B2B ad examples

𝗪𝗵𝗮𝘁 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝗔𝗜 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗲𝗹𝘀𝗲? The new 2026 report reveals the patterns. Download today.

Your GPUs aren’t the problem. Your data pipeline is. As AI moves from experimentation to production, many teams are hitting the same wall: underutilized GPUs, slow training, and rising costs, all caused by data bottlenecks. In this example, Salesforce saw GPU utilization drop as low as 40%—until they re-architected around data. By leveraging Google Cloud Managed Lustre powered by DDN, they achieved: ✔️ 70% increase in GPU utilization ✔️ 1.5× faster training ✔️ 42% lower training costs The takeaway: AI performance starts with data, not compute.

Wissen Sie, wo die Kalkulation liegt, die Ihr Controlling letzte Woche von einer KI hat gegenprüfen lassen? Wahrscheinlich nicht. Denn wenn Mitarbeitende Dokumente in ein privates KI-Tool kopieren, liegen sie Sekunden später auf Servern, über die niemand im Haus Kontrolle hat. Kein Einzelfall: In vier von zehn Unternehmen läuft KI bereits über private Zugänge, die Nutzung hat sich in einem Jahr verdoppelt. Gesperrt ist sie in den meisten Häusern trotzdem. Denn ein Verbot stoppt nicht die Nutzung. Nur die Sichtbarkeit. Und während offiziell nichts passiert, verlassen vertrauliche Daten das Unternehmen – meist Richtung USA. Beim nächsten Audit wird aus dem blinden Fleck ein Problem. Es gibt einen anderen Weg: alle führenden KI-Modelle in einem Tool, gehostet in Deutschland, DSGVO-konform und ohne dass Ihre Daten das Land verlassen. Wie Sie dorthin kommen, haben wir in eine PDF gepackt. 13 Seiten aus über 100 Einführungsprojekten im Mittelstand. Was in der PDF steht: ✅ Eine KI-Richtlinien-Vorlage, die Sie sofort anpassen können ✅ Plattformvergleich: deutsche Server, EU-Cloud, On-Premise ✅ Ein Champions-First-Rollout in vier Wochen ✅ Ein ROI-Rechner für Ihre Unternehmensgröße 👇 Gratis herunterladen

Während Sie noch über KI diskutieren, entwickelt Ihr Wettbewerber schon in einem Drittel der Zeit. Das ist kein Zukunftsszenario. Ein Hersteller aus dem produzierenden Mittelstand hat eine Entwicklungsaufgabe, für die früher drei Leute drei Wochen brauchten, auf eine Woche verkürzt. 85 % schneller. Der Unterschied war nicht ein neues Tool, sondern dass endlich alle es sicher nutzen durften. Denn die Realität in den meisten Häusern kostet doppelt: KI ist gesperrt, wird aber trotzdem genutzt. In 4 von 10 Unternehmen über private Zugänge, mit sensiblen Daten auf fremden Servern. Sie verlieren die Kontrolle beim Audit und gleichzeitig die Zeit, die andere längst einsparen. Dieses Unternehmen hat beides gelöst mit allen führenden KI-Modellen in einem Tool, gehostet in Deutschland. Governance-Lücke geschlossen, Entwicklung beschleunigt. Wie der Weg dorthin aussah, haben wir samt Vorlagen in eine PDF gepackt. 13 Seiten aus über 100 Einführungsprojekten im Mittelstand. Was in der PDF steht: ✅ Eine KI-Richtlinien-Vorlage, die Sie sofort anpassen können ✅ Plattformvergleich: deutsche Server, EU-Cloud, On-Premise ✅ Ein Champions-First-Rollout in vier Wochen ✅ Ein ROI-Rechner für Ihre Unternehmensgröße 👇 Jetzt herunterladen

𝗦𝗽𝗲𝗮𝗸𝗲𝗿 𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁: 𝗝𝗮𝗻𝗻𝗶𝘀 𝗖𝗵𝗲𝗺𝘀𝗲𝗱𝗱𝗶𝗻𝗲 (𝗧𝗨 𝗕𝗲𝗿𝗹𝗶𝗻) Next week the dida conference returns to Berlin. Diffusion reshaped image and video generation. Could it do the same for language? That's the thread Jannis Chemseddine from Technische Universität Berlin picks up at this year's dida conference. In his talk "Advances in Continuous Diffusion Language Models," he digs into the continuous formulations pushing diffusion into territory long held by autoregressive LLMs, and what they unlock along the way. 📅 June 19, 2026 · 09:00–18:00 CET 📍 frizzforum, Friedrichstraße 23, Berlin-Kreuzberg Full lineup and registration 👉 https://dida.do/conference Few spots left. #MachineLearning #DiffusionModels #LanguageModels #GenerativeAI #didaconference

