Nurix Integrations

OpenAI aims to guarantee that artificial general intelligence (AGI)—defined as highly autonomous systems excelling beyond human capabilities in most economically significant tasks—serves the interests of all humanity. While we intend to develop safe and advantageous AGI directly, we consider our mission successful if our efforts support others in achieving this goal. You can utilize our API for a variety of language-related tasks, including semantic search, summarization, sentiment analysis, content creation, translation, and beyond, all with just a few examples or by clearly stating your task in English. A straightforward integration provides you with access to our continuously advancing AI technology, allowing you to explore the API’s capabilities through these illustrative completions and discover numerous potential applications.

Gemini is Google’s intelligent AI platform built to support productivity, creativity, and learning across work, school, and everyday life. It allows users to ask questions, generate text, images, and videos, and explore ideas using conversational AI powered by Gemini 3. By integrating directly with Google Search, Gemini provides grounded answers and supports detailed follow-up discussions on complex topics. The platform includes advanced tools like Deep Research, which condenses hours of online research into structured reports in minutes. Gemini also enables real-time collaboration and spoken brainstorming through Gemini Live. Users can connect Gemini to Gmail, Google Docs, Calendar, Maps, and other Google services to complete tasks across multiple apps at once. Custom AI experts called Gems allow users to save instructions and tailor Gemini for specific roles or workflows. Gemini supports large file analysis with a long context window, making it capable of reviewing books, reports, and large codebases. Flexible subscription tiers offer different levels of access to models, credits, and creative tools. Gemini is available on web and mobile, making it accessible wherever users need intelligent assistance.

Claude is an advanced AI assistant created by Anthropic to help users think, create, and work more efficiently. It is built to handle tasks such as content creation, document editing, coding, data analysis, and research with a strong focus on safety and accuracy. Claude enables users to collaborate with AI in real time, making it easy to draft websites, generate code, and refine ideas through conversation. The platform supports uploads of text, images, and files, allowing users to analyze and visualize information directly within chat. Claude includes powerful tools like Artifacts, which help organize and iterate on creative and technical projects. Users can access Claude on the web as well as on mobile devices for seamless productivity. Built-in web search allows Claude to surface relevant information when needed. Different plans offer varying levels of usage, model access, and advanced research features. Claude is designed to support both individual users and teams at scale. Anthropic’s commitment to responsible AI ensures Claude is secure, reliable, and aligned with real-world needs.

Taam Cloud is a comprehensive platform for integrating and scaling AI APIs, providing access to more than 200 advanced AI models. Whether you're a startup or a large enterprise, Taam Cloud makes it easy to route API requests to various AI models with its fast AI Gateway, streamlining the process of incorporating AI into applications. The platform also offers powerful observability features, enabling users to track AI performance, monitor costs, and ensure reliability with over 40 real-time metrics. With AI Agents, users only need to provide a prompt, and the platform takes care of the rest, creating powerful AI assistants and chatbots. Additionally, the AI Playground lets users test models in a safe, sandbox environment before full deployment. Taam Cloud ensures that security and compliance are built into every solution, providing enterprises with peace of mind when deploying AI at scale. Its versatility and ease of integration make it an ideal choice for businesses looking to leverage AI for automation and enhanced functionality.

Anyscale is a configurable AI platform that unifies tools and infrastructure to accelerate the development, deployment, and scaling of AI and Python applications using Ray. At its core is RayTurbo, an enhanced version of the open-source Ray framework, optimized for faster, more reliable, and cost-effective AI workloads, including large language model inference. The platform integrates smoothly with popular developer environments like VSCode and Jupyter notebooks, allowing seamless code editing, job monitoring, and dependency management. Users can choose from flexible deployment models, including hosted cloud services, on-premises machine pools, or existing Kubernetes clusters, maintaining full control over their infrastructure. Anyscale supports production-grade batch workloads and HTTP services with features such as job queues, automatic retries, Grafana observability dashboards, and high availability. It also emphasizes robust security with user access controls, private data environments, audit logs, and compliance certifications like SOC 2 Type II. Leading companies report faster time-to-market and significant cost savings with Anyscale’s optimized scaling and management capabilities. The platform offers expert support from the original Ray creators, making it a trusted choice for organizations building complex AI systems.

Hugging Face is an AI community platform that provides state-of-the-art machine learning models, datasets, and APIs to help developers build intelligent applications. The platform’s extensive repository includes models for text generation, image recognition, and other advanced machine learning tasks. Hugging Face’s open-source ecosystem, with tools like Transformers and Tokenizers, empowers both individuals and enterprises to build, train, and deploy machine learning solutions at scale. It offers integration with major frameworks like TensorFlow and PyTorch for streamlined model development.

