Spinning Data Flywheels To Turn Raw Data Into Domain-Specific Intelligence
Dataloop is proud to be integrated into the expanded NVIDIA Enterprise AI Factory validated design, announced during GTC Paris at VivaTech 2025. Through these validated design integrations and production-ready pipelines, Dataloop supports scalable development of generative AI models and agentic AI systems across highly regulated enterprise environments.
The NVIDIA Enterprise AI Factory is a full-stack, validated design that offers guidance for enterprises to build and deploy their own on-premises AI factory. Validated by NVIDIA, these AI factories combine accelerated compute, including NVIDIA Blackwell GPUs and NVIDIA networking, with enterprise-grade software such as NVIDIA AI Enterprise, NVIDIA NIM, and NVIDIA NeMo microservices.
These solutions enable enterprises to run high-performance AI workloads-such as large language model (LLM) inference, multimodal processing, and agentic AI-while maintaining control, data privacy, and regulatory compliance. With pre-integrated blueprints and developer recipes,
NVIDIA Enterprise AI Factory streamlines the creation of sovereign, domain-specific AI agents.
Sovereign AI agents are designed to operate within region-specific regulatory, data residency, and privacy frameworks-while still leveraging globally validated AI infrastructure. By combining local data governance with enterprise-scale deployment, these agents empower organizations to deliver trustworthy, contextual intelligence tailored to their unique environments.
At the core of this initiative is the new NVIDIA universal LLM NIM microservice that supports lightning-fast inference for a broad range of large language models (LLMs). This capability enables automatic optimization using the most suitable inference engine for a particular LLM-such as NVIDIA TensorRT-LLM, vLLM, or SGLang-for maximum performance across any NVIDIA accelerated infrastructure.
This dramatically simplifies LLM deployment with just a few commands and extends support to LLMs specialized in 35 regional languages -empowering developers to create culturally contextualized agents. As a modular service, the universal LLM NIM microservice also ensures interoperability across open-source frameworks and real-world production environments, creating a foundation for responsible, sovereign AI.
Accelerating Model Deployment Cycles by Connecting Data and Compute Layers
Dataloop’s integration with the NVIDIA Enterprise AI Factory addresses a core challenge in AI development: connecting high-performance infrastructure with the structured, multimodal data required to train and improve models and agents at scale.
By combining the NVIDIA accelerated computing stack-including NVIDIA NIM and NVIDIA NeMo microservices, both part of NVIDIA AI Enterprise-with Dataloop’s orchestration layer for data preparation and pipeline automation, the joint architecture enables:
- One-click deployment of NVIDIA NIM microservices directly within the Dataloop platform, reducing setup overhead and enabling low-latency inference across embedded, production-grade models
- Accelerated data-to-agent pipelines that transform unstructured data-such as images, video, audio, and text-into AI-ready inputs within a unified, multimodal workflow
- Support for active learning workflows using data flywheels that leverage inference outputs and user feedback to trigger automated retraining, evaluation, and redeployment
- Multimodal pipeline orchestration that simplifies the creation of domain-specific agents capable of reasoning across diverse data types in regulated, real-world environments
- Enterprise-grade security and flexible deployment options, enabling hybrid, on-premises, and cloud-based architectures with compliance-ready infrastructure
NVIDIA solutions, including NVIDIA NIM, are available through the NVIDIA Hub within the Dataloop platform, simplifying and accelerating integration for developers. These pretrained, production-grade models are instantly accessible and ready for deployment.
Dataloop orchestrates the full Data-to-Agent Lifecycle, enabling continuous development, validation, and deployment of intelligent, domain-specific AI agents. This lifecycle bridges unstructured data management with model fine-tuning and inference-creating a reproducible, modular system for building and improving agentic systems at scale.
It begins with multimodal data ingestion-images, video, text, and audio-automatically processed through Dataloop’s visual data pipelines. The data is curated, queried, clustered, and managed using built-in DataOps tools, then funneled into Dataloop’s orchestration layer alongside NVIDIA AI Enterprise and NVIDIA NeMo framework.
From there, Dataloop enables:
- Agent validation through human-in-the-loop or automated feedback
- Model fine-tuning via NVIDIA NeMo Customizer
- Performance evaluation using NVIDIA NeMo Evaluator
- Scalable deployment using pre-integrated NVIDIA NIM Microservices from the NVIDIA Hub within the Dataloop platform
This end-to-end solution supports iterative improvement through feedback, retraining, and redeployment-ensuring AI agents remain accurate, efficient, and aligned with real-world tasks.
One Unified Flow: From Raw Data to AI Agent
Dataloop’s integration with NVIDIA Enterprise AI Factory enables enterprises to build and deploy domain-specific AI agents more rapidly and securely. With validated, interoperable infrastructure, organizations can accelerate time to production, ensure data privacy and compliance through on-premises pipelines, and continuously refine agents using real-world feedback in a closed-loop workflow.
As a unified platform for managing, processing, and preparing unstructured data, Dataloop empowers teams to train, fine-tune, and operate AI systems at scale-from annotation to multi-cloud pipeline orchestration.
Dataloop and NVIDIA are collaborating on agentic AI use cases built on the NVIDIA Enterprise AI Factory validated design. By extending its integration with NVIDIA AI Enterprise and NVIDIA NIM microservices, Dataloop enables enterprises to build GPU-accelerated AI agents using structured, multimodal data pipelines in a secure, unified environment. These capabilities are available through the NVIDIA Hub within the Dataloop platform, simplifying integration and accelerating deployment.
A real-world customer leveraging this joint value is Syngenta, a global leader in agricultural innovation. Syngenta uses AI to detect crop diseases and pests across vast and diverse farming environments. To build robust AI systems capable of such precision, Syngenta relies on Dataloop’s platform to develop high-quality datasets through advanced data preprocessing and curation workflows. The platform supports scalable data operations, collaborative human review processes, and structured data pipelines that allow Syngenta’s teams to manage complex, unstructured agricultural data efficiently and prepare it for downstream model development.
Figure 1: The image shows the NVIDIA Hub within the Dataloop platform. This interface allows users to explore and manage a wide range of NVIDIA NIM and NeMo Retriever microservices, which are containerized, ready-to-deploy AI models.
Figure 2: The image shows the Blueprints tab in the NVIDIA Hub, featuring ready-to-deploy, GPU-optimized AI pipelines designed by NVIDIA to accelerate development and simplify deployment on the Dataloop platform.
Figure 3: The image shows a multimodal AI workflow on the Dataloop platform, integrating NVIDIA NIM™ and NeMo Retriever microservices to automate inference, streamline real-time decision-making, and scale processing of unstructured data across environments.
Figure 4: The image shows a custom embedding workflow on the Dataloop platform, refining CLIP-based representations for optimized search, similarity, and recommendations using domain-specific data and reinforcement learning from human feedback (RLHF) techniques
Figure 5: The image shows a Retrieval-Augmented Generation (RAG) workflow on the Dataloop platform, powered by NVIDIA NeMo Retriever and NVIDIA NIM, combining real-time data retrieval with generative AI for context-aware, GPU-accelerated responses.
Figure 6: The image shows an automated data preparation pipeline on the Dataloop platform, using NVIDIA NeMo Retriever to transform unstructured data into structured datasets for scalable, high-performance AI training and model optimization
Dataloop accelerated by NVIDIA infrastructure, delivering the orchestration layer that brings data, models, and human input into a cohesive workflow. While NVIDIA provides the optimized compute and model-serving capabilities, Dataloop simplifies how those components are applied-transforming raw data into AI-ready pipelines and enabling faster development of production-grade agents.