𝗦𝗽𝗲𝗮𝗸𝗲𝗿 𝗔𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁: 𝗜𝗿𝗲𝗻𝗮 𝗕𝗼𝗷𝗮𝗿𝗼𝘃𝘀𝗸𝗮 (𝗭𝗮𝗹𝗮𝗻𝗱𝗼) Few days to go.... and we're excited to welcome Irena Bojarovska, Applied Scientist at Zalando, to this year's dida conference! Her talk "Beyond Accuracy: Agentic Workflows and Foundation Models in Demand Forecasting" looks at what it takes to move forecasting past a single accuracy metric, and how foundation models and agentic workflows are reshaping the way demand is predicted at scale. 📅 June 19, 2026 · 09:00–18:00 CET 📍 frizzforum, Friedrichstraße 23, Berlin-Kreuzberg Full lineup and registration 👉 https://dida.do/conference Spots are limited, so early registration is recommended. #MachineLearning #DemandForecasting #FoundationModels #AgenticAI #didaconference

In nuclear innovation and operations, every decision counts. Our trustworthy and explainable AI platform, The Uncertainty Engine, supported by our team of AI and nuclear engineering specialists, uses pioneering machine learning and uncertainty quantification to enable you to solve complex problems and deliver greater value through more informed and confident decisions, even when the stakes are at their highest. Discover how we can help you accelerate innovation and reduce risk where it matters most.

In nuclear engineering, managing uncertainty is essential to making safe, confident decisions. At digiLab, we use uncertainty quantification and advanced machine learning to help engineers optimise complex systems with greater confidence. Whether you’re responsible for nuclear material research, simulation & reactor design or monitoring & control systems to improve safety, our trusted AI platform, the Uncertainty Engine, supports confident and more informed decision-making even when data is complex, sparse or incomplete. Book a demo call today to explore how we can help you strengthen your nuclear innovation and operations.

Most AI in manufacturing supply chains stops at the forecast — producing a prediction and waiting for a planner to act on it. The cost shows up in stockouts, supplier delays, and emergency rework that traces back to the same root cause: insight that never connects to action. Our ebook walks through how agentic AI closes that gap across the full supply chain lifecycle. Forecasting is the first domino. Agentic AI is the second.

Your forecast was accurate. By the time it reached a planner, conditions had already changed. That gap between prediction and action is where supply chains lose time, money, and ground. This new ebook walks you through how agentic AI closes that gap across the full supply chain lifecycle. Forecasting is the first domino. Agentic AI is the second.

Every AI model is only as good as the data it learns from, and the industry is running low on the high-quality text that made today's models great. Researchers call this the data wall. One obvious response is to have models generate more of their own training data. The trouble is that when a model learns from the potentially dubious outputs of other models, over and over, it drifts away from reality and degrades, a problem known as model collapse. The approach that actually works, and that's now used in nearly every serious model, takes a different path. Rather than inventing new data, the team at DatologyAI pioneered a technique at scale to transform real documents into forms a model can learn from more efficiently. Because the knowledge stays grounded in real material, the model keeps getting better rather than collapsing. It's a lot like taking a handful of good ingredients and making several different and amazing dishes from them. In our latest blog, a new type for us, we have created an explainer for anyone who wants to understand synthetic data without reading a research paper. It covers what synthetic data really is, why the common "it's just fake data" worry misses the point, and where it fits across the stages of model training. Link to the blog in the comments below.

📊 We just blind-benchmarked Phantom X 3.2 against the top real-time TTS models. Thousands of pairwise comparisons in English, scored by linguistic experts. 🔥 The Results 🥇 🥈 ✅Tied for #1 on expressivity (ELO 1545) ✅ #1 on time-to-first-audio at ~125 ms ✅ 2× faster than the next-ranked model on quality We beat ElevenLabs Turbo 2.5, Hume Octave, and Async Flash 1.0 outright. Tied Inworld TTS 1.5-max for first. Years of dubbing for the world's top streamers built our foundation in human-like, expressive speech. Phantom X 3.2 brings that foundation to real-time agents. 🎬 → 🤖 ➡️ Try Phantom X 3.2 in the Playground: https://lnkd.in/eykeWa5b ➡️ Read the full study: https://lnkd.in/eznrfkra #TTS #VoiceAI #ConversationalAI #VoiceAgents #RealTimeTTS #Deepdub #PhantomX

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Alisa Cohn has coached founders at Etsy, Venmo, Foursquare, and DraftKings. Ask her Delphi anything about leadership.