DeepSpeed is an open-source library focused on optimizing deep learning processes for PyTorch. Its primary goal is to enhance efficiency by minimizing computational power and memory requirements while facilitating the training of large-scale distributed models with improved parallel processing capabilities on available hardware. By leveraging advanced techniques, DeepSpeed achieves low latency and high throughput during model training. This tool can handle deep learning models with parameter counts exceeding one hundred billion on contemporary GPU clusters, and it is capable of training models with up to 13 billion parameters on a single graphics processing unit. Developed by Microsoft, DeepSpeed is specifically tailored to support distributed training for extensive models, and it is constructed upon the PyTorch framework, which excels in data parallelism. Additionally, the library continuously evolves to incorporate cutting-edge advancements in deep learning, ensuring it remains at the forefront of AI technology.

Gemini Enterprise, an all-encompassing AI platform from Google Cloud, is designed to harness the full capabilities of Google’s sophisticated AI models, tools for creating agents, and enterprise-level access to data, seamlessly integrating these into daily workflows. This innovative solution features a cohesive chat interface that facilitates employee interaction with internal documents, applications, various data sources, and personalized AI agents. The foundation of Gemini Enterprise consists of six essential elements: the Gemini suite of large multimodal models, an agent orchestration workbench (previously known as Google Agentspace), ready-made starter agents, powerful data integration connectors for business systems, extensive security and governance frameworks, and a collaborative partner ecosystem for customized integrations. Built to scale across various departments and organizations, it empowers users to develop no-code or low-code agents capable of automating diverse tasks like research synthesis, customer service responses, code assistance, and contract analysis while adhering to corporate compliance regulations. Moreover, the platform is designed to enhance productivity and foster innovation within businesses, ensuring that users can leverage advanced AI technologies with ease.

NVIDIA AI Enterprise serves as the software backbone of the NVIDIA AI platform, enhancing the data science workflow and facilitating the development and implementation of various AI applications, including generative AI, computer vision, and speech recognition. Featuring over 50 frameworks, a range of pretrained models, and an array of development tools, NVIDIA AI Enterprise aims to propel businesses to the forefront of AI innovation while making the technology accessible to all enterprises. As artificial intelligence and machine learning have become essential components of nearly every organization's competitive strategy, the challenge of managing fragmented infrastructure between cloud services and on-premises data centers has emerged as a significant hurdle. Effective AI implementation necessitates that these environments be treated as a unified platform, rather than isolated computing units, which can lead to inefficiencies and missed opportunities. Consequently, organizations must prioritize strategies that promote integration and collaboration across their technological infrastructures to fully harness AI's potential.

AMD Radeon™ ProRender serves as a robust physically-based rendering engine that allows creative professionals to generate breathtakingly photorealistic visuals. Leveraging AMD’s advanced Radeon™ Rays technology, this comprehensive and scalable ray tracing engine utilizes open industry standards to optimize both GPU and CPU performance, ensuring rapid and impressive outcomes. It boasts an extensive, native physically-based material and camera system, empowering designers to make informed choices while implementing global illumination. The unique combination of cross-platform compatibility, rendering prowess, and efficiency significantly shortens the time needed to produce lifelike images. Additionally, it utilizes the power of machine learning to achieve high-quality final and interactive renders much more quickly than traditional denoising methods. Currently, free plug-ins for Radeon™ ProRender are available for a variety of popular 3D content creation software, enabling users to craft remarkable, physically accurate renderings with ease. This accessibility broadens the creative possibilities for artists and designers across various industries.

Together AI offers a cloud platform purpose-built for developers creating AI-native applications, providing optimized GPU infrastructure for training, fine-tuning, and inference at unprecedented scale. Its environment is engineered to remain stable even as customers push workloads to trillions of tokens, ensuring seamless reliability in production. By continuously improving inference runtime performance and GPU utilization, Together AI delivers a cost-effective foundation for companies building frontier-level AI systems. The platform features a rich model library including open-source, specialized, and multimodal models for chat, image generation, video creation, and coding tasks. Developers can replace closed APIs effortlessly through OpenAI-compatible endpoints. Innovations such as ATLAS, FlashAttention, Flash Decoding, and Mixture of Agents highlight Together AI’s strong research contributions. Instant GPU clusters allow teams to scale from prototypes to distributed workloads in minutes. AI-native companies rely on Together AI to break performance barriers and accelerate time to market.

Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain. Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.