ZiyaOS

Look at what happened in the space of a few months in 2026. Project Glasswing showed frontier models finding vulnerabilities faster than anyone can patch them. An OpenAI model disproved an 80-year-old mathematical conjecture. GPT-5.6 Sol arrived with three price tiers and 54 per cent better token efficiency. Ox Alpha topped the usage charts anonymously and turned out to be an MIT-licensed open-weight model. Claude Fable 5.1 got cheaper while getting better.

Every one of those is good news, and every one of them breaks an application built the wrong way. If your assistant has a specific provider's API woven through its business logic, each of those releases is a migration. If the model is one exchangeable layer, each of them is a line in a configuration file and a re-run of your evaluation set.

This is the argument for separating the durable from the volatile. Your connection to the ERP system will still be needed in five years. Your role model will still be needed. Your audit trail will still be needed - more, not less, as regulation tightens. The model you use today will not be the model you use next year. Building those two things as one thing is the most common and most expensive mistake we see in enterprise AI.

It is also why we do not sell a model. We sell the layer underneath it, and we stay honest about which model belongs on top for which task.

The second reason this layer matters is that the deployment question cannot be answered once for a whole company. A marketing draft and a personnel file do not belong in the same place, and an engineering firm with export-controlled designs has constraints a retailer does not.

ZiyaOS therefore offers the same application in three shapes. Cloud, operated in ISO 27001-certified EU data centres, GDPR-compliant with a data processing agreement and no infrastructure of your own. On-premise, in your own infrastructure behind your own firewall, run by your team or together with us. Air-gapped, entirely without an internet connection, for environments with the highest security requirements - and there, with open models in your own data centre, which is exactly why the open-weight releases matter commercially and not just technically.

Your data is never used to train models. That is not a setting, it is a property of how the platform is built.

What makes this workable is that the choice is per use case rather than per company. The contract analysis that must never leave the building runs locally. The customer service assistant runs in the cloud. Both use the same connectors, the same roles and the same audit trail, because both run on the same operating system.

A first project on ZiyaOS is not faster because we work faster. It is faster because the parts that usually consume the schedule already exist. The integration to your systems is configuration rather than development. The permission model is set up rather than designed. Logging and operations are there rather than deferred to phase two and then forgotten.

What remains is the part that is actually specific to you: which work should be taken over, what a good result looks like, who checks it, and how it reaches the people who need it. That is the conversation worth having, and it is the one we would rather spend the time on.

The product page for ZiyaOS goes through the technical detail, and Ziya ONE is the product most customers meet first. If you are earlier than that, an AI use case workshop is usually the right starting point: find the work worth automating before deciding what to build it on.