Product Directory

NVIDIA Isaac Sim is an open source reference framework built on NVIDIA Omniverse libraries for robotics simulation, testing, and synthetic data generation in physically based virtual environments. It is fully extensible, letting developers build custom OpenUSD-based simulators or integrate its capabilities into existing testing and validation pipelines, ingesting CAD, URDF, or real-world capture data and converting it into USD. It supports controllable synthetic data generation and works alongside NVIDIA Isaac Lab for robot learning and software/hardware-in-the-loop testing.

Used in 1 use case

NVIDIA Jetson Thor is a Blackwell-powered edge AI and robotics compute module delivering up to 2070 FP4 TFLOPS of AI compute and 128 GB of memory — 7.5x the performance and 3.5x the energy efficiency of NVIDIA AGX Orin — at 40-130W. It is built for humanoid robotics, agentic AI, high-speed sensor processing, and other physical AI workloads at the edge, powered by the NVIDIA Isaac platform and GR00T foundation models.

NVIDIA Blackwell is the GPU architecture powering AI factories for the age of AI reasoning, packing 208 billion transistors with a second-generation Transformer Engine to accelerate inference and training for large language models and Mixture-of-Experts models.

Used in 4 use cases

NVIDIA Cosmos is a world foundation model (WFM) platform for physical AI, providing open data processing, training and evaluation frameworks. Cosmos 3, its latest omni-model, generates across text, image, video, sound and action to power vision AI reasoning, robot policy learning, world simulation and synthetic video data generation for robotics, autonomous vehicles and industrial vision systems.

Used in 2 use cases

The NVIDIA DGX platform, featuring NVIDIA DGX SuperPOD, combines NVIDIA software, infrastructure and expertise in a unified AI development solution for building enterprise AI factories, used by 8 of the top 10 global telcos, 7 of the top 10 global pharmas, and 10 of the top 10 global car manufacturers and universities.

Used in 3 use cases

NVIDIA DGX B200 is a unified AI platform for develop-to-deploy pipelines for businesses of any size at any stage of their AI journey. Equipped with eight NVIDIA Blackwell GPUs interconnected with fifth-generation NVLink, it delivers 3X the training performance and 15X the inference performance of the previous-generation DGX H100, and can handle diverse workloads including large language models, recommender systems, and chatbots. DGX B200 is the foundation of NVIDIA DGX BasePOD and NVIDIA DGX SuperPOD, and ships with the full NVIDIA AI software stack including NVIDIA Mission Control and NVIDIA AI Enterprise software.

Used in 3 use cases

NVIDIA DGX Cloud is NVIDIA's fully managed AI training platform, offered on AWS, Google Cloud, Microsoft Azure and Oracle Cloud Infrastructure, providing co-engineered NVIDIA accelerated computing clusters with flexible term lengths and access to NVIDIA experts. It is also NVIDIA's own internal AI factory, used to develop open-source frontier and foundational models and validate new system architectures.

Used in 5 use cases

NVIDIA DGX SuperPOD is a full-stack, turnkey AI data center platform combining leadership-class computing, storage, networking, software and infrastructure management, purpose-built for the most challenging AI training and inference workloads. Available with NVIDIA Rubin and Blackwell-powered compute options, it scales to tens of thousands of GPUs to tackle training and inference for trillion-parameter generative AI models, with customers including BNY, MITRE, SoftBank Corp, University of Florida, and Naver Corporation.

Used in 5 use cases

NVIDIA DRIVE AGX provides the AI computing power for safe, intelligent autonomous driving. DRIVE AGX Thor delivers more than 1,000 INT8 TOPS (2,000 FP4 TFLOPs) with a scalable architecture supporting Level 2+ to fully autonomous driving and ASIL-D compliance with redundancy; DRIVE AGX Orin delivers up to 254 TOPS of AI performance with the same scalable, safety-certified architecture. NVIDIA DRIVE AV delivers the end-to-end autonomous driving software stack that runs on DRIVE AGX.

Used in 1 use case

NVIDIA DRIVE AV is the autonomous-vehicle software stack that runs on NVIDIA DRIVE AGX (Thor and Orin) in real time on the safety-certified NVIDIA DriveOS, as part of NVIDIA's end-to-end, full-stack autonomous vehicle platform spanning model training (DGX), simulation and validation (Omniverse and Cosmos), and in-vehicle computing, with NVIDIA Halos enforcing safety at every stage.

Used in 1 use case

NVIDIA DRIVE Hyperion features two NVIDIA DRIVE AGX Thor systems-on-a-chip on a single board, the safety-certified NVIDIA DriveOS operating system, and a fully qualified multimodal sensor suite including 14 high-definition cameras, nine radars, one lidar and 12 ultrasonics, delivering the compute performance and redundancy required for robotaxis and L4 autonomous driving. It includes NVIDIA DRIVE AV software, purpose-built for L4 autonomy, and global automakers and mobility leaders are bringing level 4-ready fleets to market on DRIVE Hyperion.

Used in 1 use case

NVIDIA Earth-2 is a comprehensive family of open models, libraries and frameworks that democratize global access to professional-grade weather and climate AI, providing a fully open software stack that accelerates every stage of forecasting from data processing to high-resolution visualization. The model family includes Earth-2 Medium Range (predictions for over 70 weather variables up to 15 days ahead), Earth-2 Nowcasting (zero- to six-hour hazardous weather forecasts), Earth-2 Global Data Assimilation, Earth-2 CorrDiff (generative AI downscaling), and Earth-2 FourCastNet 3, letting researchers, startups and government agencies run, fine-tune and deploy forecasting systems on their own infrastructure.

Used in 3 use cases

NVIDIA Halos is a full-stack, comprehensive safety system that unifies safety elements across vehicle architecture, AI models, chips, software, tools and services to ensure the safe development and deployment of robotaxis and autonomous vehicles, from cloud to car. It covers the full development lifecycle with design-time, deployment-time and validation-time guardrails, implemented using NVIDIA DGX for model training, NVIDIA Omniverse and Cosmos for simulation, and NVIDIA DRIVE AGX for deployment, and represents more than 18,600 engineering years invested in vehicle safety to date.

Used in 1 use case

NVIDIA Isaac is an AI robotics platform for robot simulation, training and deployment, used to create autonomous mobile robots (AMRs), robot arms and manipulators, and humanoids. It includes Isaac ROS, a CUDA-accelerated ROS 2 stack for perception and mobility; Isaac Lab, for GPU-accelerated robot learning on Isaac Sim; Isaac Sim, a robotics simulation and synthetic data engine; and Isaac GR00T, foundation models and tools for humanoid robots.

Used in 1 use case

NVIDIA Isaac Sim™ is an open source reference framework built on NVIDIA Omniverse™ libraries for robotics simulation, testing, and synthetic data generation in physically based virtual environments. It is fully extensible so developers can build custom OpenUSD-based simulators or integrate its capabilities into existing testing and validation pipelines — ingesting data from CAD, URDF or real-world captures, simulating realistic physics (rigid body and vehicle dynamics, multi-joint articulation, SDF colliders), generating scalable synthetic training data, and connecting to NVIDIA Isaac Lab for robot learning.

Used in 7 use cases

NVIDIA Jetson Orin is a family of seven embedded AI computer modules sharing an identical architecture, offering up to 275 trillion operations per second (TOPS) and 8X the performance of the last generation for multimodal AI inference, plus high-speed interface support. Built to bring generative AI, agentic AI, computer vision and advanced robotics to next-gen products, its software stack features pre-trained AI models, reference AI workflows and a vertical application framework, accelerating end-to-end development for generative AI as well as edge AI, robotics or space computing applications.

