B2B ad examples

Most enterprise organization’s AI intake process is a spreadsheet, three Slack threads and someone's memory. Credo AI is the system of record for enterprise AI governance. Every use case, model, agent and connector in one place, with the approval trail and the evidence already mapped to the frameworks you report against.

When an AI use case takes a quarter to approve, the business stops asking and ships anyway. Credo AI is the system of record for enterprise AI governance. Every use case, model, agent and connector in one place, with the approval trail and the evidence already mapped to the frameworks you report against.

Introducing Invent a Dataset. Most dataset pipelines start with existing data and work backward. You find what's available, then spend weeks labeling and reshaping it into something usable. The final result is a compromise. With Invent a Dataset, you describe the dataset you need and the system builds it. It delivers structured and training-ready data, without requiring existing data or upfront schema design. Dataset creation used to start with collection. Now it starts with specification.

We're giving all of our customers an AI engineer: Meet Val, Galtea's new AI agent, who works alongside your team like an extra AI engineer. Val knows your product, understands your evals, and helps you deploy reliable AI in production. What Val can do: → Turn production traces into actionable insights without going through thousands of them manually. → Understand what's wrong in your AI product before your users find out in production. → Generate and run evaluations without starting from scratch. What Val can't do (and where humans are still fundamental): → Decide what "good" means for your product. → Provide domain expertise. → Own accountability when quality drops. → Make high-level architectural decisions. Val is live for all Galtea customers today. Not a customer yet? You can book a demo and meet Val: https://lnkd.in/eV5wftf6

On 2 August, three transparency obligations under the EU AI Act became applicable across the EU market: → If your AI interacts with a person, it has to disclose that it is AI. → If it generates synthetic images, audio, or video, that content has to be labeled. → If it runs emotion recognition or biometric categorization, the people exposed to it have to be informed. For most teams, disclosure was a design decision made once, at launch. Under the AI Act, it is now a behavior you are accountable for, on every release and in front of every user. And you have to verify it the same way you verify anything else your system does. Three questions worth putting to your AI team: → Does our assistant reliably disclose that it is AI across the real range of conversations users actually have, not just the scripted happy path? → Does every piece of synthetic content we generate carry the label, across every format it ships in: image, audio, and video? → When we run emotion recognition or biometric flows, does the required notice actually reach the person in the interaction? And the one that matters most under the new regime: → Can we produce the evidence to prove all of this, on demand, if a regulator or an auditor asks? None of this shows up in a standard accuracy or latency eval. Disclosure has to be tested against realistic scenarios, re-run on every new model version and every prompt change, and captured as evidence, not checked once at launch and assumed stable. Manual spot-checks do not scale to that. And "we designed it to disclose" is not the same as "we can show that it still does." P.S. I'm running a free workshop on building evals for behaviors like this, which an off-the-shelf metric can't measure. If your team is building AI in a regulated industry, join me: https://lnkd.in/et-RWCjm

“Booked out” usually means booked to someone else's contract. Type the cluster you need and see who actually has capacity today.

One product shoot doesn’t always cover every setting a campaign eventually needs. Product Shots lets creative teams take an existing product shot and place it into new settings, so the same product can show up across more of the campaign without starting from scratch each time.

We’re Making Advanced AI Drug Research Free for Everyone At Apodex, our goal is to build Discoverative AI—AI that can help humanity discover things we do not yet know, not just generate answers from what we already know. I believe this is one of the most valuable things AI can do for humanity. And drug research is one of the best places to start. Few things matter more than finding better ways to prevent and treat diseases. There are thousands of small biotech companies and research teams working on important medical problems. Many have good ideas but do not have enough AI experts, computing resources, or research tools to explore them. I don’t think a good scientific idea should have to wait for a big budget. That is why we decided to make a growing set of Apodex’s AI drug research tools available for free. One thing I want to emphasize: we are offering much more than free computing. At Apodex, we have a team of scientists and AI researchers who are constantly improving our models, algorithms, and research tools. We are starting with clinical trial forecasting, drug repurposing, drug interactions, and safety assessment. But this is just the beginning. What matters even more is what you need that we have not built yet. We want to listen and develop new tools and algorithms based on real research needs. Some may become free tools for everyone, while others may require deeper collaboration. Our goal is to make useful AI research capabilities available to more people, while continuing to build better ones. So please don’t just come to Apodex to use what we have built for free. Come and tell us what we should build next. https://drug.apodexlab.ai/ #AIforScience #DrugDiscovery #Apodex

