B2B ad examples

About 1.2 million Australians switch super funds each year. That’s a lot of member balances on the move. Our AI retention software helps you identify members most at risk of leaving and gives you the right actions to retain them. Protect your fund and strengthen member loyalty with the power of Elula. Discover more at elula.ai

Elula pinpoints which lost members can be won back and equips your team with the right conversations to bring balances home. Rethink your customer retention strategy. Put the power of our AI software to work for you. Discover more at elula.ai

Enterprise AI adoption has plateaued at 10% with the power users who didn’t need much help in the first place. elvex breaks this pattern.

The only agent platform that transforms every employee into an AI native one.

Despite AI investments set to reach US$368B by 2032, many banks still struggle to turn pilots into real revenue. The barrier isn't technology-it's operational. 🔍 Key insight: Banks generating measurable top-line growth execute AI with intent: 1. Align every AI use case to P&L objectives 2. Embed intelligence into frontline workflows 3. Treat AI as an enterprise-wide transformation, not a tech experiment 🌍 Insights from Southeast Asia, Latin America, and the Middle East highlight five revenue levers already driving impact: cross-selling, financial-inclusion lending, digital wealth, SME acquisition, and AI-augmented compliance. 📈 Bottom line: Banks that operationalise AI at scale are seeing real top-line impact.

The term recursive self-improvement (RSI) is everywhere right now, appearing in roadmaps, startup names and near-term visions of AI systems that improve themselves with little to no human input. But what does it actually take to get there? TechCrunch's Russell Brandom dug into that question in an article titled “RSI is the new AGI — and it’s just as hard to pin down,” and as part of his reporting, he spoke to our CEO Doris Xin about it. The common framing treats RSI as a problem of intelligence. As the frontier models get smarter, they’ll eventually be able to improve themselves. Jack Clark, Anthropic's co-founder recently wrote on his Substack that he expects RSI to arrive by the end of 2028. Others, including Doris, believe it will happen much sooner, as early as next year. She takes this position because she argues models already possess the intelligence for RSI, but lack the required infrastructure, and developing that infrastructure will happen much more quickly than the release of new frontier models. Brandom quotes Doris in his article saying, "I would argue, given infinite compute and infinite time horizon, we are already there. I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering." That framing tracks with what we see in enterprise ML every day. The bottleneck is rarely the model, but rather the context that's fragmented, and the lack of agentic supervision that can help it determine the value of continuing on the current trajectory.

The term recursive self-improvement (RSI) is everywhere right now, appearing in roadmaps, startup names and near-term visions of AI systems that improve themselves with little to no human input. But what does it actually take to get there? TechCrunch's Russell Brandom dug into that question in an article titled “RSI is the new AGI — and it’s just as hard to pin down,” and as part of his reporting, he spoke to our CEO Doris Xin about it. The common framing treats RSI as a problem of intelligence. As the frontier models get smarter, they’ll eventually be able to improve themselves. Jack Clark, Anthropic's co-founder recently wrote on his Substack that he expects RSI to arrive by the end of 2028. Others, including Doris, believe it will happen much sooner, as early as next year. She takes this position because she argues models already possess the intelligence for RSI, but lack the required infrastructure, and developing that infrastructure will happen much more quickly than the release of new frontier models. Brandom quotes Doris in his article saying, "I would argue, given infinite compute and infinite time horizon, we are already there. I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering." That framing tracks with what we see in enterprise ML every day. The bottleneck is rarely the model, but rather the context that's fragmented, and the lack of agentic supervision that can help it determine the value of continuing on the current trajectory. Read the full TechCrunch piece here: https://lnkd.in/ewAMwrdg Where do you think the real bottleneck is on the path to self-improving AI systems? Drop your take in the comments.

DKube's AI classifies and reconciles W-2s, bank statements, and appraisals automatically — so your team doesn't.

DKube cuts document reconciliation costs by up to 80% with AI that classifies, extracts, and validates.