Used in 2 use cases

NVIDIA NeMo is an agent-first, open suite of libraries with skills for accelerating AI agent specialization, optimization, and governance. NeMo integrates with existing AI tools and agent frameworks to optimize specialized agents across any cloud, on-premises, or hybrid environment.

Used in 5 use cases

NeMo Curator is a data curation library for text, image, video and audio. It supports curating and preparing high-quality text datasets for LLM training (filtering, formatting, deduplication), curating image-text datasets with embedding, classification and deduplication, processing videos with GPU-accelerated pipelines and sharding, and transcribing, filtering and curating speech and audio datasets with ASR models.

Used in 2 use cases

The NVIDIA NeMo Guardrails library is an open-source Python package for adding programmable guardrails to LLM-based applications. Use it to block, alter, or validate unsafe, off-topic, malicious, or policy-violating user inputs and model responses. The library provides configuration files, Colang flows, built-in guardrails, custom actions, and integration APIs so you can add safety and control logic without rewriting your application or model backend.

Used in 4 use cases

NVIDIA NeMo Retriever is a collection of microservices that present a single API for indexing and querying of user data. NeMo Retriever uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, and images that you can use in downstream generative applications. It is a collection of microservices for building and scaling multimodal data extraction, embedding, and reranking pipelines with high accuracy and maximum data privacy, built with NVIDIA NIM.

Used in 4 use cases

NVIDIA Nemotron is a family of highly efficient, multimodal, open AI models built for long-running, self-evolving agents, designed for fast task completion with high reasoning throughput and leading accuracy for complex agent workflows. NVIDIA publishes the training datasets, techniques and model weights openly, and the family includes models for reasoning (Nano, Super and Ultra sizes), visual understanding, speech, retrieval-augmented generation (RAG), and safety, available as optimized NVIDIA NIM microservices for enterprise deployment.

Used in 4 use cases

NVIDIA NIM provides prebuilt, optimized inference microservices for rapidly deploying the latest AI models on any NVIDIA-accelerated infrastructure, including cloud, data center, workstation, and edge. NIM microservices come prepackaged with the latest AI foundation models, optimized inference engines, industry-standard APIs, and runtime dependencies in enterprise-grade software containers, and can be deployed with a single command and integrated with just a few lines of code.

Used in 9 use cases

NVIDIA Omniverse provides libraries, APIs, services and agent-ready tools for building physical AI applications and simulation-ready 3D worlds, integrating OpenUSD data interoperability, RTX rendering, physics, sensor simulation and validation into applications for industrial digital twins, robot simulation, robot learning, synthetic data generation and autonomous vehicle simulation.

Used in 16 use cases

NVIDIA Parabricks is a scalable genomics software suite for secondary analysis that provides GPU-accelerated versions of trusted, open-source tools, compatible with leading sequencing instruments. It increases whole-genome sequencing (WGS) analysis speed by more than 100x compared to CPU-only solutions, reducing germline analysis from about 16 hours to under 10 minutes on 4 NVIDIA RTX PRO 6000 Server Edition GPUs, and lowers WGS compute cost by up to 50%. It uses accelerated deep learning tools including Google's DeepVariant and DeepSomatic to improve accuracy for germline and somatic variants, and is freely available as a public container on NGC for use on-premises or on any cloud platform.

Used in 3 use cases

The NVIDIA RTX PRO 6000 Blackwell Series is a professional GPU family powered by the NVIDIA Blackwell architecture, equipped with 96GB of ultra-fast GDDR7 memory, fifth-generation Tensor Cores and fourth-generation RT Cores. It accelerates AI and creative workloads from agentic AI, physical AI and scientific computing to photorealistic rendering, 3D graphics and real-time video processing, and is offered in Server, Workstation and Max-Q Workstation editions.

Used in 1 use case

NVIDIA TensorRT is an ecosystem of tools for high-performance deep learning inference, including the TensorRT compiler, TensorRT-LLM, TensorRT Model Optimizer, TensorRT for RTX and TensorRT Cloud, delivering low latency and high throughput for production applications by using quantization, layer and tensor fusion, and kernel tuning.

Used in 3 use cases

NVIDIA Dynamo-Triton (formerly NVIDIA Triton Inference Server) is open-source software that enables deployment of AI models across major frameworks including TensorRT, PyTorch, ONNX, OpenVINO, Python and RAPIDS FIL, with dynamic batching and concurrent execution, supporting real-time, batched, ensemble and audio/video streaming workloads on NVIDIA GPUs, non-NVIDIA accelerators, x86 and ARM CPUs.

Used in 2 use cases

Aurora is a cutting-edge AI foundation model developed by a team of Microsoft researchers that can extract valuable insights from vast amounts of atmospheric data. This 1.3 billion parameter model excels at a wide range of prediction tasks, even in data-sparse regions or extreme weather scenarios. It is trained in two phases: a pre-training phase that learns general-purpose representations of weather and climate from a vast and diverse set of data, and a fine-tuning phase that adapts it to specific tasks like 10-day global weather forecasting or 5-day air pollution prediction, producing high-resolution global forecasts much faster than traditional numerical weather models while matching or exceeding their accuracy. Aurora 1.5, developed by Microsoft Weather as an extension of the original model by Microsoft Research AI for Science, adds 22 new forecast variables (including cloud cover, precipitation and radiation), hourly resolution, and probabilistic ensemble forecasting for uncertainty quantification; its ensemble forecasts outperform the ECMWF ensemble on 88.9% of evaluated targets (days 1-10) and cut tropical-cyclone track error by 16% versus the original Aurora. Aurora 1.5 is available on GitHub and Microsoft Foundry.

Used in 1 use case

Microsoft Azure is Microsoft's cloud computing platform, positioned as "the power of a connected AI system" that unifies AI, data, business context, apps and agents to run an intelligent system across the enterprise. Azure offers more than 200 products spanning AI and machine learning (including Microsoft Foundry, Foundry Models, Foundry Agent Service, Azure Machine Learning), databases and analytics (Azure Cosmos DB, Azure SQL, Microsoft Fabric, Azure Databricks), compute (virtual machines, Azure Functions), containers (Azure Kubernetes Service, Azure Container Apps), and hybrid and multicloud infrastructure (Azure Arc, Azure Local), with global infrastructure spanning more regions than any other cloud provider.

Azure Document Intelligence (now part of Azure Content Understanding in Foundry Tools) is a Foundry Tool that applies advanced AI models to extract text, key-value pairs, tables, and structures from documents automatically and accurately, providing high-accuracy and reliable extraction of structured and templated documents. It offers simple text extraction via prebuilt and custom models without manual labeling, customized results through automatic custom extraction improved with human feedback, and flexible deployment to ingest data from the cloud or at the edge -- including support for Azure Kubernetes Service and Azure Container Instances -- for use in search indexes and business automation workflows. It supports printed and handwritten forms, PDFs and images, in multiple languages including English, French, German, Italian, Spanish, Portuguese, Dutch, Chinese, Japanese and Korean.

Used in 1 use case

Azure Language (formerly Azure AI Language, now rebranded Azure Language in Foundry Tools) offers advanced natural language processing capabilities and customizable AI models, including personal data redaction, named entity extraction, summarization, conversational language understanding (CLU) for intent classification, custom question answering, and text analytics for health, using state-of-the-art transformer models with both large and small language models. It supports building small or large language models to analyze text, identify intents, answer questions and extract entities for a custom domain, training a model in one language and using it for multiple other languages. It can be deployed in the cloud or at the edge with containers, and works with Azure OpenAI and Azure Translator to build multilingual assistants and chatbots. Pricing is pay-as-you-go, based on the number of text records consumed for inference, training hours for custom models, and model hosting cost for custom models.

