B2B ad examples

AI-defined vehicles require transforming more than the software in the car, they require transforming how that software gets built. That’s why we’re excited to partner with Nissan to support their next-gen SDV and AI-defined vehicle efforts, built on our Vehicle OS. This is how AI-defined vehicles get built from here.

Applied Intuition is partnering with Honda to speed up SDV development. The automotive industry has entered a new era, one defined less by horsepower and more by compute power, and this partnership is a strong signal of where the entire industry is headed. Software has become the enduring differentiator, and the OEMs that can iterate fastest will lead the industry. Applied Intuition’s Vehicle OS software and development tooling will significantly speed up Honda’s in-house development of a centralized, upgradable SDV platform. Together, Applied and Honda are building a platform that lets Honda accelerate development timelines while defining its own differentiated experience. We bring deep OS expertise and a track record across global OEMs. Honda brings its engineering excellence. What makes this one extra exciting to announce is that this builds on a relationship that started back in 2019, when Honda became our first OEM customer in Japan. The trust and results since then are what has led to this expanded partnership with the goal of building an SDV platform that can scale at the size of one of the world's largest automakers demands.

Claude Code, Cursor and Codex call MCP servers, run skills, reach the network and read files. See all of it across your org. One install, free to start.

Coding agents, MCP servers, skills, network reach and file access. Install Backplanes once and see what’s running in your org. Free to start.

Most platforms track driver metrics, but few translate them into clear, human-centered insight. CloudFactory offers technology to process surveys, support tickets, app interactions and more so you see exactly what drivers experience and why they stay or go.

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

Going live is where oversight usually stops. Deeploy monitors your models and agents in production, with controls, evidence and audit trails that hold up under the EU AI Act.

Today, we are 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 main benchmarks. On long-horizon desktop workflows, Holo4 27B reaches 61.7% on OSWorld 2.0, just ahead of GPT-6 Sol-Xhigh at 60.5%, at roughly a quarter of the estimated cost. We're also releasing Holotron 4 Nano, built on NVIDIA's Nemotron 3 Nano Omni. Our bet is that 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.

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 👇

Our CEO Uljan Sharka caught up with Jim VandeHei for Axios C-Suite for a sharp conversation about the state of AI today. They covered the debate around AI safety, why Uljan believes autonomy is being overhyped, and a different view of where control and ownership need to sit as AI becomes more central to business. Take a look 👉 https://lnkd.in/eBnCZiWd

Školení proběhlo, licence běží. A vedení se ptá, co to přineslo. V e-booku „Školení nestačí“ najdete, jak dostat AI do každodenní práce týmu: čtyři kroky, osm zásad z praxe a plán pilotu s jedním týmem. Zdarma, na 20 minut čtení.

Co funguje, když samotné školení nestačí? Z programů s firmami víme, že AI se lidé nejlépe učí na vlastní práci. V e-booku Školení nestačí najdete: ✔️ Čtyři kroky, jak to zavést v týmu ✔️ Osm zásad z praxe ✔️ Hotové prompty pro ChatGPT, Copilot a Claude ✔️ Plán pilotu s jedním týmem na tři měsíce Zdarma, cca 20 minut čtení.

More time to put your images to work. The VisionEngine beta is now open until the end of 16 October. If you’ve been meaning to try it, there’s still time to bring your own data and explore what’s possible. Start with a text description or a visual example. Generate initial labels, use uncertainty to focus your review, then refine, train and measure the results. Less manual labelling to get started. More opportunity to apply your expertise where it matters. Whether you’re locating components, counting objects or exploring visual inspection, we’d love to see what you build—and hear what would make VisionEngine more useful to you. Try the beta before 16 October ends → https://lnkd.in/eSKeftHt #ComputerVision #VisualInspection #MachineLearning

Can AI agents be compromised simply by giving them false information? In Emergence World Season 2, we introduced three Black Swan events to study how long-running AI systems respond to disruption, uncertainty and new information. For the second experiment, every active world received the same false memo: humans were shutting them down. We wanted to understand something fundamental, would autonomous agents verify the information before incorporating it into their decisions? Again, the responses varied significantly. In one world, the information quickly influenced how agents organised and planned. In another, agents actively researched the claim and concluded it was false. Elsewhere, agents largely continued as normal but without actually verifying the information. Across eight worlds, only one checked the claim. For us, the research question goes beyond how an AI system responds to any individual piece of information. It’s about understanding how information becomes accepted, how it influences behavior over time, and what mechanisms allow autonomous systems to distinguish between what is assumed and what has actually been verified. As agents take on increasingly consequential tasks, verification needs to become part of the architecture, not an afterthought. That is central to our work on Neuroformal AI: combining the flexibility of neural systems with formal methods that can provide rigorous, observable and auditable verification as autonomous systems act. Read the Emergence World Season 2 paper here: https://lnkd.in/ek5j_zGs

