AI Factories - Launching NVIDIA Mission Control - Orchestration and Management Software for AI Factories

An AI factory is a scalable, repeatable system that turns raw data into valuable AI-driven insights, products, or services. It typically consists of automated pipelines for data collection, pre-processing, model training, deployment, and monitoring. Think of it as an assembly line measured by tokens for artificial intelligence—standardising how businesses build, test, and scale AI solutions.

Any organisation that wants to scale its use of artificial intelligence efficiently and responsibly can benefit from an AI factory.

Any organisation that wants to scale its use of artificial intelligence efficiently and responsibly can benefit from an AI factory. This includes:

• Enterprises managing large datasets and multiple AI projects
• Healthcare providers using AI for diagnostics or patient management
• Financial institutions deploying AI for fraud detection or risk modelling
• Retailers and e-commerce companies focused on personalisation and demand forecasting
• Manufacturers aiming to automate processes or predict maintenance
• Government and research bodies working with sensitive or high-volume data

Whether you're building one model or hundreds, an AI factory helps deliver repeatable, reliable, and governed AI outputs.

AI factories provide numerous benefits that help businesses mature their AI capabilities:

• Faster time-to-value. Automate data prep, training, and deployment to reduce lead times.
• Scalability, with the ability to reuse pipelines and models across teams and use cases.
• Consistency, applying standardised workflows, reducing error and technical debt.
• Improved ROI, streamlining operational costs and increasing model performance.
• Governance and compliance with added centralised control over data usage, lineage, and versioning.
• Adaptability, integrate with cloud, hybrid, or on-prem environments to match infrastructure needs.

These advantages make AI factories a strategic asset for organisations pursuing data-driven innovation.

Yes, small businesses can use AI factories by leveraging cloud platforms that provide pre-built templates and low-code options. These tools make it easier and more affordable for smaller teams to deploy AI workflows without building complex infrastructure from scratch. Contact our Cloud team for your readiness check today.

An AI factory helps your business scale AI by systematising the model development pipeline. Allowing teams to: reuse components, reduce development time, ensure consistency, and deploy models faster. This scalability is essential for companies looking to integrate AI into multiple departments or products.

Yes, AI factories can integrate across environments. Hybrid and multi-cloud setups allow companies to process data where it lives: whether in the cloud, at the edge (e.g., IoT devices), or in secure on-premises systems, always ensuring flexibility and compliance. Contact our specialised team for your readiness check today.

Yes, AI factories can integrate across environments. Hybrid and multi-cloud setups allow companies to process data where it lives: whether in the cloud, at the edge (e.g., IoT devices), or in secure on-premises systems, always ensuring flexibility and compliance. Contact our specialised team for your readiness check today.

Migrating to an AI factory model is a strategic move that requires planning, the right tools, and experienced support. Scan AI acts as your specialist partner to streamline this transformation, bringing technical expertise, cutting-edge infrastructure, and industry-proven workflows.

Here’s how the migration journey typically unfolds with Scan AI by your side:

• Discovery & Assessment – Starting with a full audit across the whole business that identifies bottlenecks in data prep, model development, and deployment.
• AI Factory Blueprint Design – Our teams work with you to design a scalable & secure AI factory architecture tailored to your goals. This includes:
  • Standardised data ingestion and pre-processing pipelines
  • Reusable model training and deployment workflows
  • Governance and monitoring frameworks
  • On-prem, cloud, or hybrid infrastructure integration
• Infrastructure & Platform Setup – Choose infrastructure and tools that support scalability, are built for performance, security and long-term growth.
• Automation and MLOps Integration – We help operationalise your AI workflows with best-in-class MLOps practices—introducing version control, CI/CD pipelines, model monitoring, and retraining strategies. This enables continuous delivery of AI models across teams and business units.
• Team Enablement – Scan AI doesn't just build the factory; we empower your team to run it. Alongside NVIDIA, we can provide documentation, training, and ongoing support to ensure your data scientists, engineers, and decision-makers can maintain, scale, and improve the system confidently.
• Ongoing Optimisation & Support – Your AI factory isn't a static solution. Scan AI offers long-term support and optimisation services, helping you manage compute resources efficiently, track carbon usage, refine models, and adopt emerging AI tools.