Faster generative video means more variations, more testing, and campaigns that launch on your timeline. Higgsfield Enterprise gives your team priority speed when deadlines matter most.

Big-budget production value, without the big-budget shoot. See what Higgsfield Enterprise can do for your brand.

Symbiosis 5 is almost here and I hope you'll join us. I still remember when Druid AI was at the beginning, just a few people in an apartment, with a platform that was in its early days. On October 20th, our customers and partners take the stage to show what they've built with it. This one is for the people who trusted it, and made it real. London or online. Looking forward to it! #Symbiosis5 #AgenticAI

Where should your first AI agent start? Your peers have already made that call, and 15 months of production data show which choices are paying off. Michael Yang walks through Druid's 2026 AI Adoption Benchmark industry by industry, using deployments from around the world, and the AI Agent Reference Model: a blueprint for what every production AI agent needs, and a common scorecard for comparing one agent with another. On October 20th, at Symbiosis 5. Not to be missed! ❇️ Save your seat to attend online or in London. (we've added details in the comments section below) #Symbiosis5 #AgenticAI #DruidAI

Making a big decision? The right person to learn from isn’t always in your network. Describe what you’re facing. Delphi finds people who’ve been there and lets you explore their perspectives and ask follow-up questions

There are rare driving scenarios that happen once every few million miles that more data won’t necessarily help. What would help would be to find rare scenarios, prioritize them by risk, and capture enough context to learn from them. We break down how it works in our latest article: https://lnkd.in/dukrywGK Want a second set of eyes on your AV data pipeline? Let’s chat: chemin.com/contact

Our client needed native fluency in English, Malay, Mandarin, and Cantonese, not just translation. We engaged 92 language experts in an average of 14 days and hit 96.39% accuracy.

We have signed $70 million in commercial contracts over a 12-month period and is on a path to operating breakeven — high-end autonomy on a software company’s economics. Physical AI has its own scaling law, and ours is capital-efficient: more capability per dollar of data, compute and fleet. One foundation-model platform, trained with unsupervised learning on the structure of the physical world, runs on mass-market automotive-grade compute with a fraction of the typical data, and carries a single model lineage from L2+ passenger vehicles to heavy industrial equipment and robotics. By the numbers: 🔵 $70M in commercial contracts signed over a twelve-month period, across global automotive OEMs, Tier 1 suppliers and industrial automation companies 🔵 Perception, full-stack ADAS/autonomous driving, automated data labeling and generative simulation on one platform 🔵 Production-bound vehicle programs in deep integration with next-generation architectures ahead of start of production 🔵 Production-track deployment beyond the road: perception for heavy industrial equipment in open-pit mining That is what capital-efficient scaling looks like for physical AI: the economics of a software business, applied to the physical world. Read more: https://lnkd.in/gUGjuWTP #AutonomousVehicles #PhysicalAI #Robotics #DeepTech #AI #Automotive #HelmAI

Off-the-shelf trading platforms have limitations. Proprietary builds can take years. There's a third option.

You don't need a sales call to price a dedicated node. Tell us the GPU, the count and the duration — we come back with availability and pricing.

Most job titles don’t reflect the real work being done. At Digital Futures, we match experienced Data, AI, and Transformation Consultants with enterprise projects where technology directly drives business outcomes. Explore our experienced consulting opportunities today.