Used in 1 use case

Azure AI Search (now also branded Foundry IQ) is an enterprise knowledge system that powers retrieval-augmented generation (RAG) applications and enterprise search engines. It combines vector search, keyword search, and hybrid search (merged via Reciprocal Rank Fusion) with agentic retrieval to fuel AI agents with unified enterprise context, connecting to knowledge sources such as SharePoint, OneLake, Azure Blob Storage and the web. It is used by developers at more than 80,000 enterprises, including 80% of Fortune 500 companies, processes 3 billion daily enterprise search queries, and is available in more than 30 countries and regions.

Used in 2 use cases

Azure API Management is a fully managed, Azure-native platform used to secure, govern and scale traditional and AI APIs throughout their lifecycle, offering an AI gateway for models, MCP servers and agents with token quota enforcement, semantic caching and content safety, plus a central catalog for discovery and governance across hybrid and multicloud environments; it is trusted by over 35,000 customers worldwide, manages 2 million APIs, and processes 3 trillion requests monthly across more than 60 regions.

Used in 1 use case

Azure Arc is a bridge that extends the Azure platform to help you build applications and services with the flexibility to run across datacenters, at the edge, and in multicloud environments. It provides a centralized, unified way to manage your entire environment by projecting your existing on-premises, edge, and multicloud resources into Azure Resource Manager so you have a single control plane to manage, govern, and secure your VMs, databases, Kubernetes clusters, and other cloud resources.

Used in 1 use case

Azure Cosmos DB is a fully managed, cloud-native NoSQL database designed to deliver ultra-low latency and elastic scalability at global scale, using a schema-agnostic JSON document model with built-in vector and hybrid search for AI-driven experiences like RAG and agentic apps, backed by SLAs including less than 10 millisecond write-and-read latency and 99.999 percent availability.

Used in 3 use cases

Azure Document Intelligence (now part of Azure Content Understanding in Foundry Tools) is a Foundry Tool that applies advanced AI models to automatically and accurately extract text, key-value pairs, tables and structure from documents such as PDFs, images and forms, using optical character recognition (OCR) and prebuilt or custom models — without manual labeling by document type, intensive coding or maintenance. It offers a simple REST API for extracting information from forms, receipts, invoices and cards, custom extraction capabilities that train on just a few of an organization's own documents, and flexible deployment in the cloud, on-premises or at the edge via container support (Azure Kubernetes Service, Azure Container Instances, or Azure Stack). Prebuilt features include analyzing forms and documents for data-driven decisions, creating intelligent search indexes, and automating business workflows such as claim, invoice and receipt processing.

Used in 1 use case

Azure Local extends Azure to customer-owned infrastructure, enabling organizations to run modern and traditional workloads -- including virtual machines, containers and select Azure services such as Azure Virtual Desktop and Azure IoT Operations -- on validated hardware in their own datacenters or edge locations. It provides a unified management experience through the Azure portal and familiar tools like PowerShell, supports hybrid capabilities via Azure Arc, and offers disconnected operations to meet data residency, sovereignty and low-latency requirements. It supports deployments from a single node up to thousands of nodes.

Used in 1 use case

Azure Machine Learning is an enterprise-grade AI service for the end-to-end machine learning (ML) lifecycle, used to build business-critical ML models at scale. It supports prompt engineering and ML model workflows, automated machine learning for classification, regression, vision and natural language processing tasks, MLOps for reproducing end-to-end pipelines with CI/CD, and a model catalog for discovering, fine-tuning and deploying foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere and others.

Used in 5 use cases

Azure OpenAI in Foundry Models gives instant access to cutting-edge foundational and reasoning models from OpenAI, optimized for complex problem-solving, logical reasoning and multimodal capabilities including real-time audio. It enables integrated AI agents to automate complex business workflows, offers advanced model customization through fine-tuning, and provides trustworthy AI with built-in security frameworks and content moderation tooling. Azure OpenAI offers Standard, Provisioned and Batch deployment types, and guarantees at least 99.9% availability under its SLA.

Used in 5 use cases

Azure Red Hat OpenShift is a fully managed OpenShift service on Microsoft Azure, co-managed by Microsoft and Red Hat, that lets teams run virtual machines and containers together on a unified cloud-native platform with a 99.95% availability SLA, automated cluster deployment and management by specialized site reliability engineers, and Confidential Containers now generally available to safeguard data-in-use for sensitive workloads; it is available across more than 60 Azure regions worldwide.

Used in 1 use case

Azure Synapse Analytics accelerates time to insight across enterprise data warehouses and big data systems, using a cloud-native, decoupled compute-and-storage architecture that scales independently and supports open formats, unifying SQL, Spark, data integration and business intelligence in a single workspace so data engineers, database administrators, data scientists and business analysts can use the same analytic service.

Used in 1 use case

Microsoft Copilot Studio is a platform for creating, customizing, and launching AI agents using natural language or a graphical interface, connecting agents to business data and publishing them across channels such as Microsoft Teams, SharePoint, Microsoft 365 Copilot, websites, and social channels. It supports conversational, autonomous, and multi-agent systems, with governance and analytics through the Power Platform admin center. 90% of the Fortune 500 use Copilot Studio.

Used in 2 use cases

Microsoft 365 is a cloud-based productivity and collaboration platform combining apps such as Word, Excel, PowerPoint, Outlook, OneDrive, SharePoint and Teams, with AI-powered Copilot features, offered in plans for individuals, businesses, enterprises and education.

Used in 4 use cases

Microsoft 365 Copilot is AI built for work, turning data into insights in the apps users already know. It is powered by Work IQ, a workplace intelligence layer that connects data, context and tools to deliver intelligence tailored to a user's job and company. Capabilities include Chat and Cowork (securely handing off complex tasks and keeping multiple projects moving), AI-powered enterprise Search, ready-to-use and custom-built Agents (via Copilot Studio), and Notebooks that bring together chats, files, meeting notes and project materials. Copilot inherits a user's Microsoft 365 permissions, sensitivity labels, and retention policies, and prompts, inputs, and responses are never used to train the models.

Used in 11 use cases

Microsoft 365 E5 is an enterprise plan that provides unified endpoint, identity, and access management; productivity apps including Word, Excel, PowerPoint, Outlook, Microsoft Teams, and Windows for Enterprise; advanced security and analytics (including Microsoft Defender for Endpoint, Defender for Office 365, Defender for Identity, Defender for Cloud Apps, and Purview data protection/compliance tools); cloud storage; and web-connected AI chat through Microsoft 365 Copilot Chat. It supports organizations of any size and includes Microsoft Entra ID (P2) for advanced identity and access management and Intune for device management.

Used in 1 use case

Azure is Microsoft's intelligent cloud platform, unifying AI, data, business context, apps and agents to run an intelligent system across the enterprise, with global infrastructure spanning more regions than any other cloud provider.

Used in 10 use cases

Microsoft Foundry (formerly Azure AI Studio) is a unified enterprise AI platform to build, ground and govern AI apps and agents at scale, offering access to over 11,000 foundational, open, reasoning and multimodal models, an Agent Service for building and deploying agents, and a Control Plane for governance, security and observability across the full agent lifecycle.

Microsoft Fabric is a data platform for the AI era that unifies teams and data to accelerate AI innovation, combining data integration, data engineering, data science, real-time intelligence, data warehousing and business intelligence (Power BI) with Microsoft OneLake, a multi-cloud data lake that lets teams work from a single copy of data across different analytics engines.

Used in 5 use cases

Microsoft Foundry is a unified enterprise AI platform to build, ground and govern AI apps and agents at scale, bringing together the full agent lifecycle with open development, built-in intelligence, and consistent security, compliance and policy controls. It gives access to more than 11,000 foundational, open, reasoning and multimodal models spanning OpenAI, Anthropic, Meta, Google, xAI, Hugging Face and Microsoft's own MAI multimodal family, plus Foundry Agent Service for building and orchestrating agents, Foundry IQ for grounding agents in organizational data, and Foundry Control Plane for governance and observability. Foundry is used by developers at more than 80,000 enterprises and digital natives, including 80% of Fortune 500 companies.

