# The Next Era of Advertising and Marketing Accelerate AdTech and MarTech Innovations with AI. ## Build, Deploy, and Scale Next-Generation AI-Powered Advertising and Marketing Applications The advertising and marketing technology ecosystem is undergoing a profound shift as agents, [generative AI](https://www.nvidia.com/en-gb/glossary/generative-ai.md), privacy regulations, and fragmented consumer attention upend traditional ways of planning, creating, activating, and measuring campaigns. In this rapidly evolving environment, brands, agencies, and advertising and marketing platforms are looking for infrastructure and models that can keep pace. NVIDIA equips AdTech and MarTech developers with full-stack infrastructure and software to build, deploy, and scale next-generation [AI-powered advertising and marketing applications](https://www.nvidia.com/en-us/on-demand/session/gtcparis25-gp1076/). ### Scaling Marketing Content Automation With Adobe 3D Digital Twin and Generative AI See how brands are using 3D digital twins, OpenUSD, and generative AI to automate content creation at scale—replacing manual photoshoots with cloud-native pipelines for packshots, composite imagery, and product configurations. [Watch Now](https://www.nvidia.com/en-us/on-demand/session/gtc26-s82492/) ### Building Brand Safety for Global Video: PYLER’s Context-Aware AI Platform PYLER built a trust and safety layer for digital video, verifying brand safety across 2 million videos daily. By moving beyond keyword detection to semantic analysis, PYLER understands context and sentiment in near real time for global brands like Samsung and Kenvue. [Read the Case Study](https://www.nvidia.com/en-gb/case-studies/pyler.md) ## Use Cases ## AI-Powered Advertising and Marketing Use Cases #### Agentic Advertising and Marketing Develop autonomous systems that reason, plan, and execute complex media workflows. With NVIDIA’s full stack infrastructure and software, developers can build agents that move beyond simple automation to true goal-based reasoning. These agents can ingest massive streams of live data to independently negotiate bids, manage multistep cross-channel budget shifts, and orchestrate full-funnel customer journeys. By leveraging a continuous “data flywheel,” these systems learn from every interaction, allowing brands to maintain high-performance, 24/7 campaign execution that is both self-optimizing and brand-compliant at a global scale. [Learn About Nemo Agent Toolkit](https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html) #### Real-Time Decisioning for AdTech Power every bid with intelligence and accelerated compute. NVIDIA's full-stack platform accelerates the complete real-time decisioning loop, from high-frequency scoring and real-time ranking to measurement and attribution. As models grow more complex and new channels emerge, NVIDIA provides the compute density that lets platforms process billions of RTB signals at ultra-low latency and millions of queries per second (QPS), keeping pace with the most demanding auction environments. Generative and agentic AI continuously convert auction signals into optimized bid decisions, closing the feedback loop between decisioning and measurement for better outcomes and more efficient investment. [Explore Data Science Solutions](https://www.nvidia.com/en-gb/deep-learning-ai/solutions/data-science.md) #### Contextual Intelligence Bridge the gap between surface metadata and deep semantic understanding. NVIDIA’s accelerated computing and AI software stack enables large‑scale, multimodal models that can analyze frames, audio, and accompanying text together to derive rich semantic understanding of both ad and content environments. These capabilities power solutions like sponsorship analytics for sports and live events, plus contextual targeting and measurement for relevance and brand-suitability scoring. With GPU‑accelerated inference, these models can run offline on massive media libraries as well as in real time on livestreams, enabling precise contextual personalization, safer adjacencies, and more accountable measurement. [Learn About Language Vision Models](https://www.nvidia.com/en-gb/glossary/vision-language-models.md) #### Content Generation and Optimization Generate brand-consistent experiences with AI. AI generation and real-time optimization tackle the challenge of static, one-size-fits-all creative in an environment where audience intent, channels, and formats change continuously. Platforms across the ecosystem are building solutions on NVIDIA accelerated computing and AI software that use large language models and real‑time signals—behavior, context, and inventory metadata—to automatically adapt ad and digital content for each moment. 