Are you an experienced tech consultant who can translate complex data and AI capability into clear business value? Digital Futures is hiring experienced engineers, analytics leads, and transformation specialists. Discover how you can deliver high-impact client projects.

+5.4 AP on EO. +2.94 AP on thermal. No new flight trials. A leading German drone manufacturer added 30,000 AI Verse synthetic EO and IR images to its own training data. Its vehicle detector beat a +3 mAP target on a camera it had just fitted. In the article we explain how we built the dataset in five rounds of feedback and the full EO and IR results. Read the case study ⬇️

Dr. Uri Yerushalmi and Silvio Savarese, EVP & Chief Scientist at Salesforce, unpack how market models and large language models are complementary - and together, form the foundation of a true AI-powered enterprise intelligence stack. Watch the full session 👉 https://lnkd.in/gMpYG4T6 #Salesforce #AI #Intelligence #Market #Model

🔎 Ile narzędzi AI działa dziś w Twojej organizacji poza wspólnym standardem bezpieczeństwa, kosztów i dostępu do danych? Pojedynczy chatbot dla HR, osobny asystent dla sprzedaży, eksperyment RAG w dziale prawnym. Każde rozwiązanie może być wartościowe, ale bez wspólnej warstwy zarządzania powstaje jednak kosztowny i trudny do audytu patchwork. Extentum AI porządkuje ten krajobraz w jednym, bezpiecznym środowisku dla całej organizacji. ✅ Buduj agentów i workflowy dla konkretnych procesów. ✅ Łącz AI z firmową wiedzą oraz systemami, takimi jak CRM czy ERP. ✅ Zarządzaj dostępami, modelami i integracjami centralnie. ✅ Monitoruj użycie, koszty oraz działania agentów. ✅ Wdrażaj on-premises lub w private cloud, zachowując kontrolę nad danymi. To nie jest kolejny punktowy eksperyment z GenAI. To fundament, na którym można bezpiecznie skalować AI w całej organizacji. Chcesz zobaczyć, jak może wyglądać jedno środowisko AI dla Twojej firmy? Porozmawiajmy: https://lnkd.in/e8mk4xde #AI #ExtentumAI #EnterpriseAI #AIGovernance #JednoŚrodowiskoAI

Za nami trzy intensywne dni Targów World of AI & BIG DATA EXPO. Dziękujemy za inspirujące rozmowy o tym, jak wdrażać GenAI w organizacjach tak, aby wspierała realne procesy biznesowe. 🤝 Mamy też powód do dumy: otrzymaliśmy tytuł Eksperta Branżowego. To dla nas potwierdzenie, że rozwijamy rozwiązania odpowiadające na konkretne potrzeby firm. 🏅 Podczas rozmów najczęściej wracały trzy tematy: ✅ RAG i wiedza firmowa - jak budować agentów AI, którzy odpowiadają na podstawie zweryfikowanych dokumentów i źródeł organizacji. ✅ Włączenie zespołów biznesowych - nawet przy wsparciu technicznym kluczowa wiedza o procesach, wyjątkach i zadaniach pozostaje po stronie osób, które wykonują je na co dzień. ✅ Powtarzalność procesów - GenAI potrzebuje jasno określonych reguł biznesowych, kontroli i właściwie zaprojektowanych scenariuszy, aby jej działanie było przewidywalne. Jeśli chcesz przejść od PoC do działającego procesu biznesowego, umów się na demo: https://lnkd.in/edEFX9cu. Pokażemy, jak może wyglądać bezpieczne wdrożenie GenAI w Twojej organizacji. Dziękujemy wszystkim, którzy odwiedzili stoisko C14. Do zobaczenia na kolejnych wydarzeniach: AI Summit, Państwo 2.0 i CIO Trends. 👋 #AI #ExtentumAI #WorldofAIandBIGDATA #PlatformaGenAI #PolskaTechnologia