Used in 4 use cases

Microsoft Planetary Computer Pro is a fully managed, turnkey geospatial data platform that unifies geospatial data with enterprise AI and GeoAI analytics, helping ingest, catalog, store, process and disseminate an organization's own private geospatial data at scale to deliver actionable insights. It standardizes an organization's geospatial data estate with a unified SpatioTemporal Asset Catalog (STAC) for search and discovery, provides intuitive APIs and fully managed data storage, rich interactive visualization, integration with GIS tools like ArcGIS, and automatic cloud optimization for downstream analytics and AI. It differs from the public Microsoft Planetary Computer (which hosts more than 140 public geospatial datasets) by focusing on ingesting and managing an organization's own private data, and it serves as a data platform for AI models in Microsoft Foundry, including a reference architecture for integration with Aurora, the large-scale foundation model of the Earth atmosphere. Example use cases include emergency management and disaster response, environmental protection, weather monitoring and forecasting, power grid optimization, vegetation encroachment management, risk modeling, precision farming, and intelligence and surveillance.

Used in 1 use case

Power Automate is Microsoft's end-to-end business process and workflow automation solution. It combines robotic process automation (RPA) for desktop flows and AI-powered digital process automation (DPA) for cloud flows, with task and process mining to uncover optimization and automation opportunities, and orchestration with built-in security, governance and 360-degree monitoring. It offers more than 1,000 API connectors, Copilot-assisted flow authoring in natural language, and native integration with Microsoft 365 apps such as Teams, Excel and SharePoint.

Used in 1 use case

Microsoft Power Platform is an AI platform for building and scaling intelligent apps, agents, and automation with enterprise-grade control, security, and trust. It brings together Power Apps, Power Automate, Power BI, Power Pages, Microsoft Dataverse and Copilot Studio, connected through more than 1,500 connectors across the services and systems businesses already run. Customer results include Hertz achieving 15% faster roadside issue resolution, Evri achieving £9 million in annualized cost savings and a 102% increase in successful answer rate, HEINEKEN gaining 3.1M hours in increased productivity with 10,000+ applications built using Power Apps, T-Mobile saving 97,000 hours and $4M annually, and British Heart Foundation processing 800,000 items weekly with £1M projected added revenue.

Used in 2 use cases

Amazon Bedrock is the platform for building generative AI applications and agents at production scale, powering generative AI for more than 100,000 organizations worldwide. It provides model choice across hundreds of foundation models, Bedrock AgentCore for building and deploying agents, data customization (Knowledge Bases, Bedrock Data Automation, fine-tuning), and Bedrock Guardrails for safety and compliance (ISO, SOC, CSA STAR Level 2, GDPR, FedRAMP High, HIPAA eligible).

Used in 30 use cases

Amazon Bedrock Agents enables generative AI applications to automate multistep tasks by seamlessly connecting with company systems, APIs and data sources. It uses the reasoning of foundation models to break down user requests, gather relevant information, and complete tasks, with features including multi-agent collaboration, retrieval augmented generation, memory retention and code interpretation. Amazon Bedrock Agents Classic is no longer available to new customers; AWS points new customers to Amazon Bedrock AgentCore for similar capabilities.

Used in 1 use case

Amazon EC2 G5 instances are NVIDIA GPU-based EC2 instances built on the AWS Nitro System for graphics-intensive applications and machine learning inference and training, featuring up to 8 NVIDIA A10G Tensor Core GPUs, up to 192 vCPUs, up to 100 Gbps network bandwidth, and up to 7.6 TB of local NVMe SSD storage.

Used in 1 use case

Amazon Elastic Kubernetes Service (Amazon EKS) enables teams to build, run, and scale production-ready Kubernetes applications across any environment (cloud, on-premises, or edge), with EKS Auto Mode to automate cluster management for compute, storage, and networking. Use cases include deploying generative AI applications, distributed training/inference, internal development platforms, and data platforms.

Used in 1 use case

Amazon EMR Serverless is a serverless option in Amazon EMR that lets data analysts and engineers run open-source big data analytics frameworks, such as Apache Spark and Apache Hive, without configuring, managing or scaling clusters or servers. It automatically provisions and scales compute and memory resources on demand, and customers pay only for what they use. It eliminates local storage provisioning for Apache Spark workloads, reducing data processing costs by up to 20% and preventing job failures from disk capacity constraints.

Used in 1 use case

Amazon Personalize is a fully managed AI-powered recommendation service that helps deliver hyper-personalized user experiences in real time at scale. It trains models that learn from billions of user interactions with millions of items, adapts recommendations dynamically as customer behavior changes, and integrates with Amazon Bedrock to improve customer segmentation and generate more relevant content.

Used in 1 use case

Amazon Q is a generative AI assistant that transforms how work gets done in an organization, with specialized capabilities for software developers, business intelligence analysts, contact center employees, supply chain analysts, and anyone building with AWS. Amazon Q Business makes generative AI securely accessible to everyone in an organization by connecting to a company's own content, data and systems to answer questions, solve problems, generate content and take actions. Amazon Q Developer is a generative AI-powered assistant for building, operating and transforming software, helping with coding, testing, deploying, troubleshooting, security scanning and fixes, application modernization, resource optimization and data engineering pipelines. Amazon Q is also built into Amazon QuickSight, Amazon Connect and AWS Supply Chain to bring generative AI assistance to business intelligence, customer service and supply chain management. Amazon Q is built on Amazon Bedrock and is designed to respect existing identities, roles and permissions so it cannot surface data a user isn't otherwise permitted to access.

Used in 1 use case

Amazon Q Business is a generative AI-powered assistant for finding information, gaining insight, and taking action at work. It provides a unified search experience across an organization's documents, images, audio and video files, and structured data sources, letting users request information or assistance, generate content, or create lightweight apps that automate workflows using natural language. Note: as stated on the vendor's page, Amazon Q Business is no longer available to new customers, having been succeeded by Amazon Quick.

Used in 1 use case

Amazon QuickSight delivers AI-powered business intelligence within Amazon Quick, unifying enterprise data sources, embedding analytics into applications, and enabling conversational, agentic data analysis with built-in role-based access control and support for FedRAMP, HIPAA, PCI DSS, ISO and SOC compliance.

Used in 1 use case

Amazon RDS (Relational Database Service) is an easy-to-manage relational database service optimized for total cost of ownership, automating provisioning, configuring, backing up and patching. It offers flexibility to customize databases across eight engines and two deployment options, and customers can get started with familiar open source and commercial database software including PostgreSQL, MySQL, MariaDB, SQL Server, Oracle and Db2. Amazon Aurora, offered as a separate product, provides MySQL- and PostgreSQL-compatible performance and availability at global scale at a fraction of the cost of commercial databases.

Used in 2 use cases

Amazon Redshift is a cloud data warehouse that delivers price-performance for analytics and agentic AI workloads. It unifies access across Redshift data warehouses, S3 data lakes, and third-party sources via the lakehouse in Amazon SageMaker, offers zero-ETL integrations for near real-time analytics, and integrates with Amazon Bedrock so organizations can use their data for generative AI output.

Used in 1 use case

Amazon S3 (Simple Storage Service) is an object storage service offering industry-leading scalability, data availability, security and performance, designed for 99.999999999% durability, used as the data foundation for data lakes, AI training and generative AI applications, with S3 Tables, S3 Express One Zone and S3 Vectors for AI and analytics workloads.

