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Planning your 2027 AI budget? You can now include open models. Baseten runs them in Codex and the Responses API, paid from your OpenAI commitment.

Get more out of your OpenAI commitment. Access open models from Baseten, from the same commitment, without leaving Codex.

Stuck on model selection? Evaluate performance with practitioner-built benchmarks. The Specialized Intelligence Index helps teams decide where to use open models, where frontier models earn their premium, and in what domains specialized models raise the bar. Explore the index.

Run Opik test suites in CI so every prompt, model or tool change is checked against the same cases before release.

Find the step that failed, turn it into a test case, and run the whole suite on every change before it ships.

+75% of Betsson's incoming iGaming support tickets, resolved end to end by AI. That is after 30%+ of tickets were already solved proactively, before they were even created. On top: 80–90% of KYC issues resolved by AI, and a 3.3 min average resolution. The case study shows what it took in a regulated operation, and who runs it: the support team. Free download for teams at licensed iGaming operators.

New iGaming case study: Betsson now resolves +75% of its incoming support tickets with AI, end to end. Over 30 days, 30%+ of tickets were solved proactively, before they were even created. Of the remaining tickets, +75% were resolved by AI. 80–90% of KYC issues too, 24/7. The case study shows how: one platform with central governance, rules written and owned by the support team, every reply traced to the rule behind it. Free download for teams at licensed iGaming operators.

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.

100+ applications already. This Saturday, we're bringing together some very interesting folks: → An Adobe researcher who worked on egocentric hand tracking at CMU → A former CTO who built robots to help astronauts in space → A builder making mocap gloves that can sense touch → A PhD researcher at IISc's HiRO Lab An evening led by Dr. Sandeep, discussing all things egocentric hand tracking and where gaps are. Building in this space? Last few spots. We close applications in 24 hours. Sandeep Reddy Sabbella, PhD Ishank Gupta Manish Agarwal

A smile score isn’t the story. Context is. Hume measures expression across face, voice and language in real time, so your product responds to how people actually come across, not just what they type or say.

“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.

Introducing Exa 3.0, a step change in search for agents. Exa 3.0 led on every benchmark we tested, from public research evals to coding and GTM intelligence. The gains come from upgrades across our retrieval stack: our largest embedding model, Dynamic Highlights that use 95% fewer tokens than full pages, and billions of new documents covering products, local businesses, flights, sports, and weather. We are also launching Exa Lite at $4 per 1,000 requests for cost-sensitive apps. For higher quality, Exa Auto spends more compute on every query. Together, they set the new Pareto frontier for search. For coding agents, financial research, and AI assistants answering everyday questions, Exa 3.0 is state of the art.

Sovereign AI fails the moment a developer decides the public API is faster than your private GPU cluster. Owning the hardware only protects your data if teams use it. In this post we cover how Klique runs on SUSE AI Factory with NVIDIA so the governed path is also the fastest one. SUSE and NVIDIA provide the Rancher-based infrastructure, models and serving. Klique adds self-service access to approved models and GPUs, quotas that decide which team gets accelerators, an LLM Gateway that logs tokens by user and model, and chargeback so finance can see what each project spends. Learn more about the solution in our latest blog post. At NVIDIA GTC Berlin? If you missed Damian Erangey's demo in the SUSE booth, drop by our booth #7054 and ask for an encore! We still have a few slots left on on our meeting schedule. Blog post and sign-up links are the comments!

Planning a major AI deployment means aligning GPU capacity, location and delivery dates well before workloads go live. CambridgeNexus has secured NVIDIA Vera Rubin allocation, with leasing expected in mid-2027. Reservations are open now for whole-rack, single-tenant deployments, with CambridgeNexus operating the infrastructure for you. Explore NVIDIA Vera Rubin reservation options for your 2027 capacity plans.

Many AI teams are looking for greater predictability when planning capacity for training, inference and production workloads. CambridgeNexus has secured NVIDIA Vera Rubin allocation, with leasing expected in mid-2027. Reservations are open now for whole-rack, single-tenant deployments, with CambridgeNexus operating the infrastructure for you. Explore dedicated NVIDIA Vera Rubin rack leasing and how to reserve capacity.

🧑💻 Accounting workflows spread across PDFs, Excel, emails, and specialist systems, with employees repeatedly transferring and re-entering the same information. Sounds familiar? We faced this challenge. We worked with the client to identify where AI could deliver the most value without disrupting existing systems or removing essential human oversight. In just around a month, we mapped 4 accounting processes, identified 10 automation opportunities, and built 2 working AI automations. One of them reduced reconciliation preparation from hours to approximately 20 minutes. But the most interesting part is how we approached automation despite the integration constraints. Read the full case study to explore the implementation decisions, results, and lessons learned: https://lnkd.in/dt_-hgBz/?utm_campaign=%255Btraffic%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206642164&hsa_acc=508184568&hsa_cam=1206642164&hsa_grp=905118884&hsa_ad=1606109564&hsa_net=linkedin&hsa_ver=3 #AIAutomation #CoPilots #WorkflowAutomation #PE

Another AI pilot won’t fix what’s holding enterprise AI back. The real challenge is making AI work across business processes, enterprise data, and legacy systems, with the governance needed to scale it. That’s the problem C5i is built to solve, combining human expertise, data, and agentic AI to take enterprises from isolated use cases to AI that runs inside core operations. At the Dubai AI Festival, Anees Merchant and Shreyas Sundaresh will be meeting business and technology leaders to discuss what it takes to deliver value beyond the pilot stage. Want to connect? Fill out the form to book a meeting with our team.