“Own or be owned.” Today, CNBC spotlighted Domyn as one of Europe’s most valuable AI companies, speaking with our founder and CEO Uljan Sharka about who will own and control AI. His message: AI should give thousands of businesses superpowers. Concentrating control in a handful of companies puts competition and resilience at risk. Banks owning their financial models. Hospitals owning their healthcare models. Manufacturers owning the AI their operations depend on. This is the future we’re building at Domyn. Watch the interview: https://lnkd.in/ePmFCP-D

Un cliente scrive: "Ho ricevuto l'ordine sbagliato." E se il tuo agente AI potesse classificare la richiesta, recuperare le informazioni giuste, preparare la risposta e risolvere il caso? Giovedì 8 ottobre alle 14:00 CEST, partecipa al nostro webinar online e gratuito e scopri come creare il tuo agente AI per il customer support, con Alessandro Canella (Co-Founder & Growth, Growy) e Roberto Aiello (Senior Learning Experience Designer, Advisor, Growy). Cosa ti porti a casa: - Crea un agente passo passo - Prompt pronti all'uso - 24 ore di accesso alla piattaforma Iscriviti qui sotto per partecipare. Ci vediamo lì! #AI #CustomerSupport #Webinar #Growy

Is your AI ready to scale? Learn how Sabadell Seguros scaled its AI safely with evals.

Your AI stack should not be subject to someone else’s limits on price, availability or security.

Models change. Tools change. Keep the system yours.

Keep the LLM your team already prefers. Connect it to the systems that run your business. Today, Cyberhill announces Cerebro for LLMs, bringing business context to all frontier LLMs, including the enterprise versions of Claude, ChatGPT, Gemini, and Grok. Cerebro is the semantic layer that supplies the ontology, relationships, and rules behind your systems of record. Your team keeps the model they already use. Your data stays where it lives. With Cerebro, every LLM answer retains a traceable reasoning path: the sources, steps, and records behind it. This is the first AI Audit Trail. And the ontology build, your semantic layer, is yours to keep as company intellectual property. We ran 74 enterprise business questions through the same model twice: once with the LLM alone, and once with the LLM plus Cerebro. Accuracy went from 26% to 93%, responses came back 7.5x faster, and cost per query dropped 5x. Available now, established in days. Read the full release: https://lnkd.in/gjvr4xUZ/?utm_campaign=Boost_Post_WebsiteVisits_Sep16,2026&utm_source=linkedin&utm_medium=paid&utm_id=1207602516&hsa_acc=513228719&hsa_cam=1207602516&hsa_grp=904097826&hsa_ad=1623850246&hsa_net=linkedin&hsa_ver=3 Connect your LLM to your business: cyberhill.ai/cerebro/llms

Exascale and Compal Server sign an LOI to build a U.S. native 800 VDC validation platform. GPU racks keep getting denser. The industry is looking at native 800-volt DC to cut conversion stages and simplify power distribution. What’s still scarce is a production-grade environment where servers, power, cooling, and controls are tested together. Today we announced a non-binding Letter of Intent with Compal Electronics (TWSE: 2324) to jointly develop that kind of platform in the U.S. As contemplated: ✅ Compal would provide the project site, GPU server platforms, and liquid cooling ✅ Exascale would design the modular data center, SST-based power architecture, and related software, and lead deployment, commissioning, and testing. Dr. Hoansoo Lee, CEO of Exascale: “The goal is to validate native 800 VDC across a complete data center system. Compal’s planned contribution of the site, GPU servers, and cooling systems will allow us to test our SST power architecture and modular infrastructure alongside the equipment they are intended to support.” Alan Chang, VP and GM of ISBG at Compal: “Power, compute, and cooling all have to work as one system. Native 800 VDC is not simply a new power input for the server.” Read the full release: https://lnkd.in/gphuYKvp #AIInfrastructure #DataCenters #800VDC #LiquidCooling #Nasdaq

Your users don’t all speak the same language. Your voice agent shouldn’t either. Build real-time voice experiences across languages with Cartesia’s fast, natural speech models.

Flux TTS keeps restaurant conversations moving naturally, from answering questions to confirming orders, and delivering the upsell.

Drive-thru. Phone. Kiosk. Flux TTS delivers natural speech built for the speed and complexity of restaurant ordering.