Used in 7 use cases

The next generation of Amazon SageMaker is an integrated platform for analytics and AI, bringing together SageMaker AI (model development, training and deployment), SageMaker Unified Studio, SageMaker Catalog (built on Amazon DataZone) and a lakehouse architecture that unifies Amazon S3 data lakes and Amazon Redshift data warehouses with governance built in.

Used in 9 use cases

Amazon Textract is a machine learning (ML) service that automatically extracts printed text, handwriting, layout elements and data from scanned documents, going beyond simple optical character recognition (OCR) to identify, understand and extract specific data from documents such as PDFs, images, tables and forms. It offers pretrained and customizable features to automate document processing for use cases such as loan and mortgage processing, invoices and receipts, and healthcare intake forms, extracting data in minutes instead of hours or days.

Used in 4 use cases

Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation, letting users localize content for diverse global users and translate and analyze large volumes of text to enable cross-lingual communication. It offers high quality, continually improving translations, batch and real-time translation via a single API call, and customization to define brand names, model names and other unique terms. Use cases include translating high volumes of user-generated content such as social media feed stories, profile descriptions and comments in real time; analyzing sentiment toward a brand, product or service across multiple languages; and adding real-time translation within chat, email, helpdesk and ticketing applications so an English-speaking agent can communicate with customers across multiple languages.

Used in 1 use case

AWS Config is a service that enables customers to assess, audit, and evaluate the configurations of their AWS resources, continually recording configuration changes to simplify change management, compliance auditing and operational troubleshooting.

Used in 1 use case

Amazon DataZone is a data management service that catalogs, discovers, shares and governs data stored across AWS, on-premises and third-party sources, letting administrators manage fine-grained access controls and enabling engineers, data scientists and business users to collaborate and derive data-driven insights.

Used in 1 use case

AWS Glue is a serverless data integration service that discovers, prepares and integrates data at scale, connecting to more than 100 data sources with a centralized data catalog and built-in generative AI capabilities for ETL authoring and Spark troubleshooting.

Used in 2 use cases

AWS Inferentia chips are designed by AWS to deliver high performance at the lowest cost in Amazon EC2 for deep learning and generative AI inference applications. The first-generation AWS Inferentia chip powers Amazon EC2 Inf1 instances, which deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable Amazon EC2 instances. AWS Inferentia2 delivers up to 4x higher throughput and up to 10x lower latency compared to Inferentia, and Inferentia2-based Amazon EC2 Inf2 instances are optimized to deploy large language models and latent diffusion models at scale, and are the first inference-optimized instances in Amazon EC2 to support scale-out distributed inference with ultra-high-speed connectivity between chips. The AWS Neuron SDK integrates natively with popular frameworks such as PyTorch and TensorFlow to deploy models on Inferentia chips (and train them on AWS Trainium chips) with minimal code changes.

Used in 1 use case

AWS offers Internet of Things (IoT) services and solutions to connect and manage billions of devices. Collect, store, and analyze IoT data for industrial, consumer, commercial, and automotive workloads.

Used in 1 use case

AWS Key Management Service (KMS) lets customers create and control cryptographic keys used to encrypt data, digitally sign data, and generate and verify message authentication codes (MACs) across integrated AWS services and applications from a single, centrally managed point.

Used in 1 use case

AWS Lambda is serverless compute for every workload, letting you run code at any scale with zero infrastructure management, with 220+ native AWS integrations and pay-per-use billing.

Used in 8 use cases

AWS Transform is a collaborative enterprise IT transformation workbench powered by expert agents that accelerates cloud migration, application modernization, and continuous tech debt reduction. It automates infrastructure migration for workloads across VMware, bare metal, and hybrid environments, modernizes applications including mainframe, Windows, and custom code transformations, and continuously analyzes and remediates tech debt across an entire portfolio.

Used in 2 use cases

Agent Bricks is Databricks' agent platform for building production-ready AI agents on enterprise data, providing multi-model routing (routing requests across models like GPT, Claude and Llama by cost, performance or availability through a single API), built-in evals (generating eval datasets and scoring outputs for quality, relevance and correctness), and secure data access (connecting agents to governed data with user-level permissions and no data duplication). Customer Flo Health reports Agent Bricks enabled it to double medical accuracy over standard commercial LLMs while meeting its internal standards for clinical accuracy, safety, privacy and security.

Used in 6 use cases

Agent Bricks is a Databricks platform to build and govern production AI agents that continuously improve, unifying development, management and governance of enterprise AI agents. It uses enterprise context — schemas, business definitions and custom semantics — to decide which tools and tables to use, join data correctly and produce accurate, consistent answers. It provides access to AI models from OpenAI, Anthropic, Google and open source through a single platform, letting users switch models instantly to optimize cost, quality and performance without re-architecting their stack. Databricks governs the full stack — from data to AI models — in a single system of record, tracking every agent, MCP server, model and tool with clear ownership and end-to-end permissions. Agents can be built with preferred IDEs and frameworks such as LangChain, LangGraph or LlamaIndex, or with no-code managed agent builders including Supervisor Agent and Knowledge Assistant, and validated in AI Playground before going live. Agents connect directly to the lakehouse for AI-ready enterprise data, with built-in context, retrieval and memory via Lakebase, and natively support the Model Context Protocol (MCP) for secure, governed access to APIs, databases and SaaS applications. Agents deploy to serverless compute via Databricks Apps with automatic scaling, and quality is continuously evaluated using LLM-as-judge, custom metrics and human feedback loops.

Used in 2 use cases

Databricks Genie Agents (part of Databricks AI/BI) give business and nontechnical users a natural-language interface to talk with their data: users ask questions in plain language and get immediate answers without needing BI tools or SQL. Genie Agents use agentic reasoning that adapts to evolving business concepts, and ask for clarification when unsure how to answer. Subject matter experts can add curated instructions, example queries and predefined functions so users receive trusted, verified answers, and integration with Unity Catalog ensures answers are governed, secure and auditable. Activity monitoring tracks every question asked and how answers are rated to improve quality over time.

Used in 3 use cases

Databricks Apps is the fastest and most secure way to build data and AI applications on the Databricks Data + AI Platform. Developers can create applications using popular frameworks such as Dash, Gradio and Streamlit, running on automatically provisioned serverless compute with granular access controls, automatically managed service principals, and automatic user authentication via OIDC/OAuth 2.0 and SSO, plus Unity Catalog integration for data governance.

Used in 4 use cases

Lakebase is a new database architecture that separates compute from storage, keeping data in a low-cost, open format cloud object storage while a serverless Postgres engine runs elastically on top. It is a fully managed, serverless Postgres integrated with the Databricks Data + AI Platform, designed to power AI and real-time OLTP workloads, eliminating complex, custom ETL pipelines and ensuring transactional data is integrated into analytics and AI-driven applications.

Used in 1 use case

Databricks Model Serving provides unified deployment and governance for classical ML models, generative AI models and AI agents, supporting proprietary models (Azure OpenAI, AWS Bedrock, Anthropic) and open-source models (Llama, Mistral) as low-latency, serverless API endpoints with built-in governance, lineage and monitoring.

Used in 3 use cases

Unity Catalog is Databricks' unified governance solution for data, analytics and AI assets, including agents, AI apps, MCPs and models. It provides context-rich discovery, AI-powered governance controls, fine-grained access at scale, automated column-level lineage, and agentic quality monitoring across clouds and open formats such as Delta, Iceberg and Parquet.

Used in 3 use cases

Delta Lake is Databricks' open table format providing ACID transactions, unified governance and AI-driven performance optimizations (predictive optimization, liquid clustering) for lakehouse storage. A single copy of source data in Delta Lake or Apache Iceberg can be accessed by any engine, keeping data portable, without vendor lock-in.

