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.
Enterprises are committing millions to their AI initiatives, yet seven in ten investments stall within six months. The bottleneck is no longer compute, it's the operating model around it.
Thoughtworks and NVIDIA help you cross the lab-to-production gap. We bring the industrial engineering that turns GPU capacity into measurable business outcomes: faster production readiness, predictable unit economics and governance built in from day one.
Together, we operationalize accelerated compute for enterprises that can't afford another stalled pilot. Start with a fixed-scope readiness workshop and leave with a costed roadmap, not a maybe.
| 2025 | Gartner Magic Quadrant™ Visionary winner, for three consequtive years in the custom software development category |
| 80% | of our staff are engineers building cloud-native, design-led systems |
| 4 | Papers accepted at NeurIPS 2025 — one of the world's most prestigious AI/ML conferences |
Operationalize your AI Factory
From idle GPUs to industrial-grade intelligence — engineered for production scale.
Most AI Factory investments stall because they were built as proof-of-concepts, not factories. Fragmented orchestration leaves 20 to 40 percent of GPU capacity idle. Multi-month security reviews freeze initiatives in place. Models hallucinate without secure, high-speed access to enterprise data. Workloads that perform in the lab collapse at real-world scale.
Thoughtworks closes the gap. We pair NVIDIA’s accelerated infrastructure with the operating layer AI programs actually need to turn capital expenditure into operating leverage: standardized pipelines, automated governance, optimized inference economics, and platforms that scale across teams without scaling headcount.
Our AI Factory operating model is built 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 agents high-fidelity context.
What the AI Factory model is made of
- Agentic AI systems: End-to-end workflows executed by AI agents, not single-shot prompts. Built to be governed at scale
- Performance engineering: Keeps unit economics defensible as workloads grow — cost-per-token falling, not climbing
- GPU fleet management: Topology-aware scheduling and granular observability across the cluster, not a black box
- AI-ready data: Semantic layers and knowledge graphs that give your agents high-fidelity enterprise context
Benefits of our NVIDIA partnership
Faster time-to-production
Move from lab to live in weeks, not quarters. Replace bespoke, manual integration with standardized pipelines that have security and data compliance built in from day one.
30–50% faster production readiness
Predictable unit economics
Shift from unbounded cloud spend to a clear cost-per-inference. Workload orchestration optimized to align your token budget with your business goals.
20–40% improvement in GPU utilization
Enterprise-grade governance
Replace months-long manual risk reviews with policy-as-code that becomes a release accelerator, not a release blocker. Scale dozens of AI agents across teams without a linear increase in headcount.
Reduced operational and compliance risk
From GPU capacity to running workloads
A rack on the floor isn't an AI Factory. Workloads running in production are.
Our five GPU Activation Services take you from one to the other — Cluster Setup and Activation get the cluster live in weeks, not quarters. Managed Cluster Ops keeps it live with SLA-backed uptime. Managed Inference and Performance Engineering keep cost-per-token falling as your workloads grow.
Pick the services you need today and layer in more as your program matures.
Thinking together, building together
Our work with NVIDIA goes beyond delivery, it's a shared commitment to shaping what AI-powered enterprises can become. From agentic AI frameworks and fraud prevention to media reinvention, explore the research, reports, and conversations we're building together.
-
BlogLarge language model evaluation: A key to GenAI success -
BlogEvaluating LLMs using semantic entropy -
ReportAgentic AI: The business realities of a breakthrough technology -
ArticleModernizing fraud prevention in financial services with NVIDIA and AWS, 2025 -
White paperThe agentic enterprise in 2026 : Building an ecosystem of continuous evolution and reliable impact
Ready to start your AI Factory journey?
Operationalizing accelerated compute doesn't have to be a leap of faith. As an NVIDIA partner, we co-invest alongside you, open the door to NVIDIA partner programs and funding routes, and start with a fixed-price, outcome-based workshop — so you know the cost and the outcome up front.
NVIDIA AI Factory readiness workshop and leave with a costed activation roadmap, not a maybe.
Frequently asked questions
Navigating the world of NVIDIA can be complex, but we’re here to help. Here are some common questions asked about our partnership.
-
-
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