TIME has named our founder and CEO Uljan Sharka, to the 2026 TIME100 AI, its annual list of the world’s most influential people shaping the future of AI. Uljan founded Domyn with a clear conviction: organizations should be able to own and control the intelligence they depend on. Today, that vision is taking shape through Domyn’s work to make sovereign AI a reality, from building AI for the world’s most regulated industries to advancing the frontier models and infrastructure that can help Europe lead in AI. A significant recognition of the ambition Uljan continues to drive forward, and the work taking place across Domyn to make it a reality. Congratulations, Uljan. 👏 👉 Read Uljan’s TIME100 AI profile: https://lnkd.in/ebtC5gnM 👉 Read the news here: https://lnkd.in/er2GrQAU #TIME100AI

Better decisions, not just faster workflows — that's the real value of enterprise AI. 🚀 🎬 Episode 3 of our AI Myths vs Reality series unpacks why efficiency is only the surface of what enterprise AI can actually deliver. Underneath lies everything your organization already knows but can't yet see — and surfacing it is exactly what enterprise AI does best, turning overlooked data into better decisions. Watch the new episode below. 👇

AI isn't a "future trend" anymore. It is already influencing how financial institutions make decisions, serve customers and improve operations. At this year’s #SFF2025, the conversations felt very real. People talked honestly about what is working, what is still difficult and what they are still trying to figure out. It wasn't just a talk. You could see people nodding, comparing experiences and realizing they share many of the same challenges and opportunities. Our recap reflects that atmosphere. The curiosity. The urgency. And the growing sense that AI is moving from small experiments to meaningful results. What is one AI shift you personally noticed this year?

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Co-engineered with NVIDIA. Validated on the Dell AI Factory. Endorsed by SAP. Deployable in the cloud, on-premise, hybrid, or air-gapped. DataRobot is the agent workforce platform purpose-built for manufacturing — running on the infrastructure you've already invested in, integrating with the systems your team already uses, and operating with the governance your audit teams require.

Manufacturing planning cycles can't keep up with the supply chain anymore. Tariffs shift overnight. Suppliers go dark. Equipment fails between maintenance windows. Most AI tools surface the problem in a dashboard and wait for a planner to act on it. Agentic AI closes the loop — sensing, planning, and acting in real time, inside the systems your team already runs.

𝗢𝘂𝗿 𝗮𝗴𝗲𝗻𝗱𝗮 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗱𝗶𝗱𝗮 𝗰𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟲 The fourth annual dida conference is only two weeks ahead, and we think we have our strongest program yet! You have seen the announcements of all the great speakers we have gathered for this iteration of our conference - we have now published a full schedule for the day here: https://dida.do/conference. What you need to know: 𝗪𝗵𝗲𝗻: June 19, 2026 · 09:00–18:00 CEST 𝗪𝗵𝗲𝗿𝗲: frizzforum, Berlin-Kreuzberg If you build with ML or decide where it goes next, this is a day of senior practitioners and researchers in one room - come and join us. We look forward seeing you there! Participation is capped by the venue, so register soon also at https://dida.do/conference

Every call you take starts with 10 minutes of "so what do you guys do?" What if they already knew before they got on the phone?

AI Performance Isn’t About More Compute It’s about smarter data. Discover how metadata is reshaping AI pipelines—delivering massive gains in speed, efficiency, and scalability.

𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫-𝐜𝐥𝐚𝐬𝐬 𝐯𝐢𝐬𝐢𝐨𝐧-𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐦𝐨𝐝𝐞𝐥 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲. 𝐀𝐭 𝐮𝐩 𝐭𝐨 ~150× 𝐥𝐞𝐬𝐬 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐜𝐨𝐦𝐩𝐮𝐭𝐞. 𝐀𝐧𝐝 𝐮𝐩 𝐭𝐨 3.3× 𝐥𝐨𝐰𝐞𝐫 𝐢𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐜𝐨𝐬𝐭. Without bigger clusters. Without elaborate post-training. Just curated, high-quality training data. That's what our latest research, 20/20 Vision-Language Models, delivers. And the implications for any enterprise building multimodal AI are significant. Holding architecture and compute fixed and varying only the training data, our 2B model lands within 1.8 percentage points of Qwen3-VL-2B at ~87× less training compute. Our 4B matches Qwen3.5-2B at 2.4× lower inference cost. For companies building multimodal AI, that translates into: - 𝐁𝐞𝐭𝐭𝐞𝐫 𝐦𝐨𝐝𝐞𝐥𝐬 𝐟𝐫𝐨𝐦 𝐬𝐦𝐚𝐫𝐭𝐞𝐫 𝐝𝐚𝐭𝐚, 𝐧𝐨𝐭 𝐛𝐢𝐠𝐠𝐞𝐫 𝐜𝐥𝐮𝐬𝐭𝐞𝐫𝐬. Frontier-class accuracy without committing to hyperscaler-scale pretraining budgets. - 𝐋𝐨𝐰𝐞𝐫 𝐫𝐢𝐬𝐤 𝐨𝐧 𝐞𝐯𝐞𝐫𝐲 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐫𝐮𝐧. Per-capability seed variance drops by ~67%, making the recipe robust to the seed lottery that plagues large training runs. - 𝐋𝐨𝐰𝐞𝐫 𝐜𝐨𝐬𝐭 𝐭𝐨 𝐬𝐞𝐫𝐯𝐞, 𝐟𝐨𝐫𝐞𝐯𝐞𝐫. Up to 3.3× lower inference FLOPs than comparable models, with Pareto-dominance on response cost at every scale tested. This is capable multimodal AI that can run on edge devices, not just in a data center. 𝐇𝐨𝐰 𝐲𝐨𝐮 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐲𝐨𝐮𝐫 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐭𝐡𝐞 𝐦𝐨𝐚𝐭. Companies with high-quality proprietary data and the discipline to curate and engineer it well don't need to match hyperscaler budgets dollar-for-dollar to achieve amazing performance. The standard approach to competitive multimodal AI has been to throw more compute at it. The new approach is to treat training data as the strategic lever that it is. Read the full write-up: https://lnkd.in/gwn6w-Mp