Used in 15 use cases

Apache Spark Declarative Pipelines (part of Databricks Lakeflow) simplifies batch and streaming ETL: engineers declare the data transformations they need while the platform automatically handles ingestion from any Apache Spark-supported source, dependency management, scaling and recovery, and data quality rules via Expectations. It offers unified batch and streaming processing, end-to-end incremental processing, an integrated pipeline development IDE, and is built on Unity Catalog and open table formats.

Used in 2 use cases

Unity Catalog is Databricks' unified governance product for data, apps and AI agents, providing fine-grained access control, automated lineage, AI-powered curation and agentic quality monitoring so that data, models, agents and MCPs are discoverable, governed and secure across clouds and platforms.

Used in 23 use cases

SAP Business AI Platform combines deep process context, unified data, and purpose-built models with enterprise governance, letting organizations build and integrate AI that understands their business, not just their prompts. Joule brings assistants and agents together in a unified workspace that turns intent into autonomous action, running end-to-end workflows across SAP and non-SAP systems. With Joule Work, people express what they want to accomplish, and Joule surfaces the right insights, automates routine work, and coordinates AI agents across systems to achieve it. Joule Agents and Joule Assistants are grounded in SAP's harmonized data, business process context and trusted architecture, delivering reliable performance without compromising on compliance, security or scale. Turning insights into action, organizations run workflows with Joule Agents and Joule Assistants, and build and scale AI solutions that fit their business with Joule. The platform is built for security, compliance, and control, with tenant-level data isolation and GDPR-compliant data processing.

SAP Business Data Cloud unifies and governs SAP and third-party data with a business data fabric, providing a trusted data foundation for agentic AI. It brings together curated, semantically rich data products built on SAP's business process definitions, and includes SAP Analytics Cloud, SAP Datasphere, SAP Business Warehouse, SAP Databricks, SAP HANA Cloud and SAP Master Data Governance capabilities.

Used in 1 use case

SAP Business Technology Platform (SAP BTP) is a platform engineered to integrate, automate, extend, and build AI-supported business applications and processes across the enterprise. It unifies application development, data, and AI capabilities, connecting systems and processes, integrating data via SAP Business Data Cloud, and accelerating development with AI coding grounded in business context and AI agents that automate business processes. SAP BTP capabilities are now a core part of SAP Business AI Platform, and more than 33,000 customers use SAP BTP.

Used in 2 use cases

SAP HANA Cloud is a multi-model AI database that processes relational, graph, vector, spatial, JSON, and time-series data in a single in-memory engine, grounding AI agents and applications in trusted, real-time business context. It runs native AI and semantic processing, including vector search, knowledge graph queries and machine learning directly inside the database, and serves as the AI database foundation for SAP Business Data Cloud.

Used in 1 use case

SAP Joule for Consultants is a conversational AI solution that accelerates SAP cloud transformations with expert guidance grounded in SAP's most exclusive and up-to-date knowledge base, drawing on over 12 terabytes of expert-curated SAP knowledge including over 100 SAP certifications, 250 million lines of ABAP and 30 million lines of CDS code, and over three million non-public documents such as SAP Knowledge Base Articles and SAP Notes. It helps consultants configure systems, interpret ABAP logic, and troubleshoot issues in natural language.

Used in 1 use case

Claude is Anthropic's AI product line, positioned as "The AI for Problem Solvers." Its model family includes Mythos, Fable, Opus, Sonnet and Haiku, and its broader product suite includes Claude Code, Claude Cowork, @Claude, and Claude apps built for Design, Science and Security.

Used in 4 use cases

Claude Code lets developers work with Claude directly in their codebase — build, debug, and ship from the terminal, IDE, Slack, or web. It offers code onboarding/codebase mapping, turning issues into PRs, multi-file edits, and integrates with GitHub, GitLab, VS Code and JetBrains IDEs. Available for macOS, Linux, and Windows.

Used in 5 use cases

Claude Managed Agents is Anthropic's pre-built, configurable agent harness that runs in managed infrastructure, providing the harness and infrastructure for running Claude as an autonomous agent so teams don't have to build their own agent loop, tool execution, and runtime. It supports long-running, asynchronous tasks with built-in tools (Bash, file operations, web search and fetch, MCP servers), stateful sessions with persistent filesystems and conversation history, and scheduled execution via cron-based deployments. It runs in an Anthropic-managed cloud sandbox or a self-hosted sandbox on the customer's own infrastructure, and is currently in beta.

Used in 1 use case

The Claude Platform provides an API to build new user experiences and products with Claude models, offering usage-based pricing, prompt caching, tool use, and enterprise deployment support.

Used in 1 use case

BigQuery is Google Cloud's autonomous data to AI platform, automating the entire data life cycle from ingestion to AI-driven insights. Product highlights include AI-powered conversational and agentic experiences, a unified data and AI platform for analytics on multimodal data, and flexibility with low-cost ML and interoperability with open source. BigQuery offers up to 54% lower TCO versus cloud-based alternatives.

Used in 1 use case

Gemini Enterprise app is Google Cloud's secure platform to discover, create, run and govern AI agents for employees. It is part of Google Cloud's unified agentic portfolio, Gemini Enterprise, which also includes Gemini Enterprise for Customer Experience for building and managing agents across the entire customer lifecycle. It securely connects to Microsoft 365, Google Workspace, and more, and offers agents that automate multi-step, multi-app workflows.

Used in 1 use case

Google Kubernetes Engine (GKE) is a managed environment for running containerized apps, letting users put containers on autopilot and securely run enterprise workloads at scale with little to no Kubernetes operational expertise required. GKE Autopilot manages node infrastructure, scaling and security; GKE supports clusters up to 65,000 nodes with GPU and TPU support for gen AI, ML and HPC workloads, and GKE Agent Sandbox provides scalable, low-latency, gVisor-isolated infrastructure for agentic AI.

Used in 1 use case

Gemini Enterprise Agent Platform (formerly Vertex AI) is Google Cloud's platform for developers to build, scale, govern and optimize enterprise-grade AI agents grounded in enterprise data. It provides access to 200+ Google and third-party AI models and tools, including Gemini and Anthropic's Claude model family in Model Garden, plus tools for training, tuning and deploying ML models, MLOps (Model Evaluation, Pipelines for workflow orchestration, Model Registry, Feature Store), Agent Studio for evaluating, tuning and deploying generative AI models, and the Agent Development Kit (ADK) for building, customizing and fine-tuning agents.

Red Hat OpenShift is an enterprise Kubernetes application platform for building, deploying and scaling containerized and AI workloads across hybrid cloud environments, available as managed cloud services (e.g. on AWS, Azure, IBM Cloud) or self-managed editions.

Used in 4 use cases

Red Hat OpenShift AI is a flexible hybrid cloud platform to deploy open-weight models and autonomous agents at scale, combining MLOps, GenAIOps and AgentOps capabilities with open source tools like PyTorch, Kubeflow, MLflow and vLLM, plus built-in NeMo guardrails and adversarial vulnerability scanning for AI safety and security.

Used in 3 use cases

Red Hat OpenShift Platform Plus is a unified, self-managed platform to build, modernize and deploy applications at scale, adding multicluster application life-cycle management and governance, built-in Kubernetes-native security enforced throughout the application life cycle, persistent software-defined storage integrated with and optimized for OpenShift, and a scalable central registry to distribute software across multiple clusters — on top of Red Hat OpenShift Container Platform, including Red Hat Advanced Cluster Management for Kubernetes, Red Hat Advanced Cluster Security for Kubernetes, Red Hat OpenShift Data Foundation Essentials and Red Hat Quay.

Used in 1 use case

Agentforce is Salesforce's AI agent platform that delivers 24/7 autonomous support at enterprise scale across customer service, contact center, field service, employee service, and sales use cases, built on the Atlas Reasoning Engine and configurable via the low-code Agent Builder. Over 18,000 companies run on Agentforce.

