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iGaming case study, Betsson: "We didn't deploy a chatbot. We built a governed AI system embedded in our operational stack." That's their Head of Customer Support. The numbers: 30%+ of tickets solved before they were even created, then +75% of the remaining incoming tickets resolved by AI, end to end. The case study explains the system: who writes the rules, how every reply is traced, and where a person stays in charge. Free download for licensed iGaming operators.

iGaming support and KYC teams: Betsson resolves 80–90% of its KYC issues with AI, 24/7. KYC is where the queue usually stalls: failed identity checks, documents to re-request, players waiting. At Betsson, the AI fixes most of them before the player has to ask. Overall, 30%+ of tickets are solved before they're created, and +75% of the rest are resolved by AI. The case study shows the rules behind it, and who owns them: the support team. Free download.

Theo is an AI workspace for researchers: literature, derivations and code in one place. Sign up free and get 1,500 credits.

Theo is an AI workspace for researchers: literature, derivations and code in one place. Sign up free and get 1,500 credits.

Voice AI gets judged by users in seconds, on things a transcript never shows. Hume helps teams measure and improve voice AI the way people experience it, across voice, face and language, in real time.

“Fine” can be a yes or a goodbye. The transcript can’t tell the difference, and most AI reads only the transcript. Hume measures how it was said: tone, energy, delivery, across voice, face and language, in real time.

An hourly GPU rate is only part of a long-term infrastructure decision. CambridgeNexus is accepting reservations against its secured NVIDIA Vera Rubin allocation, with leasing expected in mid-2027. Review configuration, location and commercial terms together, before committing your infrastructure budget. See how the reservation process works—and which terms to clarify before committing.

For large AI workloads, GPU performance depends on the infrastructure around it: networking, storage, system health and operational support. Planning a Vera Rubin deployment is the time to define how that infrastructure will be tested, accepted and supported. CambridgeNexus has secured Vera Rubin allocation, with leasing expected in mid-2027. Review your network and storage requirements, acceptance criteria and recovery procedures before reserving. See how CambridgeNexus plans to bring your Vera Rubin deployment from reservation to production.

KI-Modelle verändern sich. Datengrundlagen bleiben. Schaffen Sie ein Fundament für GenAI, um mit Oracle Cloud Infrastructure zukunftssicher zu sein.

Erfahren Sie, wie Unternehmen Oracle AI-Lösungen nutzen, um praktische KI-Anwendungsfälle zu implementieren, die Workflows und Entscheidungsfindung verbessern.

KogniLink Agents sind da! Mit dem neuen Modul erweitern wir KogniLink um eine agentische Arbeitsplattform: eine KI, die eigenständig komplexe Aufgaben über hunderttausende interne Dokumente hinweg abarbeitet und jedes Ergebnis mit Quellen belegt. Für Ingenieurbüros, die komplexe Vorhaben projektieren. Für Kanzleien, die in vergangenen Fällen recherchieren. Für produzierende Unternehmen, deren F&E an komplexen Lastenheften arbeitet. Für regulierte Industrien, die Entwicklung und Audits gezielt beschleunigen wollen. Ein Beispiel zeigt der Screenshot: die Biokompatibilitätsbewertung einer (fiktiven) Insulinpumpe nach ISO 10993, mit nachvollziehbarer Begründung und abschnittsgenauen Belegen. Gelb markierte Belege zeigen, wo ein Mensch noch einmal prüfen sollte. Denn die Agenten ersetzen keine Fachleute: Sie übernehmen die Vorbereitung, damit Expertinnen und Experten sich auf Bewertung und Entscheidung konzentrieren können. Das Beste daran: Alles läuft on-premise, ohne Datenabfluss, auf kompakter Hardware. Eigene Systeme anbinden? Klar. Ob ERP, PLM, DMS oder CAQ: Unsere Agenten arbeiten mit den Daten, die heute in Ihrem Unternehmen entstehen. Die ersten Pilotnutzer arbeiten bereits damit und sind begeistert. In den nächsten Wochen erhalten alle Kunden Zugriff auf das neue Modul.

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.

NVIDIA B300s are LIVE at Cyfuture’s Tier III Data Centre in Noida. And these machines get even more interesting when you connect them through InfiniBand or RoCEv2. That’s when individual GPU servers start working together as a massive compute powerhouse, ready to take on demanding AI workloads. More GPUs. More interconnect bandwidth. More compute power.The real magic isn’t just in the GPUs. It’s in how you connect them. 😉 The GPUs bring the muscle. The interconnect brings them together. The real beast is the cluster. 🔥 Cyfuture.ai

Von Grund auf privat und sicher. Mit Cohere KI nutzen und Autonomie bewahren.

Your AI agents shouldn’t feel like a black box; they should feel like a cockpit you know how to fly. On 10 Nov, WPP’s CAIO walks you through a clear six-stage lifecycle for governing agents, so you can set boundaries, define escalation paths, and prove to your stakeholders that your AI is both powerful and well-governed.

You wouldn’t run a business without a process; why run AI agents without one? In this live session with WPP’s CAIO on 10 Nov, you’ll get a practical, six-stage lifecycle for governing agents, covering when they should act, when they must stop, and how they hand off to humans - so your AI strategy is structured, auditable, and ready to scale.