3D digital twins of products serve as the precise, ground-truth foundation, ensuring brand assets remain exact while generative AI freely adapts layouts, offers, messaging, and creative variations for each user and surface. The result is always‑on, hyper‑personalized experiences that drive higher engagement, conversion, and media efficiency at scale. [Read About Content Creation Use Cases](https://www.nvidia.com/en-gb/use-cases/content-creation-using-generative-ai.md) Adobe Success Stories ## AdTech and MarTech Success Stories Learn how AdTech and MarTech companies are using AI to scale performance and drive real-time results. [More Success Stories](https://www.nvidia.com/en-us/case-studies/?filters=%7B%22Industries%22%3A%5B%22Media+%26+Entertainment%22%5D%7D&sort=recently_updated) ### Building the Trust Layer for Global Video: PYLER’s Context-Aware AI Platform PYLER set out to build the fundamental trust and safety layer for the digital video ecosystem, addressing the challenge of verifying brand safety across 2 million videos daily. The company’s goal was to move beyond keyword detection to create meaningful units of understanding that could analyze context, sentiment, and safety. This required a unified, high-performance computing architecture, which led them to NVIDIA, capable of embedding, searching, and labeling massive video streams in near real time, ensuring that global brands could deploy their media strategies with transparency and confidence. [Read the PYLER Case Study](https://www.nvidia.com/en-gb/case-studies/pyler.md) ### Streamlining Brand-Consistent Content Production Across Global Teams Many brands struggle with slow, costly, and inconsistent manual processes for localizing content—making adaptation challenging or even out of reach, impacting brand accuracy and audience engagement. Grip, INDG’s SaaS platform developed on NVIDIA Omniverse™ and OpenUSD, addresses these challenges by centralizing OpenUSD asset libraries and creating digital twins for each product. This unified approach gives teams instant access to photorealistic, brand-accurate assets through an intuitive interface powered by advanced 3D and AI workflows. [Read the Grip Case Study](https://www.nvidia.com/en-gb/case-studies/grip.md) ### Unilever Enhances Product Imagery Production With Digital Twins Unilever is one of the world’s largest global consumer goods companies with 3.4 billion people using their products every day across the world. Unilever is enhancing its product imagery workflows using Universal Scene Description (OpenUSD) workflows, real-time 3D rendering, and digital twins powered by NVIDIA Omniverse and in collaboration with creative technology partner Collective World. [Read the Unilever Case Study](https://www.nvidia.com/en-us/case-studies/unilever.md) > Video data is incredibly information-dense, demanding an almost impossible balance between processing speed and semantic accuracy. With NVIDIA’s Blackwell architecture and the NeMo Framework, PYLER has completely shattered these limitations.. — JaeHo Oh, CEO, PYLER > This NVIDIA DGX SuperPOD with NVIDIA Grace Blackwell systems is our engine. This deep integration is one of our secret weapons: We spend more time on breakthrough research and mathematics and less time on repetitive low-level engineering. —  Tomás Puig, CEO and founder,  Alembic > This collaboration with NVIDIA exemplifies how innovation happens, through iterative testing, shared real-world performance data, and truly optimizing for programmatic advertising’s unique challenges. —  Mukul Kumar, Cofounder and President, Engineering, PubMatic Previous Next 1. Quote 1 2. Quote 2 3. Quote 3 ### Resources ## Explore AdTech and MarTech Resources 1. Sessions 2. Podcasts 3. Trainings [See All](https://www.nvidia.com/en-us/on-demand/playlist/playList-0d059dd8-7c5e-491c-8d28-df8de76cd503/) ### Alembic and the Future of AI in Marketing Tomás Puig, founder and CEO of Alembic, joins the NVIDIA AI Podcast to discuss the intersection of AI, data, and marketing. He shares how Alembic