Used in 3 use cases

Agentforce is the AI agent platform that delivers 24/7 autonomous support at enterprise scale. It is a proactive, autonomous AI application that answers questions, takes actions, and improves productivity. With Agentforce, customers can unlock a limitless digital labor force by leveraging autonomous AI agents that support their employees and customers 24/7. Agentforce can reason through complex requests, safely retrieve accurate knowledge about your business, take action on your behalf, and scale across teams and departments, starting with the Atlas Reasoning Engine, which breaks down the initial prompt into smaller tasks, evaluating at each step and proposing a plan for how to proceed until the complete answer or action is achieved.

Used in 5 use cases

Salesforce Data 360 (formerly Data Cloud) is the real-time data engine that powers the entire Salesforce platform, unifying fragmented enterprise data from any source — including external data lakes, websites, and legacy systems — into a single, trusted "Customer 360" profile using a Zero-Copy architecture that lets teams access and act on data instantly without moving or duplicating it. It serves as the data foundation for Agentforce, ensuring AI agents and automated workflows have accurate, up-to-date customer context, and connects directly to platforms like Snowflake, Databricks, Google BigQuery and AWS via Zero-Copy integrations.

Used in 2 use cases

IBM Consulting Advantage is an AI-powered delivery platform that equips nearly 150,000 IBM consultants with industry-, role- and business-domain-specific AI assistants, agents and applications, providing ready-to-deploy AI applications across the innovation cycle from advisory to build, integration and operations. Use cases span finance and procurement, marketing, IT transformation, data transformation, cybersecurity, and hybrid cloud.

Used in 1 use case

IBM watsonx.data is an open, hybrid data foundation that helps organizations connect, understand, govern and optimize real-time AI-ready data across hybrid environments. It works across cloud, multicloud, SaaS, client-managed VPC and on-premises environments, deployable as SaaS, self-managed software, or BYOC, and optimizes AI, BI, analytical and operational workloads with a multi-engine architecture (including Presto, Spark, OpenSearch and Cassandra). It applies consistent governance, access controls, policies, lineage and data quality across distributed data and workloads, and supports OpenRAG to ground AI agents in governed enterprise knowledge. IBM was named a Leader in the 2026 Gartner Magic Quadrant for AI Governance Platforms and a Leader in the 2025 Gartner Magic Quadrant for Data Integration Tools.

Conversational Cloud® is LivePerson's AI platform for delivering personalized digital customer experiences across voice and messaging (including SMS and WhatsApp), powering nearly 1 billion conversational interactions per month. It combines Conversational AI & automation, Conversational Intelligence & Insights, an agent/supervisor workspace, and Generative AI and Voice AI capabilities. Reported outcomes include a 2x uptick in employee efficiency, 10x conversions vs. traditional digital, a 20% boost in customer satisfaction, 90% automation containment rates, and a 50% decrease in agent attrition rates.

Conversational Cloud® is LivePerson's conversational AI platform that creates highly personalized, connected customer experiences across voice and messaging, combining human agents, intelligent automation and Conversational AI. It powers nearly 1 billion conversational interactions each month. LivePerson reports outcomes including a 2x uptick in employee efficiency, 10x conversions vs. traditional digital, a 20% boost in customer satisfaction, 90% automation containment rates, and a 50% decrease in agent attrition rates.

Used in 3 use cases

Now Assist for Customer Service Management (CSM), powered by generative AI (GenAI), reshapes the customer and agent experience. Customers can easily access solutions to simple or complex issues without searching extensively, while agents can more efficiently provide support with less manual effort, relying on contextual insights and automated summaries. Now Assist in AI Search enhances the self-service experience in portal and Virtual Agent by generating precise answers from relevant knowledge sources. Dynamic translation, powered by GenAI, allows customers to receive service in the language they prefer, translating knowledge articles in real time. Now Assist in appointment scheduling enables customers to schedule, reschedule, or cancel appointments in a conversational manner. Now Assist for Virtual Agent increases call deflection, reduces wait times, and delivers 24/7 support in resolution of common or complex customer issues. With Now Assist for CSM, agents can generate complete chat, call, or case summaries and craft knowledge articles with the click of a button, and can use the Now Assist panel, a conversational interface within their workspace, to request summaries, update assigned cases, and ask follow-up questions.

The ServiceNow AI Platform unifies AI, data, workflows and security across an enterprise's IT, CRM, security and risk, employee experience, and app development, connecting to more than 450 systems including SAP and Salesforce. It includes ServiceNow AI Agents for autonomous task execution, the ServiceNow AI Control Tower for enterprise AI governance, ServiceNow Otto as an AI assistant that completes work end-to-end across systems, and RaptorDB for unified data and analytics at scale.

Used in 1 use case

Syte is an AI-powered product discovery and recommendation platform built for ecommerce, trained on billions of apparel shopper interactions to deliver personalized recommendations designed to convert. Visual Discovery connects shoppers with products they'll love through inspiring visual search experiences powered by AI, including Image Search & Inspiration Gallery, Visual Recommendation Engines (Shop Similar, Shop Social, Shop the Look/Room and more), advanced personalized recommendations, and visual AI & data-driven marketing tools. Apparel retailers using Syte recommendations report a 7.1x higher conversion rate, 40% uplift in average order value, and an 829% increase in average revenue per user.

Used in 1 use case

Syte's Visual Discovery Suite drives conversion by using visual AI to help shoppers find products that suit their unique preferences throughout the buyer journey. It includes three solutions: Camera Search, which lets shoppers upload an image or browse a curated inspiration gallery to find their perfect item; Recommendation Engines, which combine past behavioral data and current shopper intent to dynamically optimize recommended items and serve 'shop the look' styling suggestions; and the Discovery Button, which turns every product page into a gateway for continuous, inspiration-led browsing. Syte reports customers see an average 423% ROI, 9.8% average order value uplift and 177% conversion rate uplift from the suite.

Used in 1 use case

Lola is the virtual assistant of the University Information Service, helping new students in Degrees during the pre-registration and enrollment process of the University of Murcia. Students can talk to Lola from the UMU registration website.

LEXI is AI-Media's AI-powered captioning tool kit, covering live and recorded captioning, translation, display, archiving and search. It includes LEXI Text for automatic live captions, LEXI Recorded for automated multilingual captioning of recorded media, LEXI Voice for live AI voice translation, LEXI AI for secure generative AI insights, LEXI Translate for AI-powered caption translation, LEXI Local for on-premises captioning, and LEXI AD for AI-powered audio description.

Amdocs amAIz is a telco-grade generative AI platform designed exclusively for telecommunications, giving Service Providers a foundation to unlock the transformative potential of GenAI across BSS, OSS and Network technologies. Built on a GAI Framework, Telco Taxonomy, Use Case Kits and Governance layers, amAIz leverages existing foundation AI models and LLMs to generate highly contextual, human-like responses, and integrates with existing Telcos' B/OSS/NW portfolio for rapid deployment and scalability. It includes governance mechanisms and Telco Guardrails to help ensure compliance with privacy regulations and industry standards, with PII discovery and masking/tokenizing, encryption and data anonymization to protect customer data. amAIz features an expanding library of pre-built use cases, such as Bill Inquirer for resolving customer billing issues, and integrates with Amdocs Copilot capabilities like CPQ Copilot, which automatically generates B2B proposals and optimizes pricing to improve deal closure rates and profitability.

The C3 Agentic AI Platform is C3 AI's model-driven operating system for enterprise AI, modeling an organization's entire enterprise as a unified ontology graph that connects every business entity, process and relationship into a foundation applications, agents and workflows build from. It provides omni-modal data fusion, a unified ontology graph, proprietary AI models, and autonomous agents and workflows that can run with a human in the loop or automate end to end, with every action traceable and auditable. C3 Code is the platform's development layer for shipping production-grade AI applications quickly. C3 AI reports 500+ AI models in production for Dow across 50+ steam cracker furnaces with 13K+ sensors streaming live data, and 3,100+ AI models in production across 85+ cement plants for Holcim with 1,100+ critical assets monitored via predictive maintenance.

