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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.

Lepsze modele AI nie zwolniły zespołów z potrzeby precyzyjnego formułowania instrukcji. Prompt engineering to dziś kompetencja biznesowa, a nie techniczna. Napisałeś prompt. Model wygenerował wynik. Nietrafiony. Zmieniasz jedno słowo. Wynik lepszy, ale format nie ten. Trzecia iteracja. Czwarta. Piąta. Brzmi znajomo? Większość zespołów nie ma problemu ze słabym modelem – ma problem z brakiem systematycznego podejścia do promptów. A w 2026 roku to już nie jest indywidualna sprawa każdego pracownika. W nowym artykule na blogu Extentum AI pokazujemy: ✅ Anatomię skutecznego prompta w 7 elementach – rola, zadanie, kontekst, ograniczenia, przykłady, format, kryteria sukcesu. ✅ Techniki, które realnie działają w biznesie: zero-shot, few-shot, chain of thought – i kiedy ich nie używać. ✅ Jak zbudować firmową bibliotekę promptów z właścicielem, wersjonowaniem i testami. ✅ Bezpieczeństwo: RODO, prompt injection, shadow AI. ✅ Trendy 2026: od prompt engineering do context engineering. Przeczytaj i sprawdź, na którym etapie jest Twoja organizacja: https://lnkd.in/esWjyEae #AI #ExtentumAI #PromptEngineering #EnterpriseAI #AIwBiznesie

🔐W firmach regulowanych przejście od pilotażu GenAI do wdrożenia produkcyjnego zależy od danych, integracji z procesami, nadzoru oraz możliwości audytu. Raport MIT NANDA wskazuje, że około 95% analizowanych inicjatyw GenAI nie osiągało mierzalnego wpływu na wynik finansowy. Co pomaga budować rozwiązania gotowe do działania na większą skalę? ✅ Architektura dopasowana do wymagań dotyczących danych, np. on-premises lub private cloud, gdy organizacja nie może przetwarzać danych w chmurze publicznej. ✅ Workflowy z kontrolowanymi, audytowalnymi krokami. ✅ RAG oparty na wiedzy organizacji. ✅ Nadzór człowieka w decyzjach o istotnym ryzyku. ✅ Wsparcie inżynierów wdrożeniowych FDE, łączących technologię z realiami procesu biznesowego. W nowym artykule pokazujemy, jak bezpiecznie wdrażać GenAI w Legal, R&D i HR, zachowując kontrolę nad danymi i procesami. 📖 Przeczytaj: https://lnkd.in/eMGidGhA #AI #ExtentumAI #GenAI #Enterprise AI #TransformacjaAI

Co lidem brání používat AI v práci? Z více než 3 000 dotazníků a online rozhovorů jsme zjistili: 🟣 21 % lidí neví, k čemu AI použít. U začátečníků je to dokonce 48 %. 🟣 Zhruba třetinu pokročilých brzdí firemní pravidla, zákazy a licence. Samotné licence nestačí. Lidé potřebují zjistit, k čemu jim AI pomůže v jejich vlastní práci. V e-booku Školení nestačí najdete čtyři kroky, osm zásad z praxe a plán pilotu s jedním týmem. Zdarma, 20 minut čtení.

Ptáte se, co vám AI školení přineslo? Často tohle: lidé AI pár týdnů zkoušeli a vrátili se k tomu, jak pracovali dřív. V e-booku zjistíte, proč se to děje a jak tým naučit používat AI na konkrétních úkolech. Čtyři kroky, osm zásad z praxe a plán pilotu s jedním týmem. Zdarma.

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.

We pointed Crusoe Managed Inference at Wiz's Day-One CTF. With the right browser tooling and a run-code primitive, the agent solved all seven challenges in less than 2 hours for ~$13, showing a strong harness matters more than a frontier model.

Getting GPU allocation is hard. Paying a premium for it makes it worse. io.net secures the compute you need from our own inventory and partner network, at 50% less than big cloud.

Tell us what you need to run. We match it to our inventory and partner network and lock in your allocation. No waitlist, no premium, 50% less than AWS.

A code-first walkthrough of Serverless Fine-Tuning on Crusoe Intelligence Foundry, from uploading a dataset to deploying a LoRA adapter to a production endpoint using OpenAI-compatible API calls.

🎙️Voice Agents That Actually Work: Cartesia × Coval 📍Coval HQ, SoMa · Thu, Oct 8 · 6:30 PM Demos are easy. Production is hard. We're getting real about what it takes to build voice agents customers actually trust. Drinks included, spots are limited. RSVP: https://lnkd.in/gyFD74Rh

Our revenue more than doubled in FY2026. Exascale generated $14.8 million in revenue, up 111.3% from the previous year. Existing customers spent more with us, and our customer base expanded, driving growth in our intelligent compute service. After fiscal year-end, we completed our business combination and began trading on Nasdaq as $XLAB on August 28. Hoansoo Lee, our CEO, on what comes next: “Our focus now is on converting that growth into disciplined, scalable execution as a public company.” Read the full financial results and business update: https://lnkd.in/gMYGF3im

Compliance-Bögen und Lastenhefte automatisch beantworten: aus Ihren internen Dokumenten, mit Quellenverweis. Ein Compliance-Bogen mit 140 Fragen? 15 Minuten statt ein halber Tag. Ihr Team prüft nur noch, statt zu schreiben – und hat den Kopf wieder frei für die Arbeit, für die es eigentlich da ist.

Nie wieder in alten Bögen, Projektordnern und Laufwerken nach der passenden Antwort suchen. Unsere KI beantwortet Kundenanforderungen aus Compliance-Bögen und Lastenheften in Minuten: aus Ihren internen Dokumenten, mit Quellenverweis. Fragebögen und Excel-Matrizen werden direkt ausgefüllt. Ihr Team gibt frei, unklare Stellen werden markiert. In einer 20-minütigen Demo zeigen wir Ihnen, wie das mit Ihren Dokumenten funktioniert.

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