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Wondering what the Great Wave AI Platform actually does? Here are the answers: 1. De-risks and accelerates GenAI product delivery 2. Provides a single pane of glass through which to observe and manage agents 3. Delivers governance, risk management and assurance out of the box Don’t believe me …? Check out what Phill Magill Chief Engineer at PXC - the UK’s fastest growing independent wholesale telecoms platform - says about how our platform has turbo-charged their GenAI journey. Link to write-up and full video in comments 👇

Why we built the Great Wave AI platform We didn’t set out to “revolutionise AI,” “disrupt industries,” or any of the other phrases that usually come with too many pitch decks and not enough sleep. We built Great Wave AI for a much simpler reason: because trying to make AI actually useful inside real organisations kept turning into a farce. Everywhere we looked, teams were stitching together half a dozen tools and whatever the hyperscalers had announced that week. Developers were drowning in frameworks. Non-developers were told to “just use no-code” (which usually meant “good luck”). And the moment anyone said “regulated environment,” half the available tooling quietly left the room. So we took the scenic route and built the thing we wished already existed: a platform where you can break work into sensible, specialised agents, give them access to the APIs and information they need, and have them reliably put everything back together again with human feedback and auditability built in. No drama. No rituals. No hype. And, next year even the option to run the whole thing on the edge, should you need to deploy an agentic system somewhere where god forbid, there is sometimes a poor internet connection (PIC), like a police car, because the PIC crew shouldn't miss out because of mother nature. Not reinventing AI. Just making it behave, so that it can actually make the transformational challenges promised. That is what its all about right ?

Most AI companies ask enterprises to do something completely unrealistic: “Just upload all your documents to our platform and we’ll take care of the rest.” For an enterprise company with tens of thousands of documents across hundreds of different data sources??? That’s not happening. It would mean: - Internal alignment meetings across every department - Changes to established processes and procedures - Moving sensitive data outside your firewall - Getting compliance to sign off on all of it What a nightmare. This is why we built the Abacus Indexer to be decentralized. Instead of asking you to bring your data to us, our Indexer goes to where your information lives today – indexes it right there – and brings it together. The Indexer stays completely inside your infrastructure. So that you have peace of mind and a sense of security when using Abacus. To check out our integrations, go to the link in our comments.

We are heading to The AI Summit Series in New York Dec 10–11 Our Head of Engineering, Yujing Qian, joins the panel: “The Custom Code Conundrum: Fine-Tuning vs. Starting from Scratch” 📅 December 10 | 2:50 PM 📍 Data Excellence Stage We’re looking forward to the discussion alongside Svetlana Makarova (Mayo Clinic), Khaoula ILAJE, Freddy Castro (Definity), and Kevin McGrath — each bringing a different perspective on model adaptation and compute reality. We’ll be at Booth 109 with: ☁️ Inference Engine 2.0 demos ⚡️ GPU + agent infrastructure tooling 🧠 RL stack previews If you’re attending, stop by — we’d love to connect. #AISummitNY #GMICloud #AIInfrastructure #InferenceEngine #GenerativeAI #CloudComputing

With Microsoft, we are building a long-term partnership spanning cloud, compute, governance and real-world deployment. At its core is a shared belief that trusted infrastructure, responsible innovation and the capability to scale are fundamental to the future of AI. Foundations built to serve governments, industries and communities for years to come. #G42 #Exponentialai #AI #Microsoft #Technology #InnovationHub

Progress compounds when it's built on strong foundations. Our partnership with Microsoft continues to grow because it's built on a shared conviction: AI should be secure, responsible and designed to create lasting value. Across the G42 ecosystem, we're expanding AI infrastructure, advancing responsible AI governance and extending trusted capabilities from cloud and compute to healthcare and beyond. Through Inception42, this partnership also brings that vision to life by integrating Insight with Microsoft Teams, enabling interoperability between Microsoft 365 Copilot and Catalyst and co-developing AI products like Seraj. Because the future of AI is built through partnerships that endure. Experience our collaboration with Microsoft firsthand at the Innovation Hub in Dubai. Register your interest. #G42 #G42ai #Exponentialai #AI #Microsoft #InnovationHub

Fusemachines’ Global Reseller Network enables companies to become authorized resellers of AI Studio and AI Engines, enabling enterprises in retail, real estate, and food and beverage to build and deploy highly accurate, industry specific AI solutions at scale. Become an authorized reseller. Apply here: https://bit.ly/4oMcTgu

An exciting new chapter begins for Fusemachines. Here’s to growth, innovation, and #DemocratizingAI.