Used in 2 use cases

Cognite Data Fusion® is an open, secure Industrial Data and AI platform that enables quick deployment of a contextualized data foundation for rapid scaling of AI-powered digital solutions, unifying operational, IT and engineering data to break down data silos, contextualizing it (time series, P&IDs, 3D models and more) with AI-assisted search, and activating it to power industrial AI solutions at scale. It offers 90+ ready-to-use extractors and connectors and data extraction pipeline monitoring, and reports a 465% ROI and $29.4M of total benefits over three years from a Total Economic Impact study.

Used in 1 use case

The Dell AI Factory with NVIDIA is a full-stack, end-to-end enterprise AI solution combining Dell's AI-optimized infrastructure with NVIDIA's accelerated computing and enterprise AI software, including NVIDIA AI Enterprise, NVIDIA NIM, NVIDIA Run:ai and NVIDIA Omniverse, to take agentic AI from pilot to production with data control, governance and compliance-readiness built in. It is trusted by over 5,000 customers worldwide, runs on Dell PowerEdge servers (including the XE9780, R770, R7725, XE7740 and XE7745) alongside NVIDIA RTX PRO GPUs, and its modular architecture helps organizations scale from pilot projects to full-scale production.

Used in 1 use case

ey.ai, EY's Reimagination Engine, is an open, dynamic AI-led technology engine at the heart of EY that gives organizations the confidence to reimagine the enterprise and the capability to realize value at scale. Through connected capabilities, confident intelligence and enduring transformation, it orchestrates EY technology and solutions with an ecosystem of alliance partners so AI works across an organization rather than in isolated tools or teams, with governance, security, risk and data controls built into its foundation.

Figure 02 is Figure AI's second-generation humanoid robot, combining the dexterity of the human form with advanced AI to perform tasks across commercial applications. It features speech-to-speech conversation through onboard mics and speakers connected to custom AI models trained in partnership with OpenAI, an onboard vision language model for fast common-sense visual reasoning from robot cameras, a 2.25 kWh custom battery pack delivering 50%+ more energy than the prior generation, integrated wiring for concealed cabling, an AI-driven vision system powered by 6 onboard RGB cameras, and 4th-generation hands with 16 degrees of freedom and human-equivalent strength. Figure 02 has 3x the onboard computation and AI inference of the previous generation, enabling real-world AI tasks to be performed fully autonomously, and was tested at BMW Manufacturing for AI data collection and use case training.

The Han Configurator is HARTING's step-by-step online tool for designing and customizing Han connector solutions, letting customers select compatible housings, inserts and modules from the Han portfolio while the tool validates compatibility with application requirements. It includes a Solution Wizard that lets users answer a few questions and receive an intelligent recommendation for the right configuration, and lets customers export CAD data, technical data sheets and parts lists, or send the configuration directly for ordering.

HMND 01 Alpha Wheeled is Humanoid's next-generation humanoid robot, built in just 7 months, designed to efficiently perform a wide range of industrial tasks. It runs on KinetIQ, Humanoid's proprietary four-layer AI stack built on NVIDIA compute, which coordinates multiple robots across industrial environments, with KinetIQ Ascend enabling real-world learning through trial and error. The robot weighs 300 kg, stands 220 cm tall, has 29 degrees of freedom, a max speed of 2 m/s on an omnidirectional wheeled base (up to 7.2 km/h), an average run time of 4 hours, and a 15 kg payload. It features RGB, depth and 6D force/torque sensors with haptic feedback, 360° RGB cameras with two depth sensors on the head, and a modular end-effector design offering a 12-DOF five-finger hand or a 1-DOF parallel gripper.

Used in 1 use case

Immuta provisions data in real time, with full control, across an organization's entire data ecosystem, treating both humans and AI agents as first-class, governable participants for policy, provisioning, and compliance.

Used in 1 use case

Khanmigo is Khan Academy's AI-powered teaching assistant and personal tutor for students, teachers, and parents. Rather than giving direct answers, it guides learners to find answers themselves, drawing on Khan Academy's content library covering math, humanities, coding, and social studies. For teachers, it helps with lesson planning, rubrics, quiz questions, and student groupings. Common Sense Media gave Khanmigo an overall 4-star rating among AI-for-education tools.

Luminance uses Legal-Grade™ AI agents to automate, expedite, and enhance contract activity across the business — drafting, negotiating, analyzing, complying, investigating, and collaborating on contracts, including AI-powered review that integrates directly with Word. Luminance's AI-powered contract repository gives instant insight into an organization's entire contractual landscape, automatically identifying anomalies, trends and deviations, and its Lumi Pro assistant allows fast document review across an entire contract database. Luminance reports helping 1,000+ customers achieve 90% time-savings on contract review, a 98% reduction in contract management costs, and 500+ hours saved on contract generation.

The i.MX 93W Integrated Wireless Applications Processor brings compute and connectivity together by integrating the i.MX 93 applications processor, the IW610G tri-radio and its complete radio bill of materials (passives, crystal and RF front-end) into a single package. It features Arm Cortex-A55 and Cortex-M33 cores, an EdgeLock Secure Enclave, a dedicated neural processor (NPU), dual-band Wi-Fi 6, Bluetooth Low Energy and 802.15.4 connectivity. This integration simplifies RF design, speeds certification and time-to-market, and reduces size and supply-chain complexity by replacing up to 60 discrete components with a single, pre-validated solution. The i.MX 93W is currently a preproduction product expected to be available in Q2 2027.

Shift Claims is Shift Technology's agentic AI solution that helps insurers transform claims operations by assessing and prioritizing cases, guiding and assisting claim handlers, and automating tasks across the claims lifecycle, speeding processing, improving accuracy and lowering claims losses and handling costs. It transforms four key areas of claims management: assessing claims complexity (reviewing documentation for coverage exclusion, liability, damage, injury, subrogation and litigation exposure); classifying, prioritizing and assigning claims; assisting and advising claims handlers with dynamic guidance through document review, decision-making, communication and negotiation; and automating claims and individual tasks throughout the claims lifecycle via an STP agent. Designed for AI and human collaboration, Shift Claims integrates seamlessly with existing claims and core systems as an AI layer, with insurers retaining full control of the process. Early adopters report 3% lower claims losses, 30% faster claims handling, a 60% overall automation rate, and greater than 99% accuracy in claims assessment. AXA Switzerland is an early adopter of Shift Claims.

Used in 1 use case

Philips GrowWise smart spectrum is an algorithm designed to automatically optimize horticultural LED lighting based on real-time sunlight irradiation, fed with data from 18 years of in-depth research and leadership in light-plant interaction. By replacing less efficient spectra with more energy-efficient ones, GrowWise smart spectrum saves on energy use or boosts crop growth by up to 6%. It works seamlessly with Philips GrowWise control, a proven solution already installed at over 750 horticultural sites worldwide, integrates directly with leading climate computers, and gives growers a dashboard with clear insights into energy savings, light efficiency, and spectral distribution.

Athlete Intelligence is Strava's generative AI feature that analyzes activity data, including health and location data, to create personalized summaries and insights on activities for select sport types (run, trail run, ride, gravel ride, mountain bike ride, walk, hike). It provides detailed analysis of stats such as pace/speed, Grade Adjusted Pace, heart rate, heart rate zones and pace/power zones, may reference the athlete's focus for further personalization, and is available to Strava subscribers and athletes with a free trial, on mobile only, in all languages Strava supports.