World-class AI. On your terms.

It is a short sentence. World-class AI. On your terms. Both halves need unpacking, because each asks for something that is not self-evident.

A quiet conversation is taking place in European boardrooms right now. No longer about whether AI matters, because that question has been answered. The questions now are sharper. Which models do we let our people use? Where does our data go when someone pastes a contract into a chatbot? Who decides how our AI reasons? Underneath all of them sits one more: are we building our organisation's intelligence on infrastructure we control, or renting it from someone whose interests are not ours?

Below I unpack both halves: first what 'world-class AI' is today and where it is going, then what 'on your terms' requires.

World-class AI

It is tempting to equate world-class AI, sometimes called the frontier models, with 'the best model'. That is a mistake. There is no single best model, and there will not be one. The leaders shift every quarter: ChatGPT is ahead on one type of task, Claude on another, Gemini on a third. The best model for a memo is not the best model for a spreadsheet, and not for a regulated question that needs a citation.

Locking your organisation to one provider is therefore not a strategy. It is a hostage situation that has not yet happened. A change in pricing, a shift in model behaviour, a new rate limit, and you have no alternative. What 'world-class' actually means for an organisation is access to the best, whichever that is at any given moment.

How big that gap is becomes visible only side by side. Ask the same question to three different top models and you get three different answers, with different sources, in different styles.

That is not the whole picture. A model is not an agent, and a chatbot is not a workflow. The next layer is more concrete: agents that do work, each scoped to one task and anchored in the sources that belong to it, and the same for everyone who uses them. That is what world-class AI within an organisation actually means.

Once agents from different organisations start talking to each other, it matters that a locally aligned agent is not tied to one model provider. An assistant running on Claude today should be able to switch to a new, better model tomorrow without its sources, instructions, or users having to be reconfigured. That is where Localign differs from the rest, and it will weigh heavier in the years ahead than it does now.

On your terms

The second half of the title is the harder one. 'On your terms' sounds self-evident until you ask what it requires. Three layers: trust, instructions, control.

Trust

What feels trusted today can feel foreign tomorrow. One acquisition is enough, or a legal order, a geopolitical shift, a change in the terms of service. What sits in trusted hands now falls under foreign jurisdiction then.

Localign is built on European infrastructure, with contractual guarantees on data location and jurisdiction. That goes further than 'we host in Frankfurt' while the parent companies answer to another legal system. It is fixed in the contract, so you do not have to work it out afterwards.

How deep that goes only becomes visible when you look at the chain. Every AI provider relies on a ladder of other companies, called subprocessors, and that chain is where your data actually lives. Switch between OpenAI, Mistral, and Localign below to see where each one sends it.

Instructions

Your AI works for you, but who is giving it instructions? Every AI model is trained on norms and values set by someone else. A model that has read the English-language internet knows a great deal about Delaware corporate law and very little about Dutch sick-leave obligations, and answers both questions with the same confidence.

Localign lets you align instructions with your own context and your own rules. You decide what the model answers, which sources it draws on, which boundaries it stays inside, and in what style it writes. You are the client, and the instructions follow your organisation rather than the provider's business model.

An example. Ask a competing AI assistant what the AI Act requires of your organisation, and you will get a confident answer. It will cite the European Commission. It will give you a date. That date will be wrong, because the AI Omnibus package agreed in May 2026 delayed the core high-risk obligations by nearly two years. A system trained on last year's text does not know what changed last month.

Ask a Localign Grounded Agent pointed at the right pages of digital-strategy.ec.europa.eu, and you get the answer those pages carry today: December 2027 and August 2028, with the citation attached, the same for every user. Toggle between the Localign answer and a competitor's below.

Within each assistant, grounding is enforced for everyone who uses it, with the same sources and the same scope in every conversation. If a team needs a different set of sources, they copy the assistant in minutes. What you do not get is an answer that depends on whether your colleague remembered to paste the right instruction today.

That difference does not live in the prompt. It lives in where the boundary is drawn. Prompt engineering, which most AI tools allow, tells the model what to do. But the moment the model calls a tool, a web search, a document lookup, a database query, it decides the parameters itself. Ask a competitor to consult only rijksoverheid.nl and the prompt is a suggestion: the model picks its own queries, fetches what it can, and quietly skips what fails. You can see it in the employment-contracts example above, where Claude, even with the three URLs in the prompt, still failed to fetch half of them.

Localign constrains the capability rather than the prompt. The web search a Grounded Agent uses can only reach the URLs you have locked in, because that boundary sits on the tool itself and not in the instruction. That is no longer prompt engineering but tool engineering. In an agentic future, where agents act more autonomously, that distinction is the difference between an agent you can stand behind and one you have to hope works today.

Because a Localign assistant is not tied to one model provider, you can run it on a better model tomorrow without reconfiguring its grounding, its instructions, or its users. Once agents from different organisations start talking to each other, that is a precondition for taking part.

Control

You own it, but who holds the keys? With most AI tools, the vendor controls the rules: which model responds, what instructions it follows, where your data goes. What you buy is a product; a say in those rules is not part of it.

Localign gives you those keys back. You decide who has access, which model is used, and what happens to your data, and you can show how that is set up. If the rules change, you change the settings.

Secure model

Always on

Always self-hosted within the EU. Choose which external models it may consult. Sensitive data is filtered before being forwarded.

OpenAI

ChatGPT

Anthropic

Claude

Mistral AI

Mistral

DeepSeek

DeepSeek

Show more models

The invitation

The question is not whether your organisation will use world-class AI. It already is, whether you have decided so or not. The question is whether you will use it on your terms, or on someone else's.

All three layers come down to the same thing: your organisation is the author and the AI is the tool, not the other way around. That is what Localign makes possible, and what the name itself promises. Localign means 'locally aligned': to your country's laws, your sector's regulations, your company's policies, and your team's way of working. That alignment sits in the design and is not a setting you toggle.