What we are building for.

AI is expanding the range of things that can be understood, designed, and built. We want that expansion to reach the people who see a possibility before anyone else does, and the organizations with the knowledge to make it useful. The ability to shape intelligence should grow alongside the intelligence itself.

SADAIR builds foundation models for a Personal Intelligence Layer, bringing local language and voice intelligence together with the reach of frontier models. We are developing this foundation for people to use directly and for developers to build into software, devices, and machines. Our ambition is for the work people begin with AI to grow into capabilities they can keep developing, long after any particular model has been replaced.

Build for human capability

Learning a subject, starting a company, or making something new changes the person doing it. Along the way, people develop judgment about what matters, recognize possibilities they could not see before, and acquire the confidence to attempt more. We want AI to support that growth.

This includes giving machines substantial work to do. People should be able to delegate familiar tasks and gain room for more ambitious ones. They should also be able to question a result, understand an important decision, and develop a method of their own. The balance will differ with the person and the work. Our task is to make those choices practical.

We care about what people accomplish with our systems and how their own capabilities grow through use. A useful result can become the beginning of a better tool, a new practice, or a deeper understanding.

Let intelligence develop in many places

A stronger general model gives people a better starting point. What they develop from it should reflect the variety of the world. Different professions, languages, and communities have different ways of recognizing a problem and deciding what a good answer looks like. Some possibilities only become apparent when people put an idea to work in a particular place.

We want people and organizations to have the means to bring their knowledge into AI and shape how it works. Developers should be able to create products whose character comes from their own ideas and expertise. Our foundation must leave room for those products to take forms we did not design and serve purposes we did not anticipate.

Making intelligence widely available is one step. Giving people the ability to develop it for themselves changes who can take part in its future. We build infrastructure because its value can grow through independent work, with useful discoveries able to travel between communities and inform the next generation of models and tools.

Make intelligence a resource people can build with

Where computation happens changes what can be built. A system running on a device can stay available through an activity without sending every exchange to a remote service. Work completed locally has zero inference cost. This changes the economics of continuous assistance and gives developers room to explore products that would be difficult to sustain if every interaction required cloud inference.

That is why we invest in our own language and voice models, trained and optimized for the devices where they will run. We develop them alongside the systems that connect them to frontier models when more capability is needed. Everyday interaction can remain on the device while more demanding work draws on deeper reasoning and specialized capabilities beyond it.

We design these parts together so developers can build around the experience they want to create. An assistant in a pair of glasses and an AI system used across an organization will ask different things of the same foundation. Supporting that variety is part of the research.

Keep the work open to human direction

People rarely know everything they want at the beginning of a task. They notice something, hear a suggestion, or reconsider a decision. An AI system needs to remain receptive while the work is underway. Voice, timing, and interruption matter because they let people participate without continually stepping away from what they are doing.

A correction should change the system’s understanding and guide what happens next. People must be able to stop an action, revise a goal, and decide what the system may use. These controls belong in the architecture, where they can govern behavior across models and tools.

The freedom to customize also carries responsibility. Safeguards must protect people affected by a system, including those who did not choose to use it. We pursue broad freedom to create within clear safety boundaries, and evaluate those boundaries as capabilities and uses change.

Let research and use improve each other

A new capability can suggest a product that was not possible before. Building that product can expose a limitation that a benchmark never asked about. For us, model research, systems engineering, and product development belong in the same conversation.

We value people who can carry an idea from a scientific question through to something that works, and who are willing to change their approach when the evidence changes. We evaluate individual models and how well the complete system works for the people using it. Work with developers and organizations helps us understand which problems deserve deeper research. Learning from that work must respect the privacy of the people whose experience makes it possible.

Our models and methods will evolve. The purpose of the company will remain. We want more people to have the means to pursue an idea, build on what they learn, and leave something useful for others. The future we are working toward will be made in many places, by people with different answers to what is worth doing. SADAIR exists to help them give those answers form.