New York, NY, USA, 2 September 2026<\/p>\n
Key takeaways<\/p>\n
\nDeloitte\u2019s Open Model Engineering practice is designed to help clients deploy enterprise AI applications with greater flexibility, cost predictability, sovereignty, and enhanced control over their data, intellectual property, model behavior, and inference transparency.
\nAt the outset, the practice will work with Deloitte firm clients in North America, Europe, and Asia Pacific developing enterprise AI applications and products built on open-source AI frameworks and open models.
\nDeloitte plans to hire, train, and certify forward deployed engineers through fiscal year 2027 to grow the new practice.
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Why this matters<\/p>\n
Deloitte today announced the launch of its Open Model Engineering practice in Deloitte firms around the globe. The move is an expansion of Deloitte\u2019s AI services portfolio, reflecting how enterprises and governments are increasingly evaluating open AI models as a way to gain deployment flexibility, manage cost, and increase choices through a mixture of models. Deloitte’s new Open Model Engineering practice aims to help organizations design, engineer, deploy and operate AI solutions using open models and full stack open-source engineering tailored to each client\u2019s business requirements, regulatory environment and technology footprint.<\/p>\n
\u201cOpen models are an increasingly important part of the AI landscape and can complement proprietary AI platforms,\u201d says Nitin Mittal, Deloitte Global AI leader. \u201cBy helping organizations take a mixed-model approach to building, deploying, and scaling multi-agent systems, we can help optimize technical choices while preserving flexibility across their AI stack and maximizing return on investment.\u201d<\/p>\n
Building an Open Model Engineering practice around the globe<\/p>\n
Deloitte firms are rolling out Open Model Engineering services across multiple geographies to help clients evaluate, adopt, and scale open models alongside their existing AI investments, with an initial focus in North America, Europe and Asia Pacific.<\/p>\n
\nEnterprises today are navigating four important considerations in AI deployment: flexibility to select the right model and architecture for each workload, predictable cost economics as Agentic AI usage scales, sovereignty over where and how models are deployed, and control over enterprise data, intellectual property (IP), and model behavior with transparency on inference.
\nThrough Deloitte\u2019s Open Model Engineering practice, Deloitte firms can help clients:
\n\u2022 Make model and architecture choices selecting from proprietary models, open models, Agentic platforms, cloud services, and\/or on-premises infrastructure;
\n\u2022 Manage token economics by choosing the right model, infrastructure and deployment patterns, resulting in improved return on investment;
\n\u2022 Provide greater flexibility to build AI applications of open models, while further fine-tuning them for geographical, linguistic and cultural context; and
\n\u2022 Gain greater choice over where models run, what data is uploaded, and how competitive advantage is retained by protecting IP and the uniqueness of generated inference.
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Agentic AI capabilities powered by NVIDIA Nemotron family of open models<\/p>\n
Deloitte\u2019s Open Model Engineering practice will initially focus on developing enterprise AI applications built on NVIDIA Nemotron open models and NIM microservices. Deloitte firms will also help clients build Sovereign AI stacks using open-source AI frameworks, fine tune open models, and enhance cyber defenses with open harnesses.<\/p>\n
\u201cEnterprises need both open and proprietary models for different workloads,\u201d says Kari Briski, vice president of Generative AI at NVIDIA. \u201cDeloitte\u2019s Open Model Engineering practice, built on NVIDIA Nemotron models, gives developers greater control over enterprise data and proprietary information while enabling secure, responsible deployment of Agentic AI.\u201d<\/p>\n