Enterprise data can be available, but still hard to use. 👇 In one recent project, we built an MCP server on Azure that connected ChatGPT with Microsoft Fabric for a US-based spirits producer and distributor. The goal was simple: give business users a 𝐬𝐞𝐜𝐮𝐫𝐞 𝐰𝐚𝐲 𝐭𝐨 𝐚𝐬𝐤 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐚𝐛𝐨𝐮𝐭 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 without having to rely on a technical team every time. Users could query approved datasets, compare information, and get structured answers, summaries, and reports in ChatGPT. This setup can also work for other companies with 𝐥𝐚𝐫𝐠𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬. Examples include: • operational and supply chain analysis • sales and commercial reporting • financial analysis • recurring management reports • internal data search • AI agents that need access to company systems 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐩𝐚𝐫𝐭 𝐢𝐬 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. An LLM should not have open access to business systems. Companies need clear rules for authentication, permissions, available actions, and data access. That is where a custom MCP server can help. It creates a controlled connection between AI tools and enterprise systems. Read more about how we build 𝐌𝐂𝐏 𝐬𝐞𝐫𝐯𝐞𝐫𝐬 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 👉 https://lnkd.in/dxFCnXve/?utm_campaign=%255Btraffic%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206642164&hsa_acc=508184568&hsa_cam=1206642164&hsa_grp=887012544&hsa_ad=1541830714&hsa_net=linkedin&hsa_ver=3

Insurance competitors never reveal their strategy in a single place. 🛑 They reveal it through 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐥𝐚𝐮𝐧𝐜𝐡𝐞𝐬, 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩𝐬, 𝐩𝐢𝐥𝐨𝐭 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐬, 𝐡𝐢𝐫𝐢𝐧𝐠 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬, 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐛𝐞𝐭𝐬, 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 𝐦𝐨𝐯𝐞𝐬, 𝐚𝐧𝐝 𝐫𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐫𝐨𝐥𝐥𝐨𝐮𝐭𝐬. For financial services and insurance companies, tracking those signals manually creates a real business problem, where teams see too much information, too late, with too little structure. That makes it harder to answer questions like: > Which peers are investing in embedded insurance? > Who is piloting GenAI in claims, underwriting, or customer service? > Which partnerships are turning into actual distribution advantage? > Where are competitors moving from experimentation to scaled deployment? 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐞𝐫𝐞 𝐜𝐮𝐬𝐭𝐨𝐦 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐦𝐚𝐤𝐞 𝐬𝐞𝐧𝐬𝐞 and work better than generic tools can summarize content. Competitive intelligence in insurance requires: - source-grounded signals, - industry-specific classification, - consistent peer benchmarking, - configurable dimensions, - auditability, - and reporting workflows that align with how strategy, innovation, and market intelligence teams actually work. For one of the leading insurance providers, we built an 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐭𝐨 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐡𝐨𝐰 𝐩𝐞𝐞𝐫 𝐢𝐧𝐬𝐮𝐫𝐞𝐫𝐬 𝐠𝐥𝐨𝐛𝐚𝐥𝐥𝐲 𝐚𝐝𝐨𝐩𝐭 𝐧𝐞𝐰 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬, launch partnerships, pilot innovation initiatives, and shift strategic priorities. The system combined: 1. Automated public data collection, 2. An AI-powered pipeline for signal extraction, classification, summarization, visualization, and report generation. The result: stakeholders moved to a structured, repeatable intelligence process. Instead of manually reviewing large volumes of public sources, they could 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐦𝐨𝐫𝐞 𝐪𝐮𝐢𝐜𝐤𝐥𝐲, 𝐜𝐨𝐦𝐩𝐚𝐫𝐞 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐨𝐫𝐬 across selected dimensions, and trace insights back to the source data. In insurance, the advantage is not just having more information, but knowing which signals matter, seeing them early, and turning them into decisions before the market has already moved. If your team is exploring where agentic AI and custom LLM or RAG systems can create measurable operational value, this is where we usually start: 👉 https://lnkd.in/esYN8uD7/?utm_campaign=%255Bclick%255DLLM(WIP)&utm_source=linkedin&utm_medium=paid&utm_id=703271224&hsa_acc=508184568&hsa_cam=703271224&hsa_grp=326434034&hsa_ad=1462897284&hsa_net=linkedin&hsa_ver=3

Get Cohere's single agentic platform for full-stack control, enterprise-grade security, and AI intelligence where your data lives.

AI strategy is not the finish line. See how manufacturing teams can start with one practical use case, prove value, and scale what works.

Fast Company just named Aranya as one of The Next Big Things in Tech! Big thanks to the team and everyone who's helped us get here. Link to the full article in the comments ↓

A lot of this week's datacenter news comes back to one date: when the power turns on. But for operators who already have power, there’s a second date that matters just as much: the day the GPUs are online and actually usable. The industry norm from racked servers to a production-ready cluster is weeks, and until then, those GPUs aren't earning. We do it within 48 hours. The only requirement? Hardware is racked. We handle the rest: Bare metal provisioning, GPU orchestration, CPUs, networking, and storage. From there we build the architecture the tenant needs, whether that's Kubernetes, Slurm, VMs, or storage, and hand it over production-ready. Power decides when you can build. The cluster decides when you can earn.

Recognized by Gartner®, we believe Dataiku helps teams move from models to production faster and deliver AI success across real-world workflows. Explore the report to learn more.

Want to learn how to use agents for market research? Watch Fred Gray, Head of VC Research at Crunchbase and Dan Moroz, Chief Economist at Exa as they share how to use agents for deep research.