Every task you send to a frontier model passes your prompts, context and domain knowledge to a vendor. Your contract may say you own the inputs and outputs, but it gives you no say in what the lab does next: how it prices and retires models, or which markets it enters. Tune a closed model on your data and the weights belong to them. The know-how that sets your business apart ends up inside a system you cannot move, inspect or take with you.
Thoughtworks AI Factory helps organizations own their intelligence.
None of this shows up on a single invoice. It shows up over years, in decisions you didn't realize you were making.
Prompts, retrieved documents and outputs run on the lab’s infrastructure. Retention and use are set by terms the lab can change, so you depend on a contract rather than a boundary you control.
Integrating third party models into sensitive workloads exposes your most valuable asset: your intelligence. The improvement your data produces cannot move with you if you switch.
Pricing, model retirements and terms change on the vendor’s schedule. Model providers also ship their own products, including in markets where their customers compete.
Your most valuable asset is usually your most sensitive data. AI could be transformative, but regulation, risk and policy prevent you from unlocking that value through hosted solutions.
Organizations became dependent on external AI vendors before the economic and risk ramifications became apparent. Most organizations don't lack the will to own their AI, they lack a clear picture of where they actually stand today and what to do about it.
Thoughtworks AI Factory closes this gap. Providing organizations with a clear definition of what sovereignty actually requires, a real assessment of where you stand today, and the expertise and services to achieve AI sovereignty.
Source: The state of sovereign AI adoption in 2026, IDC InfoBrief
| 50%+ | of executive leaders call sovereign AI a strategic priority, but few can agree on what the term actually means. |
| 13% | describe their organization as "very widely" aware of what sovereign AI actually requires. |
We review your current position on AI sovereignty: which models, data and vendors your AI depends on, and where you hold control today. You leave with a clear picture of where you stand.
We work with your leadership team to decide which controls matter most for your business and define the target state. The roadmap sets out the steps to reach it, with owners, costs and timing.
We prove the approach on one real workload and measure the result against your own baseline. You see what changes in cost, quality and control before you commit more widely.
Models, vendors and regulations keep changing, so sovereignty needs maintaining. We leave behind a managed service that helps you keep control. It can take the form of a subscription, a platform and tooling, dedicated people, or a combination.
Choosing and changing models requires evidence of how they behave under real conditions. Thoughtworks AI Labs investigates model evaluation, interpretability, robustness and control. Its researchers publish findings and, where available, code and datasets so others can examine the methods and build on the results.
Our work includes more than 1,000 experiments on how changes in a query affect retrieval-augmented generation, including publications across NeurIPS, ICLR, AAAI, ACL and EMNLP, and in IEEE publications.
Most sovereignty coverage focuses on nations building domestic AI capability. AI sovereignty within the enterprise is not just about where your data sits, but whether you control the models making decisions on your behalf, the intelligence they produce, and where that intelligence goes next. That is a business continuity and negotiating leverage question, not only a policy one.
Data sovereignty is about where your data physically sits and under which jurisdiction. AI sovereignty is broader: it covers who controls your models, your infrastructure, your operations and the decisions that shape all three. Data residency is one input into that picture, not the whole picture.
Not for every workload. You may retain control of your data, application logic and model selection while using an external model. More controlled infrastructure may make sense for particular sensitive or critical workloads, with the people, capacity and processes required to run it.
The right approach is workload by workload, tested against your own quality bar, not a blanket switch. Open-weight models increasingly clear the quality bar for routine, high-volume tasks. They are not automatically a drop-in replacement for work that genuinely needs frontier reasoning in every case.
There is a real cost, in engineering time, infrastructure, and in some cases short-term speed. It is worth naming honestly rather than promising sovereignty for free. The trade is worth making when a workload carries real IP sensitivity, regulatory exposure or a long time horizon. It is not worth making for every workload.
It depends on the workload, not on one company-wide answer. Our AI Factory model treats this as a spectrum: renting frontier models, renting alternative intelligence, renting hardware, or owning hardware outright. Most enterprises sit at more than one stage at once, deliberately.
Thoughtworks AI Factory brings together strategy, architecture and engineering across model and infrastructure choices. Thoughtworks helps organizations assess where they are, match models to workloads and build toward the level of control their AI systems require.