Ever wondered how the best CEO's get better? They get coached by Alisa Cohn.

Most of the data needed to predict failures and eliminate downtime already exists in your environment. Cerebro surfaces it before it hits the line.

Most enterprise AI initiatives never reach production. Not because the data isn't there, but because deployment is broken. Cerebro fixes that.

𝐓𝐨𝐝𝐚𝐲 𝐰𝐞 𝐥𝐚𝐮𝐧𝐜𝐡𝐞𝐝 𝐏𝐡𝐚𝐧𝐭𝐨𝐦 𝐙 𝟑.𝟒 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥.🔥 Built for the moment voice AI stops being a demo and starts talking to real customers. In production, everything has to work at once. The voice has to sound human. Responses have to come back fast enough to feel conversational. And the details, names, numbers, amounts, dates, have to come out right. 150ms to first audio. Full 48 kHz. Multilingual. Built for real-time conversations. Some models see digits. Phantom Z 3.4 understands how they should be spoken. 👇 Below, three models speaking the exact same date. Same text, very different speech. Read more → https://lnkd.in/dgGSDQBJ Talk to our team → https://lnkd.in/dkiz-dJS #VoiceAI #ConversationalAI #ContactCenter #CX #TextToSpeech Ofir Krakowski Nir Krakowski Chen Zarfati Ohad Zohar Michael Moshe Michelashvili

The research teams from Thomson Reuters and DatologyAI got together to walk through how the Thomson-1 model was built. They get into the data, the mid-training, and what it takes to build a frontier model on open weights. If you're building your own model or want to own your intelligence, it's worth the watch. Congrats to Thomson Reuters on the project. Thanks to the presenters: Jonathan Richard Schwarz, Yejin Bang, Shengzhuang Chen, Ari Morcos, Josh Wills, Amro Abbas, and Katherine Mentzer for sharing your insights. Watch the full conversation: https://lnkd.in/gpSVaJpv

Big news for d-Matrix and the future of AI inference. d-Matrix is collaborating with NVIDIA to bring our next-generation Raptor inference XPUs into NVIDIA’s rack-scale AI infrastructure with NVIDIA NVLink scale-up interconnect. As an NVIDIA NVLink Fusion partner, we’re developing Raptor to integrate directly into the NVIDIA MGX rack architecture, creating a scalable foundation for ultra-low latency, premium-level token services. This brings together d-Matrix’s memory-centric inference architecture with NVIDIA’s widely deployed AI factory ecosystem, giving AI labs, hyperscalers, and neoclouds greater flexibility to deploy the right compute for each phase of inference. From AI coding assistants to real-time chatbots and voice agents, the next generation of AI demands faster, more responsive inference at scale. Raptor is being built for that future. Read the full announcement: https://lnkd.in/gsBcAwqC #NVLinkFusion #AIInfrastructure #AgenticAI #dMatrix

Trendspek accelerated their infrastructure inspections 8× using Datature’s easy-to-use AI platform for crack detection. No complex coding or heavy team investment needed - just fast, accurate insights at scale. Even in 3D. Learn how you can simplify your inspection workflows and protect your infrastructure with Datature.

Datature empowered MacroInsight to rapidly train vision AI models for radiology workflows, accelerating smart healthcare innovation. Clinicians now make faster, more informed decisions with secure, and accurate ML-driven insights.

𝐏𝐨𝐰𝐞𝐫 𝐘𝐨𝐮𝐫 𝐀𝐈 𝐖𝐨𝐫𝐤𝐥𝐨𝐚𝐝𝐬 𝐰𝐢𝐭𝐡 𝐁𝐥𝐚𝐳𝐢𝐧𝐠 𝐒𝐩𝐞𝐞𝐝! Looking for unmatched AI performance at unbeatable pricing? Get access to NVIDIA H100 SXM GPUs on Cyfuture.ai – the ultimate choice for training massive models and running high-speed inferencing. ✔Just $2.34/hr ✔Perfect for LLMs, GenAI, and HPC tasks ✔Scalable, reliable, enterprise-ready infrastructure Whether you're a researcher, developer, or enterprise AI team — Cyfuture.ai brings GPU firepower to your fingertips. 👉 Start building with H100 now – no waitlist, no hassle!

An AI factory’s power lies in fast, continuous iteration. The AI-ready data stack drives nonstop feedback loops ,compounding accuracy, accelerating improvement, and scaling enterprise AI systems. Get a demo to learn how it works.