uses advanced mathematics and AI—particularly spiking neural networks and causal inference—to help brands extract actionable insights from massive, anonymized datasets. [Listen Now](https://podcasts.apple.com/us/podcast/alembic-and-the-future-of-ai-in-marketing/id1186480811?i=1000715462085) ### Amperity Reimagines Data and Developer Workflows With AI Hear how Amperity’s platform unifies customer data, powers advanced analytics, and brings conversational interfaces to every part of the organization—helping brands activate, segment, and leverage insights at scale. [Listen Now](https://open.spotify.com/episode/0FObII17sWkVBoSv2C773j) ### Canva’s Danny Wu on Giving 230 Million Users Superpowers With AI Danny Wu, head of AI products at Canva, explains how AI is transforming visual communication from a tool for professionals into superpowers for everyone. With over 18 billion uses of its AI features, Canva demonstrates how machine learning evolved from content search to generative design tools that let anyone create on-demand visuals. [Listen Now](https://open.spotify.com/episode/1ME7KRRU31nZq4vyhrWvV8) ![ NVIDIA AI Essentials Learning Series](https://images.nvidia.com/aem-dam/en-zz/Solutions/industries/her/resources-component/her-resources-training-essentials-2560x1440.jpg " NVIDIA AI Essentials Learning Series") ### NVIDIA AI Essentials Learning Series Ramp up your knowledge and accelerate your career in AI. Explore these training sessions and resources to get started with generative AI, data science, and accelerated graphics. [View Series](https://www.nvidia.com/en-gb/learn.md) ### NVIDIA Training Our expert-led courses and workshops provide learners with the knowledge and hands-on experience necessary to unlock the full potential of NVIDIA solutions. [Explore Training Solutions](https://www.nvidia.com/en-gb/learn/organizations/?nvid=nv-int-bnr-800425) ## Frequently Asked Questions ### What is the role of AI-powered agents in advertising and marketing workflows? Agents are autonomous systems that leverage NVIDIA’s full stack to go beyond simple automation, using goal-based reasoning to execute complex media workflows. These agents can ingest massive streams of live data to independently manage multistep tasks, from bid negotiation to orchestrating full-funnel customer journeys, [NVIDIA NeMo Agent Toolkit](https://developer.nvidia.com/nemo-agent-toolkit) enables developers to build agents that perform complex reasoning and execution. ### How does NVIDIA accelerate data science for predictive modeling in AdTech? [NVIDIA’s full-stack platform](https://www.nvidia.com/en-gb/deep-learning-ai/solutions/data-science.md) accelerates the entire data lifecycle, from high-throughput engineering to the continuous training of specialized models. This infrastructure provides the compute density to handle millions of queries per second (QPS) and process billions of real-time bidding (RTB) signals at ultra-low latency to power ad intelligence with accelerated compute. ### What is real-time bidding (RTB) and how does NVIDIA infrastructure support it? RTB is the immediate, per-impression auction of ad inventory, which requires ultra-low-latency compute for processing billions of signals. NVIDIA’s platform is engineered to address performance bottlenecks, such as low-latency requirements for RTB and scaling QPS ad bidding infrastructure. ### What is multimodal media intelligence, and how does it enhance contextual targeting? Multimodal media intelligence uses accelerated computing and AI software to run large-scale models that analyze frames, audio, and accompanying text together for deep semantic understanding of content environments. This capability moves beyond basic keyword detection to allow for precise contextual personalization, safer brand adjacencies, and accurate brand suitability scoring. ### How are 3D digital twins and OpenUSD used for content generation and optimization? 