Dataiku Launches AI Blueprint to Help Manufacturers Modernize Factory Operations With NVIDIA AI

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Dataiku Launches AI Blueprint to Help Manufacturers Modernize Factory Operations With NVIDIA AI Business Wire Thu, June 25, 2026 at 9:00 AM EDT 4 min read NVDA New Manufacturing AI Blueprint delivers an intelligent Maintenance Scheduling Assistant designed to reduce disruption, improve coordination, and bring governed AI to the factory floor NEW YORK, June 25, 2026 --( BUSINESS WIRE )--Amid evolving labor shortages and supply chain dynamics, manufacturers are focused on delivering more uptime with fewer resources — while many of the systems guiding critical maintenance decisions remain largely manual and fragmented. Dataiku , The Platform for AI Success, today announced a new Manufacturing AI Blueprint, Maintenance Scheduling Assistant , built with NVIDIA AI to help industrial organizations modernize how maintenance decisions are made. Designed for global manufacturers, the blueprint combines Dataiku's governed AI platform with NVIDIA AI infrastructure and leverages the recently released NVIDIA Nemotron 3 Super open model to give maintenance leaders a smarter, more coordinated way to plan by delivering high accuracy in reasoning and instruction-following for complex agentic tasks. By harnessing NVIDIA's high-performance (NVFP4) precision format, this architecture allows manufacturers to process large volumes of operational data faster and generate maintenance recommendations without sacrificing real-time performance. Built for real-world industrial environments, the Dataiku Manufacturing AI Blueprint is validated on NVIDIA RTX PRO 6000 Blackwell Server Edition and NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, delivering the performance and efficiency required to run advanced AI workloads reliably at scale. "Manufacturing is entering a phase where operational resilience is becoming a competitive advantage," said Todd Hallett, SVP of GTM Strategy at Dataiku. "The question isn't whether AI can predict failures — it's whether organizations can operationalize that intelligence in a way that's trusted, scalable, and embedded into daily decision-making. This blueprint, powered by NVIDIA, helps manufacturers move beyond isolated automation toward AI systems that can reason through complex operational trade-offs and support better decisions in everyday operations, laying the foundation for a new class of AI reasoning systems." Through a natural language interface, maintenance managers can request optimized maintenance schedules based on current plant conditions and business priorities. The system evaluates operational signals and production constraints in real time, recommending clear, prioritized maintenance plans that adapt as conditions on the factory floor shift. Instead of relying on spreadsheets, siloed systems, and reactive workflows, factories can now use an intelligent assistant that dynamically balances equipment health, production schedules, and workforce availability — adapting to changing conditions rather than relying on static rules or manual coordination. The result: fewer unplanned disruptions, faster planning cycles, and stronger alignment between maintenance and production teams, all within an enterprise AI environment built for oversight and control. By pairing NVIDIA's high-performance AI infrastructure and AI software with Dataiku's centralized governance, manufacturers can deploy advanced predictive capabilities without sacrificing transparency or operational discipline. This approach reflects a broader shift toward AI-driven reasoning systems, where decisions are not just automated but informed by data, models, and business context to support more consistent and coordinated operations. By bringing together data, models, and human expertise, manufacturers can coordinate decisions across maintenance, production, and resource planning — embedding institutional knowledge directly into workflows with transparency and control. We call this the Dataiku Reasoning System for Manufacturing Operations , and it's available today. This new offering extends use cases like the Maintenance Scheduling Assistant Blueprint, allowing them to be integrated and managed at scale across production areas and even factories. "AI is improving uptime and scaling efficiency across complex manufacturing pipelines," said Jason Schroedl, Director, Product Marketing - Enterprise Platforms, NVIDIA. "Dataiku's integration of NVIDIA AI software, Nemotron open models, and RTX PRO Blackwell Server Edition GPUs provides manufacturers with performance and reliability for deploying intelligent maintenance systems at scale." One of several solutions designed in collaboration between Dataiku and NVIDIA, the Manufacturing AI Blueprint is available to customers in environments running NVIDIA AI Enterprise software. To learn more, visit: https://www.dataiku.com/partners/nvidia/ . Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams design and operate analytics, machine learning, and AI agents with the transparency, collaboration, and control enterprises require. Sitting above data platforms, cloud infrastructure, and AI services, Dataiku connects the full enterprise AI stack — empowering organizations to run AI across multi-vendor environments with centralized governance. The world's leading companies rely on Dataiku to operationalize AI and run it as a true business performance engine delivering measurable value. For more, visit the Dataiku blog , LinkedIn , X , and YouTube . View source version on businesswire.com: https://www.businesswire.com/news/home/20260625926674/en/

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Dataiku announced NVIDIA AI-based Manufacturing AI Blueprint, Maintenance Scheduling Assistant. It leverages the Nemotron 3 Super open model and NVFP4 precision, and has been validated on RTX PRO 6000·4500 Blackwell Server Edition to secure real-time inference performance in factories. With a natural language interface to automate maintenance planning, it aims to reduce unplanned downtime and speed up planning while providing centralized governance for transparency and control. This is expected to accelerate adoption of AI inference systems in factory operations, increasing demand for GPUs and platforms and prompting a shift away from existing manual processes.

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