Turn your AI factory into business outcomes:

The AI Factory operating model is how we help enterprises convert NVIDIA's accelerated infrastructure into running production systems. It rests on four capabilities: agentic AI systems that execute end-to-end workflows; performance engineering that keeps unit economics defensible; GPU fleet management with topology-aware scheduling and granular observability; and AI-Ready data with the semantic layers and knowledge graphs that give your agents high-fidelity context. Together they replace fragmented orchestration, manual risk reviews and bespoke integration with a platform-enabled team and standardized pipelines.

We pair NVIDIA AI Enterprise (NVAIE) with the operating layer your AI program actually needs — standardized pipelines, automated governance, optimized inference economics and platforms that scale across teams. In practice that means we get the cluster live, keep it live, and continuously optimize the cost per token while your internal teams focus on the business problem.

As an NVIDIA partner, we help eligible customers access partner programs and route to suitable NVIDIA funding opportunities. The right path depends on workload, industry and stage of maturity — we shape it during the readiness workshop.

Most integrators stop at infrastructure stand-up. We operate at the seams between site readiness, cluster bring-up, steady-state operations and inference economics — and we run those seams as a single integrated practice rather than handing them between teams. The result is fewer weeks of idle GPU capacity and a cost per inference that keeps falling as workloads grow.

Start with a fixed-scope, outcome-based workshop. You leave with a costed activation roadmap that names the workloads, the timeline and the SLA — not a generic strategy deck. From there you can engage any of our GPU Activation Services individually or layer them into a single managed engagement.

The AI Factory operating model is the full program — turning NVIDIA capacity into business outcomes. GPU Activation Services are the five engineering services that execute it: Cluster Setup, Activation, Managed Cluster Ops, Managed Inference and Performance Engineering. Most customers start with the workshop, then pick the services that match where they are. Explore GPU Activation Services

Fraud is where the AI Factory model proves itself, because it demands everything production AI is usually missing: real-time decisions, regulatory-grade explainability, and enterprise data context — all at once. We apply the same four capabilities to it. AI-ready data foundations turn fragmented transaction history into the knowledge graphs and transaction foundation models that let a system reason about behaviour across fraud, credit risk and authorization from a single reusable representation, rather than a siloed model per use case. Performance engineering keeps fraud scoring inside the sub-second budget a live payment allows, with defensible cost-per-inference as volumes grow. Agentic AI systems orchestrate the downstream response, holds, step-up authentication, case creation, under governance rather than as single-shot prompts. And GPU fleet management, on NVIDIA accelerated infrastructure with RAPIDS and cuGraph, makes graph-based approaches like graph neural networks fast enough to run in production instead of the lab. The result is relational intelligence that catches the coordinated fraud, money-mule rings, synthetic identities, circular fund flows, that transaction-level models miss, with the explainability audit and regulators require. It's the clearest example of what we mean by moving AI from pilot to production, and the same foundation extends to credit decisioning, reconciliation and agentic payment orchestration.