Your AI pilots perform. Almost none of them have reached a GxP production workload that can support a regulatory submission.

The board asks you, “So what did you get out of your investment in artificial intelligence?” ⠀ If you were the CIO in that meeting, how would you answer? ⠀ Support uses an agent to write answers for customers. Finance uses an app to help reconcile accounts. Operations uses an agent to identify shipping delays. ⠀ The teams say these tools are helping, but some agents were built outside of IT. And each team has a different definition of "success." ⠀ Since there's no way for the CIO to combine the local results of these initiatives to give a complete picture of the total cost/risk/value to the company, she'll have to say, "I believe we're getting value, but I don't know exactly how much." This accountability gap is something I've seen in Dataiku's Global AI Confessions Report: CIO Edition 2026. The report includes results from a Harris Poll survey of 685 global CIOs. In the survey, the CIOs confessed that: ⠀ - 84% say employees are creating AI agents and applications faster than IT can govern them. - 86% believe their compensation will be linked to measurable AI outcomes. - 76% believe their CIO position may be at risk if their company fails to generate measurable business benefits from AI by the end of 2027. - 88% believe their success with AI will affect their professional reputation or career. ⠀ 𝗪𝗵𝗮𝘁 𝗲𝗮𝗰𝗵 𝗮𝗴𝗲𝗻𝘁 𝗻𝗲𝗲𝗱𝘀 For architecture teams, this starts with three things: - A named owner. - Clear access limits. - A clear way to measure success. When an agent goes into production, these three elements need to remain linked. ⠀ 𝗪𝗵𝗮𝘁 𝗜 𝘄𝗼𝘂𝗹𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 I would track the following for the support agent mentioned earlier: - The number of cases resolved correctly. - The amount of time it takes to review cases. - The cost per resolution of each case. Those numbers would help me decide whether to give the agent more responsibility. ⠀ 𝗪𝗵𝗲𝗿𝗲 𝗗𝗮𝘁𝗮𝗶𝗸𝘂 𝗳𝗶𝘁𝘀 Business units should continue to create new apps while embedding ownership and performance measurement into that creation process. Dataiku Cobuild offers teams the opportunity to convert business objectives into project plans that can be reviewed and approved. ⠀ Dataiku Cobuild helps teams turn business goals into AI projects they can inspect and approve. ⠀ The CIO needs evidence she can explain: what improved, what it cost, and where to invest next. ⠀ You can download the full Global AI Confessions Report , CIO Edition 2026 here: https://lnkd.in/dy5S9hQ2 ⠀ If your board asked you what AI produced for your organization last year, what is the single number you would want to show them? Thanks to Dataiku for supporting this post.

In regulated work, a system whose behavior you can't account for is one you can't put to use, however clever it is. A multi-agent system won't behave the same way twice, so a single accuracy score won't cut it. See the ten-question checklist to evaluate and reproduce multi-agent AI you can stand behind.

Schaffen Sie vom ersten Tag an die Voraussetzungen für ein Milliardenwachstum. Erfahren Sie, wie Sie mit Oracle Cloud Infrastructure Daten vereinheitlichen, die Sicherheit verbessern und KI in großem Maßstab unterstützen können.

Machen Sie KI-Agents zu wertvolleren Unternehmenswerkzeugen, indem Sie Daten und Anwendungen mit Oracle AI-Lösungen verbinden.

NVIDIA released Nemotron 3.5 Super VL today. As NVIDIA's post-training ecosystem partner, AgileRL got early access, and I'm proud of what it shows for any enterprise deciding whether to rent a frontier model or train its own. We trained it to use a web browser, reading what's on screen and clicking through an online shop, a classifieds site, a forum, and Wikipedia. On 114 tasks it had never seen, it went from: - 30.7% before training - to 90.2% after training - ahead of Claude Opus 5.5 at 63.2% That increase in accuracy came from training on the task itself, and for teams in regulated industries like banking and insurance, that's the part worth paying attention to. Your claims process, your admin tools, and the way your team handles a case aren't in any model's pretraining data, so a prompt can describe them, but training is what actually puts them into the model. Owning the model also changes who's in control. When an agent reads screenshots of your internal systems, a model you train and host yourself means those screenshots never leave your network, and you're no longer relying on a provider to keep the model available and behaving the same way between releases. And an agent like this reads a screenshot on every turn, often for many turns per task, so per-token pricing compounds faster than most teams budget for, whereas a model you host is a fixed cost on hardware you own. Nemotron 3.5 Super VL is on Arena from day 0, so if you have a task you'd want an agent for, send me a message and we'll train and deploy it with you, on your infrastructure, with the weights yours to keep. How we trained it, and the full results, are on our blog: