People should be able to learn, work, create, and use technology in their own language. Every language carries knowledge, relationships, and ways of understanding the world that deserve a place in digital life.
Algebras is an AI lab for localization, researching how technology can preserve meaning and help language sound natural across cultures. We turn that research into tools for the people who create, translate, and release multilingual products.
We began with the ambition to automate the entire localization process. Real projects taught us how much depends on information that never reaches the model: a character’s relationship, a product’s purpose, a writer’s intention, or a decision that nobody has yet made. That experience shaped our commitment to automation guided by linguistic expertise.
We believe translators should have the tools and authority to shape how AI works. They should be able to inspect context, resolve ambiguities, define terminology, compare models, and approve the result. We oppose a future in which the translation profession disappears, and we work to make powerful tools more accessible to its practitioners.
Quality should be something people can examine, question, and improve. FluencyScore helps evaluate naturalness across several dimensions, making weaknesses easier to investigate. Our public translation benchmark gives people access to model comparisons, while paid evaluations let teams assess their own content. Naturalness, accuracy, and technical correctness each deserve attention before a translation is approved.
Our research must lead to better decisions in real projects. We commit to explaining our methods, publishing findings and limitations, and revising our conclusions when evidence changes.
A model’s confidence should never conceal missing information, and an evaluation score should never become unquestionable authority.
Privacy and intellectual property must be respected throughout the localization process. Unreleased scripts, product code, recordings, and translation memories deserve clear safeguards. Clients, creators, and linguists should understand who can access their materials, which model providers process them, and how long they are retained.
Ownership and usage rights should be explicit in our agreements. Reusing confidential project materials for training, research, benchmarks, or public examples should require separate, explicit permission from the relevant rights holders. Our commitment to open research must respect the confidentiality and rights attached to the work entrusted to us.
Our work brings these commitments into everyday work on games and consumer products. It helps agents collect context, use approved terminology, adapt tools to the project, and run translation and quality checks. Linguists guide the decisions, while automation reduces the repetitive work surrounding them.
Our work also reaches languages with limited digital resources and support. Through research, language data, and contributions to digital infrastructure, we aim to expand what their speakers can do with technology. We are building open.algebras.ai as a home for this work and for people who want to contribute.
The future we want gives more people access to useful technology in their own languages. It gives translators greater control over their tools and gives teams clearer evidence about the quality they release.
That is the future we are building at Algebras.
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