Ahora está disponible para tu contact center, tu IVR y tus agentes de voz. Sin hardware. Sin contratos permanentes. Sin complicaciones técnicas. Solo una API que se integra en días y que tus clientes notarán desde la primera llamada. Deepdub no es un proveedor de TTS más. Es la plataforma que los líderes tecnológicos globales han elegido para construir el futuro de la conversación con IA🔝🔝 El playground es gratuito. Pega tu propio script, elige una voz y comprueba cómo podría sonar tu empresa. Después de eso, tú decides☝🏽

Deepdub no es un proveedor de TTS más. Es la plataforma que los líderes tecnológicos globales han elegido para construir el futuro de la conversación con IA🔝🔝 Ahora está disponible para tu contact center, tu IVR y tus agentes de voz. Sin hardware. Sin contratos permanentes. Sin complicaciones técnicas. Solo una API que se integra en días y que tus clientes notarán desde la primera llamada. El playground es gratuito. Pega tu propio script, elige una voz y comprueba cómo podría sonar tu empresa. Después de eso, tú decides☝🏽

A lot of companies talk about “doing” synthetic data, but what does it actually take to generate high-quality synthetic pretraining data at the trillion-token scale, necessary for training foundation models? The DatologyAI team answers this question with BeyondWeb, a synthetic data generation system that outperforms all publicly available methods for synthetic pretraining data generation. BeyondWeb establishes a new Pareto frontier for the trade-off between model size and quality in language model pretraining. It delivers superior performance at every scale, with our 3B model surpassing the results of most 8B-scale baselines, using the same token budget. This new frontier reshapes the economics and accessibility of high-performance AI development, enabling organizations to achieve state-of-the-art results with computational budgets that were previously insufficient for competitive model development. Read more about the impact of BeyondWeb—and what it means for the future of the AI industry—here: https://lnkd.in/g_6-3QBQ

Jede Gesellschaft nutzt eine andere KI. Wer in Ihrer Gruppe kennt am Ende alle Datenflüsse? Microsoft 365 ist überall gesetzt, Copilot ist eingeführt. Doch Copilot deckt ein Modell ab und nicht jeden Bedarf der Fachabteilungen. Also greifen Teams zu eigenen Tools, und die IT prüft seit Monaten einen Wunsch nach dem nächsten. So entsteht entweder Schatten-KI ohne Standard oder ein Verbot, das die Mitarbeiter ohnehin umgehen. Das passiert nicht irgendwann. Das passiert täglich. In deutschen Unternehmensgruppen jeder Größe. DeutschlandGPT gibt allen Gesellschaften denselben sicheren Zugang, zentral steuerbar und auf Wunsch im eigenen Branding. Die kostenlose PDF gibt Ihnen die Orientierung dafür: ✔ Wie ein gemeinsamer Standard für die Gruppe aussieht, neben Microsoft 365 ✔ Wie zentrale Rollen, Rechte und Audit Logs funktionieren ✔ Wie sich die Plattform im eigenen Corporate Design ausrollen lässt ✔ Welche technischen Eckdaten die IT für die Bewertung braucht Jetzt kostenlose PDF herunterladen.