meebouwen aan wat GLBNXT de komende jaren wordt. GLBNXT maakt sovereign AI mogelijk voor gereguleerde organisaties in Europa. Geen Amerikaanse cloudprovider. Geen data buiten eigen controle. Alles op EU-compute, ISO 27001:2022, gebouwd voor de AVG, NIS2 en de EU AI Act. We zijn op zoek naar: SENIOR AI/DEVOPS ENGINEER Kubernetes, LLM inference, CI/CD regie. Jij neemt de productie-infrastructuur voor je rekening naast onze lead developer. Breda, hybride. FOUNDING COMMERCIAL LEAD Eerste commerciële aanstelling. Salesfunctie van nul opbouwen. Enterprise-deals in de Benelux, werken vanuit een product dat voor zichzelf spreekt. FORWARD DEPLOYED ENGINEER Van kickoff tot productie naast de klant. Technisch genoeg om te coderen, klantgericht genoeg om een directeur bij te praten. Dit zijn geen standaard vacatures. Meer weten of iemand kennen die hier iets mee kan? Solliciteer via glbnxt.com/jobs of stuur mij een DM. #SovereignAI #AIInfrastructuur #Hiring #GLBNXT #EuropeanAI