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NVIDIA Nemotron 3 Ultra — 550B total, 55B active, 1M token context, Hybrid Transformer-Mamba MoE model is available Day 0 on FriendliAI’s frontier inference cloud 🙌 We’re built for the hardest part of agentic AI: orchestration. It reads your full context, breaks down complex goals, delegates across sub-agents, and synthesizes everything back at a speed that makes long-running agents viable in production. What FriendliAI brings to production-ready: ⚡ 2×-5× faster 💰 50–90% cost savings ⛑️ 99.99% uptime 🔒 Deploy on your own infra Deploy Nemotron 3 Ultra → https://lnkd.in/gXt3T_CK Find your Nemotron model → https://lnkd.in/g3Ttd9Di Link to blog in the comments 👇 #NVIDIA #Nemotron #AgenticAI

One benchmark won't tell you which inference provider to pick. The Pareto front will. A single point—best TTFT, peak throughput—only describes one operating condition. Your workload doesn't run at one point. Vary batch size and concurrency and you get a whole curve: the Pareto front of latency vs. throughput. Each non-dominated point is the best throughput achievable at a given latency. That's the thing to compare. ❓ Why it matters: Providers can swap places along the curve. Provider A wins at the benchmark point; Provider B owns the rest of the frontier your traffic actually hits. 💡 Pick your point by workload: → chat / voice agents → minimize TTFT → batch / summarization → maximize TPS → search / RAG → somewhere in between Real question isn't "who wins the benchmark?" It's "who has the better frontier for my model, my SLA?" That's the question worth asking — and worth measuring. Full methodology in the comments below 👇

Are you shipping AI agents that your system flags as "healthy," even when they're failing in production? Fiddler Agentic Observability closes this go-live gap by providing the unified platform you need to maintain full visibility, context, and control. With Fiddler, you can: 🎯 Test What Matters: Evaluate agents with curated datasets and run experiments before launch. 🕵️♀️ Catch Hidden Failures: Get full visibility into reasoning chains, dependencies, and cross-agent coordination that traditional monitoring tools can’t see. 🔍 Find the Root Cause: Diagnose and pinpoint issues across the entire agentic hierarchy. 🎛️ Control Costs: Eliminate hidden costs related to calling external models with our “batteries included” Fiddler Trust Models and maintain data privacy. See how Fiddler Agentic Observability can help: https://lnkd.in/dThZ32qR

Join our live webinar to see how Fiddler unifies evaluations and production monitoring into a continuous feedback loop that improves accuracy, safety, and deployment speed. Few spots left. Claim yours👇

Validating perception models requires more than just bounding boxes. It requires physics-based sensor simulation that mirrors reality. The new Foretellix + Parallel Domain solution delivers exactly that: ✅ Automated generation of scenarios from real-world logs ✅ Hyper-realistic sensor sim (Camera, Radar, LiDAR) ✅ Closed-loop validation for the full AV stack See how this joint solution is accelerating AI-powered validation and testing.

𝐘𝐨𝐮 𝐜𝐚𝐧 𝐧𝐨𝐰 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐲𝐨𝐮𝐫 𝐟𝐮𝐥𝐥 𝐀𝐕 𝐬𝐭𝐚𝐜𝐤 𝐰𝐢𝐭𝐡 𝐩𝐡𝐨𝐭𝐨𝐫𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐭𝐰𝐢𝐧𝐬 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐝 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐟𝐫𝐨𝐦 𝐝𝐫𝐢𝐯𝐞 𝐥𝐨𝐠𝐬. Validating perception models requires more than just bounding boxes. It requires physics-based sensor simulation that mirrors reality. The new Foretellix + Parallel Domain solution delivers exactly that: ✅ Automated generation of scenarios from real-world logs ✅ Hyper-realistic sensor sim (Camera, Radar, LiDAR) ✅ Closed-loop validation for the full AV stack See how this joint solution is accelerating AI-powered validation and testing. 👉 Read the full article #AutonomousVehicles #Simulation #AI #Safety #DigitalTwins

How our AI predicted a massive demand spike – without even knowing Madonna was behind it 🎤 🇧🇷 When a last-minute pop-up show appeared in Rio, it wasn’t on any standard event calendar. But our Market Model still picked up the signal - and immediately adjusted availability and prices. In complex markets, value comes from detecting market shifts early 🚀 📈 Watch the full interview to see how our AI supports smarter enterprise decision-making: https://lnkd.in/dXcM93rp #AI #Decision #Making #Market #Signals

𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐢𝐬 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐡𝐚𝐫𝐝𝐞𝐫. 𝐓𝐫𝐚𝐜𝐞𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐞𝐯𝐞𝐫. For organisations operating in regulated environments, it’s not enough to say what happened in an AI workflow. ➡️ You need to prove it. At Exfluency, #blockchain is used to ensure the veracity and provenance of data within AI-driven workflows. Every step can be traced, verified, and audited – creating a clear record of how content, knowledge, and decisions were produced. This matters for organisations navigating frameworks such as #GDPR and other compliance requirements where transparency and accountability are essential. The result: #AI systems that are not only powerful, but auditable and trustworthy. ➡️ 𝐋𝐞𝐚𝐫𝐧 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐡𝐨𝐰 𝐛𝐥𝐨𝐜𝐤𝐜𝐡𝐚𝐢𝐧 𝐬𝐮𝐩𝐩𝐨𝐫𝐭𝐬 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐭 𝐀𝐈 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: https://lnkd.in/drQCY5ym #DataGovernance

🧸 𝐀𝐈 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐮𝐬𝐭 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐚 𝐭𝐨𝐲 Many organisations are rushing to deploy #AI tools. But the real challenge isn’t generating answers, content, or translations. The challenge is 𝘵𝘳𝘶𝘴𝘵: 🔹 Where did the information come from? 🔹 Which model produced the output? 🔹 Who had access to the data? 🔹 Can the result be traced and verified? 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲, 𝐚𝐮𝐝𝐢𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬, 𝐀𝐈 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐥𝐢𝐭𝐭𝐥𝐞 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐚 𝐜𝐥𝐞𝐯𝐞𝐫 𝐭𝐨𝐲. At Exfluency, we focus on making AI usable in serious organisations – where confidentiality, multilingual knowledge, and verifiable sources actually matter. That means: 🔹 controlled AI workflows 🔹 trusted knowledge bases 🔹 model choice per task 🔹 and traceable output with clear data provenance Because without trust and transparency, AI doesn’t scale beyond experimentation. #AIGovernance #AgenticAI

"The only AI SDR that actually books meetings." Every vendor says it. We'll do something different, we'll show you in 30 minutes. Watch it research accounts, write personalized outbound, send, handle replies, and book meetings end to end. If it's not better than what you're running now, you've lost half an hour. If it is, you've just found your next hire.

Every sales newsletter this quarter has declared the AI SDR dead, reborn, and inevitable, usually in the same week. Here's what's actually true: The tools that draft email copy and hand it back to a human? Still require the human. The autonomous kind that researches, personalizes, sends, replies, and books meetings on your stack is the one rewriting the role. EverWorker is that kind. See it run on your ICP in a 30-min live demo. No deck. No whitepaper. Your pipeline, your cadence, your data.

Most pricing systems aren't slow. They're blind 🕶️ While every airline is busy reacting to last night's data, the market has already moved. Fetcherr's Director of Deep Learning, Hadar Sharvit, broke down why at Aviation Festival Asia - and what it actually takes to price ahead of the curve. Watch the full keynote 👉 https://lnkd.in/dtSxecNg #AviationFestivalAsia #AI #MarketModel #Pricing #RM

Over a decade of working with organisations where getting decisions right genuinely matters, at scale and under pressure, has left me with one consistent observation. The sectors look very different from the outside, and this holds regardless of industry: financial services, energy, pharma, national infrastructure. But the underlying challenge is remarkably similar. The number of decisions organisations must make has multiplied. The stakes of getting them wrong have increased. The operating model for making them has barely changed. When we founded Faculty, our mission was the safe and widespread adoption of AI. What's become increasingly clear to me is that "widespread" won't come from better models alone. It will come when organisations are willing to rethink how decisions are made, coordinated, executed, and learned from over time. That is the shift most enterprises are still yet to make. I've tried to put some of my thinking on what it actually requires here: https://lnkd.in/eF-UVzzB

Modern healthcare systems are under pressure to do more with less. Yet most hospitals still rely on reactive, manual processes that make it difficult to stay ahead of patient flow demand, at a time when the cost of getting it wrong has never been higher. Drawing on the experience of TeleTracking and University of Louisville Health, Andy Brookes, CTO and Co-Founder of Faculty, explores how Faculty Frontier™ enabled frontline teams shift to proactive, coordinated decision-making at scale - helping patients get home faster. Read the full article here: https://lnkd.in/eu9_3ktJ