3D digital twins serve as the precise, ground-truth foundation for products, ensuring brand assets remain exact while generative AI freely adapts layouts, offers, and messaging. OpenUSD (Universal Scene Description) workflows, along with NVIDIA Omniverse libraries, are used to centralize these assets and [create digital twins](https://www.nvidia.com/en-gb/case-studies/grip.md) for content automation at scale using OpenUSD. ### What is OpenUSD, and why is it important for brand-consistent marketing? OpenUSD, or Universal Scene Description, is a critical technology used to establish centralized asset libraries and digital twins of products. This approach allows generative AI to adapt creative variations for hyper-personalization while ensuring that the underlying photorealistic product assets are always brand-accurate. ### What are DSPs, SSPs, and exchanges? Demand-side platforms (DSPs), supply-side platforms (SSPs), and exchanges are three key components of the programmatic advertising ecosystem where inventory is bought and sold. [NVIDIA’s platform](https://www.nvidia.com/en-gb/deep-learning-ai/solutions/data-science.md) provides compute density to optimize campaigns and enable more efficient investment across these platforms in the data science and predictive modeling section. ### How do NVIDIA solutions address localization and content production challenges for global teams? NVIDIA Omniverse and OpenUSD address slow, costly, and inconsistent manual localization processes by centralizing brand-accurate asset libraries in a unified approach. This method provides teams with instant access to photorealistic, brand-accurate assets through [advanced 3D and AI workflows](https://www.nvidia.com/en-gb/case-studies/grip.md). ### Where can I find training and resources to develop applications using NVIDIA’s AI solutions? NVIDIA offers the AI Essentials Learning Series, expert-led courses, and workshops to help developers and IT professionals accelerate their careers and knowledge in AI. You can explore the resources available through the [NVIDIA AI Essentials Learning Series](https://www.nvidia.com/en-gb/learn.md) for training in generative AI, data science, and accelerated graphics. ### What is generative AI, and why is it important for modern marketing content? [Generative AI](https://www.nvidia.com/en-gb/glossary/generative-ai.md) is a form of artificial intelligence that creates new content, and in marketing, it is used to automatically adapt ad and digital content based on real-time signals, channels, and audience intent as a core technology in this shift. This ability to [continuously adapt creative variations](https://www.nvidia.com/en-us/use-cases/content-creation-using-generative-ai.md) drives higher engagement, conversion, and media efficiency at scale. ### How does NVIDIA help AdTech and MarTech companies scale performance? NVIDIA provides full-stack infrastructure and software, including accelerated computing and specialized AI models, that enable companies to scale tasks from processing billions of RTB signals to deploying self-optimizing agentic systems. This infrastructure is designed to solve the most complex computational challenges, driving real-time results and shifting perception to view NVIDIA as an infrastructure and software partner. ### How do agentic systems achieve “self-optimizing” and “brand-compliant” campaign execution? Agentic systems learn from every interaction by leveraging a continuous “data flywheel” of massive streams of live data to achieve self-optimization. Brand compliance is maintained by embedding these constraints within the goal-based reasoning and execution frameworks of the autonomous systems. ### What kind of models are used to bridge the gap between surface metadata and deep semantic understanding in media? Large-scale, multimodal models are enabled by NVIDIA’s accelerated computing and AI software stack to analyze frames, audio, and text together. These models are more powerful than traditional keyword detection and allow for a rich [semantic understanding of media environments](https://www.nvidia.com/en-gb/glossary/vision-language-models.md). ## Get Started ### Explore Our Network of Partners Find the right solution provider, OEM, ISV, or cloud partner to help address your current business needs and achieve success in today’s ever-changing markets. [Find a Partner](https://marketplace.nvidia.com/en-us/enterprise/partners/?location=US&page=1&limit=15) #### NVIDIA Accelerated Application Catalog Explore a wide array of NVIDIA-accelerated applications, tools, and services built on NVIDIA platforms. [Explore Apps](https://marketplace.nvidia.com/en-us/enterprise/applications/) #### Request a Consultation Our experts can help your business unlock potential and unleash innovation. [Let’s Talk](https://www.nvidia.com/en-gb/contact/sales.md)