LinkedIn image ad examples

Every research report you publish has hidden opportunities inside. The problem? Traditional PDFs can't: • Capture leads where readers show interest • Tell you what content resonates • Keep readers engaged without external tools Factify fixes this: ✅ Built-in lead capture - Gate sections, insert forms anywhere in your report ✅ Advanced analytics - See which pages hold attention and where readers drop off ✅ In-document AI chat - Readers get answers without leaving (no more ChatGPT copying) Transform your research from static PDFs to interactive revenue engines.

Once a PDF leaves your inbox, it’s out of your control — passed between subs, printed, or used weeks later from the wrong version. Factify keeps every drawing live, trackable, and revocable, so you know exactly who viewed it, and can kill access anytime.

Pick a track. Ship something in three days. 1. Specification Elicitation: pulling formal specs out of code, docs, conversations. 2. Specification Validation: checking whether a spec captures intended behavior. 3. Spec-Driven Development and Evaluation: generating and ranking implementations from a spec. 4. Adversarial Robustness: stress-testing formal methods pipelines. Generalist engineers welcome. No formal methods background required, but expect to lean on teammates who have it. Top teams: invitation to the Secure Program Synthesis Fellowship (June - October 2026). Sign up now.

【ELYZA Works with KDDI】フォーム形式で統一されるから使いやすい！現場主導で自社専用のAIアプリを作れるから日々の業務を効率化できる。企業のAI導入課題も解決。

Get a free 2-week, hands-on trial with Edge Impulse's ML experts. Our solutions engineers will assess your data compatibility, optimize models, and validate feasibility for anomaly detection, predictive maintenance, or smart devices. 10-20 hours of guided experience. Limited spots available.

One of our Applied Research Engineers, Yan L., built a smaller, open-data-trained audio embedding model for real-world sound classification and anomaly detection use cases. Here's how it came together. https://lnkd.in/eKAQNFP5 #edgeAI #AI

What does it actually mean for your business to have sovereignty over its AI? Our CEO Brian Mengwasser breaks it down to two conditions: a 𝗴𝘂𝗮𝗿𝗮𝗻𝘁𝗲𝗲𝗱 ability to produce, and 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆 over your own information. Lose either one, and you're not really in control of your AI - or, increasingly, your business. This is Part 1 of an ongoing series we're tackling on Sovereign AI and what it means for the physical operations that keep society running. Link in comments. #SovereignAI #PhysicalAI #DataSovereignty #AISovereignty #AI

Sovereign AI comes down to two conditions: a guaranteed ability to produce, and authority over your own information. Most enterprises have neither today. Our CEO breaks down why AI sovereignty is no longer optional - and what it actually takes to get there.

Activate real business impact with ChatGPT in 4 weeks. Eliza helps organizations move beyond simple tasks to high-value, ROI-unlocking use cases. Our hands-on training model embeds ChatGPT into daily workflows through: ✔ Co-build sessions with internal champions to tailor use cases by role and function ✔ Playbooks and prompts that target measurable outcomes like hours saved and quality improved ✔ Team workshops and office hours that drive adoption and confidence The result: rapid scale, high engagement, and visible ROI across the organization. Want to see this model in action? Request a free consultation to get a custom AI adoption roadmap for your organization. Fill out the form to connect.

Most companies have ChatGPT, but few use it for high-value, ROI-unlocking use cases. It's time to move beyond simple tasks. Eliza's 4-week, hands-on training model helps you do exactly that, embedding ChatGPT into daily workflows through: ✅ Co-build sessions with internal champions to tailor use cases by role and function ✅ Playbooks and prompts that target measurable outcomes like hours saved and quality improved ✅ Team workshops and office hours that drive adoption and confidence The result: rapid scale, high engagement, and visible ROI across the organization. Want to see this model in action? Request a free consultation to get a custom AI adoption roadmap for your organization. Fill out the form to connect.

Millions of Australians are paying duplicate fees because they hold more than one super account. Our software uses the power of AI to identify which of your members are most likely to consolidate. That means stronger balances for your fund and better outcomes for your members. Discover more at elula.ai

About 1.2 million Australians switch super funds each year. That’s a lot of member balances on the move. Our AI retention software helps you identify members most at risk of leaving and gives you the right actions to retain them. Protect your fund and strengthen member loyalty with the power of Elula. Discover more at elula.ai

Imagine having one platform providing seamless access to the leading AI tools. Simplify and excel together.

Imagine if your team could connect data seamlessly across systems while leveraging advanced AI models such as ChatGPT or Claude for actionable insights instantly?

Most banks pilot AI. Few turn it into revenue. Which side is your institution on? 78% of financial institutions are experimenting with AI. But only 28% are scaling it to drive measurable revenue. Why the gap? And how can you bridge it? Our latest whitepaper dives deep into: ✅ Revenue opportunities in Southeast Asia, Latin America, and MEA ✅ The critical barriers to scaling AI—and how to overcome them ✅ Real-world use cases If your goal is to move AI from experimentation to real revenue impact, this report is made for you. 👇 Download the full report now for actionable insights and execution frameworks.

AI is no longer an experiment in banking and insurance, it’s a profit engine. From credit decisioning to claims automation, leading BFSI players are already reducing costs and unlocking new revenue streams. With the AI market projected to grow from USD 35B in 2023 to USD 368B by 2032, the gap between adopters and laggards is widening fast. Are you positioned to win?

AI agents are increasingly deployed on long-horizon tasks across complex, fragmented enterprise systems. To do that work well, they need a unified view of the data, the code, decisions and history. Right now, most agents don't have that. They reconstruct it from scratch, on every single query. The common responses to this problem? "Just give the agent more tools." "Wait for the next model release." "An infinite context window will solve this." Fair objections. We've heard them all, and we've stress-tested them in production. Runtime reconstruction taxes every query, and the result is fragile code, authority bias toward clean-but-wrong config files, and no lasting memory once the session ends. A larger context window doesn't help when retrieved documents contradict each other and the agent has no way to tell which is canonical. Stronger models don't fix missing data infrastructure any more than faster CPUs make database indices unnecessary. We published a deep dive on why AI agents need a context graph, when it's worth building one, and what it actually takes to build it. In the article, we cover: • What a context graph is and the four operational roles it plays • The four most difficult construction challenges • The importance of calibrated trust and why it matters more than raw retrieval power • Where the engineering investment pays off and where simpler retrieval is enough Read it here: https://lnkd.in/gD89aUtX Have you run into these issues with long-running agents? Tell us your story in the comments. #AIAgents #ContextGraph #DataInfrastructure #KnowledgeGraph #EnterpriseAI #MLOps #AgenticAI #AIEngineering

AI agents are increasingly deployed on long-horizon tasks across complex, fragmented enterprise systems. To do that work well, they need a unified view of the data, the code, decisions and history. Right now, most agents don't have that. They reconstruct it from scratch, on every single query. The common responses to this problem? "Just give the agent more tools." "Wait for the next model release." "An infinite context window will solve this." Fair objections. We've heard them all, and we've stress-tested them in production. Runtime reconstruction taxes every query, and the result is fragile code, authority bias toward clean-but-wrong config files, and no lasting memory once the session ends. A larger context window doesn't help when retrieved documents contradict each other and the agent has no way to tell which is canonical. Stronger models don't fix missing data infrastructure any more than faster CPUs make database indices unnecessary. We published a deep dive on why AI agents need a context graph, when it's worth building one, and what it actually takes to build it. In the article, we cover: • What a context graph is and the four operational roles it plays • The four most difficult construction challenges • The importance of calibrated trust and why it matters more than raw retrieval power • Where the engineering investment pays off and where simpler retrieval is enough

DKube reads every supplier paper for you, so you stop manually reviewing purchasing and receiving documents.

DKube reconciles the documents first and sends only the exceptions to your team.

Yesterday, our CEO and Founder Uljan Sharka joined John Thornhill on the Sifted podcast for a conversation on Europe's real position in the AI race. In the podcast, Uljan discussed his own path into tech and how he came to found Domyn. He also covered why the next phase of AI will be won through applications and distribution (not model size) and how fast-improving chips are closing the compute gap faster than expected. Thank you John and the Sifted team for having Uljan on. 🎧 Listen to the full episode: https://lnkd.in/dWyYHNUp

TIME has named our founder and CEO Uljan Sharka, to the 2026 TIME100 AI, its annual list of the world’s most influential people shaping the future of AI. Uljan founded Domyn with a clear conviction: organizations should be able to own and control the intelligence they depend on. Today, that vision is taking shape through Domyn’s work to make sovereign AI a reality, from building AI for the world’s most regulated industries to advancing the frontier models and infrastructure that can help Europe lead in AI. A significant recognition of the ambition Uljan continues to drive forward, and the work taking place across Domyn to make it a reality. Congratulations, Uljan. 👏 👉 Read Uljan’s TIME100 AI profile: https://lnkd.in/ebtC5gnM 👉 Read the news here: https://lnkd.in/er2GrQAU #TIME100AI

Co-engineered with NVIDIA. Validated on the Dell AI Factory. Endorsed by SAP. Deployable in the cloud, on-premise, hybrid, or air-gapped. DataRobot is the agent workforce platform purpose-built for manufacturing — running on the infrastructure you've already invested in, integrating with the systems your team already uses, and operating with the governance your audit teams require.

Manufacturing planning cycles can't keep up with the supply chain anymore. Tariffs shift overnight. Suppliers go dark. Equipment fails between maintenance windows. Most AI tools surface the problem in a dashboard and wait for a planner to act on it. Agentic AI closes the loop — sensing, planning, and acting in real time, inside the systems your team already runs.